1 men’s health and wellbeing in australia canada new zealand the united kingdom and united states by Innowell

Menâ&#x20AC;&#x2122;s health and wellbeing in Australia, Canada, New Zealand, the United Kingdom and United States FINDINGS FROM AN INTERNATIONAL ONLINE PILOT SURVEY

Jane M. Burns, Tracey A. Davenport, Alyssa C. Milton and Ian B. Hickie With: Vicky S. Baldwin and Louise A. Ellis

Internal Report Submitted October 2016

Men’s health and wellbeing in Australia, Canada, New Zealand, the United Kingdom and United States: findings from an international online pilot survey Jane M. Burns, Tracey A. Davenport, Alyssa C. Milton and Ian B. Hickie With: Vicky S. Baldwin and Louise A. Ellis.

Additional copies of this report can be obtained from the Movember Foundation by emailing your request to references@movember.com

A PDF of this report can be downloaded from https://au.movember.com/programs/publications

Suggested reference Burns JM, Davenport TA, Milton AC, Hickie IB et al. Men’s health and wellbeing in Australia, Canada, New Zealand, the United Kingdom and United States: findings from an international online pilot survey. The Movember Foundation, Melbourne, 2016.

© Movember Foundation 2016 This work is copyright. Apart from any use as permitted under the Copyright Act 1968, no part may be reproduced by any process without prior written permission from the Movember Foundation. Requests and enquiries concerning reproduction and rights should be addressed to references@movember.com ISBN: 978-0-6480721-0-2

Acknowledgements The Global Health & Wellbeing 2015 Survey was commissioned by the Movember Foundation and conducted by the Young and Well Cooperative Research Centre (Young and Well CRC) and The University of Sydneyâ&#x20AC;&#x2122;s Brain and Mind Centre. The authors would like to acknowledge the following individuals and organisations for their contribution: Respondents: The respondents who consented to participate online in the Global Health & Wellbeing 2015 Survey. International Consortia and Champions of the Global Health & Wellbeing 2015 Survey: Internationally, a large number of individuals and organisations assisted with the dissemination of the Global Health & Wellbeing 2015 Survey. A detailed list of acknowledgements is found in Appendix 3. Movember Foundation (Australia) team: Therese Fitzpatrick, Anna Flego, Dr Clare Shann and Juliette Smith. Young and Well CRC team: Melissa Weinberg, Rebecca Philpot, Zoe Stephenson and Dr Michelle Blanchard. Brain and Mind Centre team: Sarah Piper, Lisa Whittle, Django White, Dr Laura Ospina Pinillos, Frank Iorfino, Stephen Kunkler, Dr Haley LaMonica, Atsushi Kobayashi, Alastair Christian, James Flynn, Ellena Danielle, Joanne Hanna, Cece Wolfner, Chiara Pomare and Emily Van Der Pol-Harney.

Research Partners

Movember Foundation (Australia) During November each year, Movember is responsible for the sprouting of millions of moustaches around the world. With their ‘mo’s’ men raise vital funds and awareness for prostate and testicular cancers, and mental health. As an independent global charity, Movember’s vision is to have an everlasting impact on the face of men’s health.

Young and Well Cooperative Research Centre The Young and Well CRC was an Australian-based, international research centre that unites young people with researchers, practitioners, innovators and policymakers from more than 70 partner organisations. Together, they explored the role of technology in young people’s lives, and how it can be used to improve the mental health and wellbeing of young people aged 12 to 25 years. The Young and Well CRC was established under the Australian Government’s Cooperative Research Centres Program and concluded its research in June 2016.

Brain and Mind Centre The University of Sydneyâ&#x20AC;&#x2122;s Brain and Mind Centre (formally Brain and Mind Research Institute) is dedicated to reducing the burden of disease due to brain and mind disorders through interdisciplinary, collaborative, basic, translational and clinical research and education. The Brain and Mind Centre works to develop new procedures, technologies and medicines, and to provide immediate access to the most advanced treatments for mental and neurological disorders, thereby contributing to the prevention or cure of these disorders.

Mental Health Foundation of New Zealand The Mental Health Foundation is a charitable trust that works towards creating a society free from discrimination, where all people enjoy positive mental health and wellbeing. The Mental Health Foundation delivers a range of programs and activities that support its vision of a society where all people flourish. Mental health promotion is also a focus with the provision of information and resources on topics such as depression awareness, youth mental health promotion, suicide prevention, social inclusion and the reduction of stigma and discrimination, consumer/ tangata whaiora issues, older peopleâ&#x20AC;&#x2122;s mental health and workplace mental health. The Mental Health Foundation seeks to inform, influence and advocate in all areas of mental health and wellbeing through research projects, policy and development work.

Foreword Too many men are dying too young. Death at any age, by any cause, is a tragedy. But when death comes sooner to men, in ways that are largely preventable, it’s a crisis. Men around the world are not faring well when it comes to their health. There is a widening gap between male and female life expectancy and men are more likely to develop noncommunicable diseases, such as cardiovascular disease, diabetes and cancer and at a younger age. Globally 3 times more men die by suicide than women. The result is that too many men are living in poor health and dying prematurely and that impacts all of us – our families, our workplaces, our communities. We know that gender is one of the strongest and most consistent predictors of health and life expectancy. Yet men’s health globally does not get the urgent attention it deserves. The Movember Foundation believes it can lead the way in giving men healthier, happier and longer lives but to do this, we need to better understand how men perceive and deal with their health along their life journey. The Survey pilot is just the starting point of a broader knowledge agenda to ‘disrupt’ the way we think about men’s health and masculinity and better empower the Foundation to identify areas in men’s health where greater attention is deserved. With new knowledge comes the opportunity to act through programmatic activity, utilising local and global partnerships, working ultimately towards turning the trends and giving men back their missing years. Paul Villanti Executive Director of Programs, Movember Foundation

Contents Acknowledgements Research Partners Foreword Executive Summary

3 4 6 11

// The risks of poor mental health and suicide throughout a man’s life were not well understood

// Helpful and harmful responses to tough times

// The importance of social connection

// Life afttecting lifestyles

// Help and information seeking

// Groups at risk

// Painting a picture of a healthy man

Key Findings What We Already Know

16 20

// Background in brief

// Project overview

// Main aims of the Global Health & Wellbeing 2015 Survey

Methods

// Design

// Respondents

// Ethics

// Survey

// Survey fatigue

// Recruitment overview

// Data analysis

// Data presentation

Demographics

// Participation

// Respondents

Perceptions of Health and Wellbeing

// Chapter overview

// Overview of perceptions of health and wellbeing

// Perceptions of major health problems

// Perceptions of major mental health problems

// Age of onset

// Perceptions of health and wellbeing chapter summary

Masculinity, Emotionality and Social Connectedness // Chapter overview

// Overview of masculinity, emotionality and social connectedness

// Masculinity, emotionality and social connectedness

// Masculinity, emotionality and social connectedness chapter summary

Mental Health, Wellbeing, Happiness and Resilience

// Chapter overview

// Overall health

// Overall health summary

// Mental health

// Mental health summary

// Wellbeing, happiness and resilience

// Wellbeing, happiness and resilience summary

// Predictors of health, mental health and wellbeing

// Predictors of health, mental health and wellbeing summary

Major Life Events

// Chapter overview

// Major life events

// Major life events summary

Health Behaviours and Perceptions

108

111

// Chapter overview

111

// Physical activity

111

// Physical activity summary

114

// Sleep behaviours

114

// Sleep behaviours summary

120

// Eating behaviours

121

// Eating behaviours summary

127

// Weight, weight perceptions and dieting behaviours

127

// Weight, weight perceptions and dieting behaviours summary

131

// Body image concern

131

// Body image concern summary

136

// Weight lifting and steriod use

137

// Weight lifting and steroid use summary

138

// Alcohol and/or other substance mis/use

139

// Alcohol and/or other substance mis/use summary

145

// Gambling

147

// Gambling summary

149

Perceptions and Experience of Stigma and Discrimination

150

// Chapter overview

150

// Perceptions and experiences of stigma and discrimination

150

// Perceptions of public stigma and discrimination

151

// Self-stigma

158

// Stigma and discrimination chapter summary

161

Information and Help-Seeking, Service Utilisation and Satisfaction

165

// Chapter overview

165

// Information seeking

165

// Experience of help-seeking

174

// Help-seeking summary

183

Country Specific findings: AUSTRALIA Country Specific Findings: CANADA Country Specific Findings: NEW ZEALAND Country Specific Findings: UNITED KINGDOM Country Specific Findings: UNITED STATES

189 194 199 204 209

// Country specific chapter summary

Limitations References Tables and Figures

214

217 219 235

// List of tables

235

// List of figures

239

The Authors Appendix 1: Recruitment

240 242

// Facebook

242

// Twitter

247

// Instagram

248

// YouTube

250

// Newspaper advertising

250

// Additional online social media and advertising

251

Appendix 2: Additional data tables 252 Appendix 3: International consortia and champions of The Global Health & Wellbeing 2015 Survey 256 Appendix 4: The Global Health and Wellbeing 2015 Survey 259 // MODULE A.

259

// MODULE B.

262

// MODULE C.

265

// MODULE D.

270

// MODULE E.

275

// MODULE F.

287

// MODULE G.

292

// MODULE H.

308

// MODULE I.

316

// MODULE J.

317

Executive Summary This Executive Summary was adapted by the Movember Foundation from the initial Report delivered to Movember in July 2016

On average, men die five years younger than women, live with worse health and carry the greater burden of chronic disease. Around the world, we lose a man to suicide every minute of every day. This is a health crisis that demands urgent action in order to give men back their missing years and protect their health and wellbeing throughout their life. The Global Health & Wellbeing Survey is a pilot study, commissioned by The Movember Foundation, to help the health charity better understand the men they’re serving and take action to protect men’s health. The survey explored the health beliefs, perceptions and health behaviours of 10,765 people (40% males, 60% females) aged 16 years and older in the five countries where The Movember Foundation is active— Australia, Canada, New Zealand, the United Kingdom (UK), and United States (US). The Global Health & Wellbeing Survey explores the complexities of how people’s behaviours, attitudes, life courses and social contexts can influence their health. The survey reveals that the risks to men’s mental health is not well understood by men or women. The survey results point to the impact of the vulnerable times in a man’s life, revealing men’s helpful and harmful responses to tough times, and how different life events that are perceived by many as stressful are associated with suicidal thoughts and behaviours for men of different ages. It also emphasises the importance of social connection for good health and wellbeing. The Global Health & Wellbeing Survey report provides insights into the health and wellbeing of men (and women) and helps to paint a picture of what it is to be a healthy man in the 21st century. The survey was conducted by the Young and Well Cooperative Research Centre (Young and Well CRC) and The University of Sydney’s Brain and Mind Centre online between 1 July and 11 December 2015. The study design was based on learnings from the Young and Well CRC’s First and Second National Surveys that focused on young people’s mental health and wellbeing as well as current technology use.

The Global Health & Wellbeing Survey utilised an online approach using social media promotion and snowball sampling as the main form of recruitment. Although targeted recruitment across gender and age was undertaken, this survey is a nonepidemiological convenience sample. This research does not claim to report population prevalence rates. For example, the use of online surveying and recruitment through social media can result in sampling bias and non-response bias. Avidity bias may also be present whereby individuals with a greater interest in, or experience with, a survey topic may be more likely to respond. The principal aims of the Global Health & Wellbeing Survey were to: •

assess perceptions of the health and wellbeing (including mental health) of men in Australia, Canada, New Zealand, the UK and US;

•

gain insights into those personal and social factors associated with men’s health and wellbeing, with a specific focus on mental health;

•

identify areas where there is a significant difference between the perceptions and experiences reported by men compared with women; and,

•

better understand men’s health behaviours in modern times, including those related to physical activity, alcohol and/or other substance use, eating behaviours, body image, health information seeking, and help-seeking.

The study included modules exploring: •

people’s perceptions of the major health problems and the major mental health problems facing younger men (aged 16 to 39 years old), and older men (aged 40 years and older);

•

people’s perceptions of their current general health, mental health, happiness, wellbeing, and resilience;

•

people’s emotional empathy, their social connectedness, and their conformity to masculine norms, and how these factors might influence health and wellbeing;

•

people’s responses and coping strategies during major life events;

•

lifestyle habits and their influence on health and wellbeing;

•

perceptions and experiences of stigma and discrimination due to health, mental health and alcohol and/or other substance use problems; and,

•

people’s help and information seeking confidence, perceptions, and behaviours.

// THE RISKS OF POOR MENTAL HEALTH AND SUICIDE THROUGHOUT A MAN’S LIFE WERE NOT WELL UNDERSTOOD The survey highlighted that many people don’t realise that mental health problems and the risk of non-accidental injury, such as suicide, continue throughout men’s lives. Data from the Australian Bureau of Statistics shows suicide is the top cause of death in Australian men aged 25 to 44 years, and the third highest killer of men aged 45 to 54 years. Overall, male and female survey respondents perceived mental health as an issue for younger men, and physical health as an issue for older men. When men aged 40 years and over were asked to identify the major health problems facing their age category, they mentioned heart disease or cancer at a far greater rate than mental health related problems. Importantly, less than 10 percent identified nonaccidental injury, such as suicide and self-harm, which is in stark contrast with suicide statistics. The survey did reveal an awareness of alcohol related issues, with many respondents identifying alcohol misuse and addiction as a major mental health problem for younger and older men. // HELPFUL AND HARMFUL RESPONSES TO TOUGH TIMES The seven major life events asked about included suddenly or unexpectedly becoming unemployed, becoming a parent for the first time, experiencing a relationship breakdown, retiring, starting a new job, finishing school or starting university or college. Alarmingly, almost half (46 percent) of the men surveyed who had experienced at least one of these major life events in the last 12 months which they found stressful reported having had suicidal thoughts. For comparison, 22 percent of men who did not report experiencing a major life event in the last 12 months reported suicidal ideation, as did 17 percent that did experience a major life event but did not find it stressful. Compared to women, more men facing major life events that they found to be stressful reported responding with behaviours and coping strategies that are ultimately harmful for health and wellbeing. These harmful behaviours included increasing alcohol, tobacco and/or drug consumption (35 percent); becoming aggressive (21 percent); and taking more risks (27 percent). Like women, many men also reported isolating themselves socially (58 percent). More than one third (35 percent) of men who had

experienced a stressful life event did not report that they would talk about their feelings and 28 percent would do nothing. // THE IMPORTANCE OF SOCIAL CONNECTION Men who identified as more socially connected trended towards responses which indicated they had good health and wellbeing. Worryingly, men who identified more closely with social isolation reported higher psychological distress, higher self-stigma, and lower personal wellbeing (measured through responses to questions about personal health, achievement, relationships, safety, community connectedness, standard of living, and future security). They also reported lower confidence relating to seeking help for physical health, mental health, and alcohol and/or other substance use problems. // LIFE AFTTECTING LIFESTYLES The survey results also illustrated connections between healthy living habits and good physical and mental health, whilst also identifying that there is room for improvement. More than half of the men surveyed (55 percent) reported that they engaged in less physical activity than recommended for a healthy lifestyle. Men reporting healthy levels of physical activity was associated with them also reporting higher personal wellbeing and greater happiness, although the relationship between these variables was not strong. Based on reported height and weight, 68 percent of men reported a body mass index outside the range considered healthy; with one percent underweight, 37 percent overweight and 30 percent obese. With regard to eating habits, 65 percent of men surveyed reported they do not eat enough fruit and vegetables daily, and 27 percent eat fast food a few times a week or more. Women were more likely to consider themselves non-drinkers or occasional alcohol drinkers, while men were more likely to consider themselves social drinkers, heavy drinkers, or binge drinkers. Of the men who had consumed alcohol, tobacco and/or other drugs at some point during their lifetime, 38 percent had thought recently that they should cut down. Male respondents reported a higher frequency of late night Internet use than females. For men, late night Internet use was associated, albeit weakly, with poorer sleep quality

and mental health and suicidal ideation, as well as lower happiness, resilience, and personal wellbeing. Men also reported higher levels of gambling than women. // HELP AND INFORMATION SEEKING Overall, about six out of 10 men expressed confidence that they would be able to find information if they experienced a challenging health problem. More men (71 percent) were confident about finding information regarding a physical health problem, compared with 61 percent for a mental health problem, and 70 percent for alcohol and/or other substance use problem. // GROUPS AT RISK For men, there were some groups that experienced significantly higher psychological distress and lower personal wellbeing. These included men who identified as gay, bisexual, transgender, queer, intersex, or asexual; those who were more socially isolated; and those who had experienced a stressful major life event. Men who were younger were more likely to have worse mental health, and men who had poorer overall health were more likely to report worse mental health and personal wellbeing. All these groups also reported significantly less confidence in help-seeking overall. Additionally, those who were not in employment, education, or training were significantly more likely to have worse mental health and self-rated overall health and reported less confidence in help-seeking for physical health conditions. // PAINTING A PICTURE OF A HEALTHY MAN The Global Health & Wellbeing Survey paints a picture of a healthy man as someone who is not just physically fit, but someone who can respond to tough times in ways that are healthy and helpful in the long run, someone who knows to seek help and how to seek that help when things arenâ&#x20AC;&#x2122;t right, and someone who has strong, supportive social connections. Understanding and addressing lifestyle behaviours is also important, given the role of exercise and diet in preventing poor physical and mental health, and the associations between alcohol and/or other substance use and gambling, and mental health problems. The Global Health & Wellbeing Survey provides insights that can help organisations design more effective and better targeted menâ&#x20AC;&#x2122;s health and wellbeing programs and inform future population research projects.

Key Findings Overall, there is a plethora of inter-related themes that recur throughout the chapters of this report. It is these inter-related themes that form the basis of our key findings and recommendations. It is essential to note beforehand that findings are from a nonepidemiological sample. Important associations are highlighted, however, the findings do not necessarily reflect community prevalence rates â&#x20AC;&#x201C; particularly due to the possibility of noted limitations such as Internet and avidity biases. Perception of menâ&#x20AC;&#x2122;s health and wellbeing KEY FINDING 1: Respondents reported the top three major health problems for younger men (aged 16 to 39 years) were: 1) brain, behavioural and mental health disorders (80%); 2) accidental injury (73%); and, 3) non-accidental injury (57%). For older men (aged 40 years and over), these were: 1) heart disease (69%); 2) cancer (64%); and 3) diabetes (49%). Perceptions regarding the type of health problems faced by men of different age groups suggested that mental health problems were more likely to be seen as a younger manâ&#x20AC;&#x2122;s issue, whereas physical health problems were viewed as an issue for older men. KEY FINDING 2: In contrast to international prevalence statistics reporting older men are the highest risk group to die by suicide, respondents reported a very low awareness of this health problem with just 9% endorsing non-accidental injury for older men. KEY FINDING 3: Respondents reported the top three major mental health problems for younger men (aged 16 to 39 years) were: 1) alcohol misuse or addiction (77%); 2) drug misuse or addiction (68%); and, 3) depressive illness (61%). For older men (aged 40 years and over), these were: 1) alcohol misuse or addiction (75%); 2) depressive illness (67%); and, 3) dementia (52%). KEY FINDING 4: While eating disorders as a major mental health problem for men were only endorsed by approximately 9% of respondents, analysis by gender surprisingly found that men rate eating disorders (as a major mental health problem for both younger and older men) at a significantly higher rate than women (younger men: 14% vs 9%, respectively; older men: 10% vs 4%, respectively).

Masculinity, emotionality and social connectedness KEY FINDING 5: Analysis of conformity to masculine norms found three distinct clusters of belief that respondents grouped into. These included people who identified as more socially connected and have more emotionality, those who identified as more self-reliant risk takers or ‘boyish’ (i.e. express views that were impulsive, driven, open to experience

and

self-reliant

but

demonstrated

less

emotionality

and

social

connectedness), and those who were more isolated (or marginalised) and have lower emotionality. KEY FINDING 6: While men who identified more closely with the socially connected group trended towards good health and wellbeing, those men who identified as selfreliant risk takers expressed somewhat higher psychological distress but reasonable confidence in help-seeking for these mental health problems. Worryingly, men who identified more closely with the isolated group, reported higher psychological distress and lower personal wellbeing, higher self-stigma and lower confidence help-seeking for physical health, mental health and alcohol and/or other substance use problems. Mental health, wellbeing, happiness and resilience KEY FINDING 7: Young people (aged 16 to 24 years) reported the highest levels of psychological distress (55% vs 27%), suicidal ideation (46% vs 25%) and self-harm (29% vs 5%). However, once psychological distress was statistically controlled for, young people were more likely to report good overall health as well as high personal wellbeing. KEY FINDING 8: When solely considering the male sample, young men (aged 16 to 24 years) reported the highest levels of psychological distress (48% vs 27%), suicidal ideation (43% vs 25%) and self-harm (21% vs 3%). KEY FINDING 9: Alcohol and/or other substance misuse was identified as one of the biggest major health problems for men – with 75% of respondents endorsing this as a major mental health problem for both younger men (aged 16 to 39 years) and 76% for older men (aged 40 years and over). Men reported experiencing higher levels of alcohol and/or other substance misuse than women (41% vs 31%); and, probable substance misuse increased the likelihood of reporting high psychological distress. Alcohol and/or other substance misuse was a commonly reported coping strategies when experiencing a stressful life event – with 35% of men reporting increased use of alcohol, tobacco or other substance to cope. Men were also more likely to engage in

the use of other substances whilst consuming alcohol as compared to women (56% vs 50%). Major life events KEY FINDING 10: Men who reported a stressful life event in the past 12 months were significantly more likely to have poor mental health and lower personal wellbeing (as compared to men who had not experienced any ‘stressful life events’). Of those men who found a life event(s) stressful, 65% reported talking to people about their feelings. Interestingly, new fathers less frequently endorsed talking to someone about such feelings than men who experienced other stressful life events, like starting university/ college (57% vs 71%). KEY FINDING 11: Relationship breakdown was rated consistently as the most stressful life event (91%), followed by suddenly or unexpectedly becoming unemployed (86%). Alarmingly, almost half of all men (48%) who experienced either of these two events in the last 12 months reported suicidal ideation. Health behaviours and perceptions KEY FINDING 12: Approximately 55% of men were not sufficiently physically active, with 67% reportedly being overweight or obese. Men reported eating more fast food, meat, fish and less fruit and vegetables than women. Low physical activity was weakly associated with a higher body mass index (BMI), higher psychological distress and lower personal wellbeing, happiness, resilience and social connectedness. The strongest positive associations for sufficient physical activity were with higher personal wellbeing and greater happiness. KEY FINDING 13: While 65% of men reported poor quality sleep, good quality was significantly associated with lower psychological distress, higher personal wellbeing and greater happiness. KEY FINDING 14: Men reported higher levels of gambling than women; with 28% of men reporting they had gambled in the past 12 months as opposed to 19% of women. Of those who had gambled, men also reported the highest frequency – with 31% gambling ‘once a week’. There were noticeable age effects with only 13% of young people (aged 16 to 24 years) reported gambling in the past 12 months; while 41% of older people (age 65 years and over) reported gambling at least ‘once a week’ over the same time period.

Perceptions and Experience of Stigma and Discrimination KEY FINDING 15: Alcohol and/or other substance misuse was associated with the highest stigma and discrimination beliefs. Men who had experienced alcohol and/or other substance misuse or a mental health problem reported consistently higher selfstigma levels than those who had experienced a physical health problem. Information and Help-seeking, Service Utilisation and Satisfaction KEY FINDING 16: For men, searching the Internet was the most commonly reported method of accessing information about a physical health problem (89%), mental health problem (80%) or alcohol and/or other substance misuse (78%). KEY FINDING 17: Men and women report similar levels of help-seeking confidence for physical health problems, mental health problems or alcohol and/or other substance misuse. Compared to women: men were more likely to wait longer before seeking help for a mental health problem; but were more likely to wait less time before seeking help for a physical health problem. Both men and women report waiting longer periods of time to seek help for alcohol and/or other substance use problems. KEY FINDING 18: Men who reported higher psychological distress, lower personal wellbeing, lower happiness and higher self-stigma had significantly lower help-seeking confidence for physical health problems, mental health problems or alcohol and/or other substance use problems. This may be a combination of both a lack of personal confidence but also lower confidence in the (mental) health system. KEY FINDING 19: For men, there were some groups that experienced significantly higher psychological distress and lower personal wellbeing. These included men who identified as LGBTQIA, those who were not in education or training, those who identified with the isolated group and those who had experienced a stressful life event. These groups also reported significantly less confidence in help-seeking overall. Country Specific Findings KEY FINDING 20: Each country (Australia, Canada, New Zealand, the UK and US) needs to consider results from their respondents within the context of that countryâ&#x20AC;&#x2122;s current population and health services environment. Importantly, this allows each country to generate their own unique (and novel) approaches to addressing menâ&#x20AC;&#x2122;s health, mental health and welling at the local (rather than global) level.

What We Already Know // BACKGROUND IN BRIEF Throughout most of the world, the health outcomes of boys and men lag significantly behind those of girls and women; however, this inequity remains largely unaddressed at a national, regional, or global level as well as among health policy makers and health-care providers (Baker et al 2014). The five leading global risks for mortality include high blood pressure, tobacco use, high blood glucose, physical inactivity and obesity (WHO 2009). These risk factors contribute to the high levels of chronic diseases such as heart disease and cancer. International statistics demonstrate that men have a significantly higher risk of developing these chronic diseases than women (AIHW 2011; European Commission 2010; Public Health Agency of Canada 2009; New Zealand Ministry of Health 2015). In addition to the physical health risks, there is evidence demonstrating the increase in mental health problems in men. Suicide and alcohol and/or other substance misuse is most prevalent in men, with those aged over 40 years being at highest risk of suicide (ABS 2016; CDC 2015; Statistics Canada 2015; ONS 2015). Men are more likely to engage in risk taking behaviours that have a detrimental impact on health, which also contributes to the identified poor health outcomes (European commission 2011; Patton et al 2009; Thomas et al 2007). The above risk factors for both physical and mental health problems in men are substantial, however research suggests that men are reluctant to access services and seek help related to these problems (Yousef et al 2015; Steinfeldt et al 2009; Galdas et al 2005; Oliver et al 2005; Addis & Mahalik 2003; Courtenay 2000). This increases the long-term risks for men due to less early intervention and treatment for avoidable health problems. Gender differences in help-seeking behaviours may be attributed to multiple factors, however, there is increasing research on the influence of ‘masculinity’ on helpseeking. There is evidence to suggest that masculine ‘norms’ may prevent or delay men from accessing support (Yousaf et al 2015). A recent study by Vogel et al (2011) found that men who demonstrated a higher level of masculine beliefs had less favourable views regarding seeking psychological support. However, there is also acknowledgement that a more indepth consideration of what influences men and their approach to help-seeking is needed, particularly focusing on environmental, social and demographic factors (Gough 2006; Addis & Mahalik 2003).

Despite the above statistics related to health risks and health outcomes, men continue to be under-represented in service use, and although there is an increasing body of evidence related to men’s health, there is still limited knowledge to aid an understanding of why these inequalities exist (Wilkins 2010). Concerted global action is clearly needed to reduce the men’s health gap and promote preventative health behaviours more assertively. Improving men’s health has the potential to improve positively on outcomes not only for men, but also more widely for women, children and society as a whole (Baker et al 2014). This survey provides an important opportunity to establish further evidence regarding men’s health with a view to influencing the future direction of policy and services. // PROJECT OVERVIEW The Movember Foundation is the leading global organisation committed to changing the face of men’s health. The Foundation has recently commissioned the Young and Well Cooperative Research Centre (Young and Well CRC; Professor Jane Burns) in partnership with The University of Sydney’s Brain and Mind Centre (Professor Ian Hickie) to undertake a global survey on boy’s and men’s general health and wellbeing (including mental health). The ‘Global Health & Wellbeing 2015 Survey’, includes a multi-site international study with a Consortia consisting of research partners from five target countries including Australia, Canada, New Zealand, the UK and US. This Report presents the top-line findings from the Global Health & Wellbeing 2015 Survey. // MAIN AIMS OF THE GLOBAL HEALTH & WELLBEING 2015 SURVEY The main aims of the Global Health & Wellbeing 2015 Survey were: 1. To assess the health, mental health and wellbeing of men in Australia, Canada, New Zealand, the UK and US. 2. Gain insights into factors associated with men’s health, mental health and wellbeing. 3. Better understand men’s health behaviours (including physical activity, alcohol and/or other substance use, eating behaviours, body image, health information seeking and help-seeking) and factors associated with these behaviours. Outside of general demographics, through the development of the survey and the current evidence base, additional factors of particular interest were established. These included: social connectedness; individual beliefs around masculinity; and, the impact of significant life events. Social connectedness can be viewed as a key development need for individuals, with evidence suggesting that increased social connectedness is linked to life satisfaction (Diener & Seligman 2002). In addition to this, evidence

suggests that lack of attachments or connectedness can result in ill-effects on health, adjustments and overall wellbeing (Baumeister & Leary 1995). The discourse around masculinity and health has increased considerably over the past 10 years in response to a reported ‘crisis’ regarding the poor health outcomes for men (Ricciardelli 2012). There is evidence to suggest that masculine norms can influence men’s engagement with services and their views regarding the need to access support (Emslie, Ridge, Ziebland et al 2006; Evans et al 2011; Moller-Leimkuhler 2002). Significant life events also play a key role in individual health, and evidence has shown a relationship between life events and subjective wellbeing (Luhmann et al 2012). Additional focus was also on sub-samples of the population such as those who are not currently working, those who work in emergency services or in the military, and those who live in rural areas. Lastly, in this survey women’s health and wellbeing status, their health-related behaviours and their views on men’s health is also measured. This is not just to simply provide a comparison group, but to also understand men’s health and wellbeing from the perspective of others; as women themselves play a key complementary role in supporting men’s health and wellbeing.

Methods // DESIGN The study design was based on learnings from the Young and Well CRCâ&#x20AC;&#x2122;s First and Second National Surveys, which focused on the impact of technology on young peopleâ&#x20AC;&#x2122;s mental health and wellbeing (Burns et al 2010, 2013, in submission). In line with this previous research, the current study utilised an online survey methodology. Benefits to this approach include accessing large numbers of participants at low cost, and reduced time between data collection and analysis (Duffy et al 2005; Galesic, Tourangeau & Couper 2006; Szolnoki & Hoffmann 2013). Additionally, an online platform is advocated in study design when the survey is complex and requires randomisation and/or branching and/or question logic to guide respondents through its items (Daley et al 2003); all features that were used within this current study. A voluntary convenience sample was studied, with respondents being recruited from cross-sections of the International community chiefly via social media channels (see recruitment overview). An Australian-based online sample recruited through social media has been previously compared against the more traditional methodology of Computer-Assisted Telephone Interviewing (CATI; Burns et al in submission), where it was found to be an effective tool reaching large numbers of participants, and in particular accessing young people (Burns et al in submission). // RESPONDENTS The survey sample comprised males and females (aged 16 years and older) who responded they had lived in Australia, Canada, New Zealand, the UK or US for the best part of the past 12 months. The survey was hosted online from 01 July 2015 to 11 December 2015. // ETHICS This study received institutional ethics approval from The University of Sydney Human Research Ethics Committee (Protocol No. 2015/412). Respondents gave consent online and understood they could cease participation at any time, and that their responses were confidential and were not identifiable. // SURVEY Depending on participant answers, the survey took between 20 to 45 minutes to complete. As this was a pilot survey, it was only provided in written English. Though the

survey was designed by the investigators, it was conducted via LimeSurvey (an online application using a web interface for survey development, publication and collection) and hosted by The University of Sydney’s Brain and Mind Centre (Australia). The survey included nine modules (A to I). The survey utilised various design features including randomisation, branching and question logic. Design features of each module including eligibility screening (16 years or older, provide consent), randomisation, branching and question logic are presented in Figure 1. Each chapter describes the measures in detail and the full survey is presented in Appendix 4. In brief, the content of each module included: Module A: Demographics This section contained scoping demographics, including age, gender, country, postcode, rurality, highest level of education, employment status and industry of work (adaptation of the Standard Classification of Occupations across all participating countries – Australian Bureau of Statistics 2009; Office for National Statistics 2015; Statistics Canada 2015; Statistics New Zealand 2015; US Bureau of Labor Statistics 2015). Module B: Perceptions of health and wellbeing problems In alignment with previous research (Rong et al 2007), this module determined respondents’ knowledge of common health and mental health problems. Respondents were requested to nominate the main causes of death or disability in their country from a list of general health problem categories, a list of specific illness and injuries, and a list of mental health problems for both younger men (aged 16 to 39 years) and men aged over 40 years. Items were based on the leading causes of death and disability as specified by census data in each country. Respondents were randomised to respond to questions relating to either major physical health problems or major mental health problems. Three additional items also asked all respondents their beliefs about the typical age of onset for physical health, mental health, and alcohol and/or other substance use problems for men. Module C: Health, wellbeing, happiness and resilience This module included specific questions about experiencing and coping with significant life events. Happiness was measured with the Oxford Happiness Questionnaire – short scale (Hills & Argyle 2002). Resilience was measured with the Brief Resilient Coping Scale (BRCS; Sinclair & Wallston 2004). Additionally, this module measured the respondent’s overall health (Blanchard et al 2014), current mental health using the 10item Kessler Psychological Distress Scale (K10; Kessler et al 2003), suicidal ideation

items from the Psychiatric Frequency Symptom Scale – suicidal ideation and acts submeasure (PSFS; Lindelow et al 1997) and self-harm with a single-item measure. A further question to investigate burden was asked about ‘days out of role’, which was extracted from the Brief Disability Questionnaire (BDQ; von Korff et al 1996). Subjective wellbeing was measured with the Personal Wellbeing Index (PWI; International Wellbeing Group 2013). Any respondents indicating elevated distress were provided with the contact details of local support lines. Module D: Masculinity, emotionality and social connectedness Behavioural and affective adherence to stereotypical masculine gender roles was measured using an adapted version of the Conformity to Masculine Norms Index (CMNI-22; Mahalik et al 2003). The Toronto Empathy Questionnaire (TEQ; Spreng et al 2009) was used to measure emotional empathy. The 11-item Duke Social Support Index (DSSI; Koenig et al 1993) captured social support, including the ‘social interaction’ subscale and the ‘satisfaction with social support subscale’. Additionally, perceived support and conflict in close relationships was measured by Schuster’s Social Support Scale (SSSS; Schuster et al 1990). The 12-items on the ‘care dimension’ of the Intimate Bond Measure (IBM; Wilhelm & Parker 1988) were used as an indicator of perceived care from one’s partner. Module E: Help-seeking, service utilisation and satisfaction Items in this module were adapted from previous research (Burns et al 2013; Rong et al 2007) to suit three conditions (1. physical health; 2. mental health; and 3. alcohol and/or other substance misuse). For each condition, items assessed respondents’ helpseeking confidence, their views on how long problems needed to be present before seeking help, whether they had ever looked for health-related information, and if they had done so, the method in which they sought this information. This module also assessed whether respondents, or those close to them, had experienced a major physical health, mental health or alcohol and/or other substance use problem. Respondents who did experience one or more of these conditions were subsequently asked about the help sought from professionals and/or non-professionals for that problem, and rated how helpful they felt that treatment was. Module F: Perceptions and experience of stigma and discrimination This module commenced with an adapted stigma vignette (Corrigan et al 2009) that randomised respondents to one of three health conditions (1. physical health; 2. mental health; or 3. alcohol and/or other substance misuse) and assessed respondents’ levels of stigma and discrimination towards a person experiencing this condition. Additional items adapted from Rong et al (2007) further assessed respondents’ attitudes towards

people who had experienced these conditions, as well as their beliefs concerning the stigma and discrimination associated with these conditions. For all individuals who identified as having experienced a physical health, mental health or alcohol and/or other substance use problem, an additional series of questions adapted from the Self-Stigma of Depression Scale (SSDS; Barney et al 2010) to suit each health condition were included. These items assessed feelings of shame, selfblame, social inadequacy and help-seeking inhibition. Module G: Health behaviours and perceptions In this module, health behaviours and health perceptions were assessed. The International Physical Activity Questionnaire short form (IPAQ; Craig et al 2003) was used to determine a respondentâ&#x20AC;&#x2122;s physical activity level. This questionnaire asks about activity undertaken in three domains (walking, moderate-intensity and vigorous-intensity activities) as well as sitting. Questions concerning weightlifting frequency, duration and intensity were also included (Hale et al 2013), and were followed by two additional items determining lifetime anabolic steroid use (Blashill 2014). Frequency of healthy or unhealthy eating behaviours by food group was measured using items taken from the Young and Well National Surveys (Burns et al 2013). Current dieting status and reason for dieting were derived from Blashill (2014). Body Mass Index (BMI) was also included, as was waist circumference (but presented to males only). In order to assess body image attitudes, respondents were asked to selfevaluate the importance of weight and/or shape, whether this was distressing to them and to indicate with which areas of their body they were concerned (see Burns et al 2013). Three items relating to sleep were measured, which included average number of hours of sleep, sleep quality (adapted from Buysse et al 1989) and feeling refreshed upon waking (Bayer & Pathy 1985). An additional item assessing late night Internet use was also included. Lifetime alcohol, tobacco and other substance use items were included and were derived from the Young and Well National Surveys (Burns et al 2013). For respondents who identified using any substance(s), they were asked further questions concerning frequency of use, age of first use, dependency, desire to cut back, social or professional encouragement to stop, and reason for use. For tobacco products, frequency and amount of tobacco used within the past three months was also assessed. For alcohol, average number of standard drinks consumed in a drinking

session, and self-identified current drinking status was also determined. Additionally, there were four items measuring gambling: 1) incidence during the past 12 months; 2) gambling frequency; 3) desire to cut back; and, 4) social encouragement to stop. Any respondents indicating potentially problematic alcohol or substance use were provided with the contact details of local support lines. Module H: Additional demographics Additional demographics were assessed in this module. These included ethnicity, current living arrangements, sexual orientation, marital status and religious affiliation. An additional item determined whether the person was a current patient or consumer of health services, a carer, or a health professional. Respondent recruitment pathways were also assessed in this section; such as being recruited via social media or through word of mouth, as was awareness of menâ&#x20AC;&#x2122;s health organisations. Module I: Future contact The final module asked respondents whether they were interested in participating in future surveys and sharing the Global Health & Wellbeing 2015 Survey.

Figure 1 illustrates the participant journey through the Global Health and Wellbeing 2015 Survey.

Pre-survey landing page: All respondents complete questions about country and gender to determine appropriate survey questions

Module A: All respondents complete eligibility screen (16 years and older and willing to provide consent). Eligible respondents complete additional demographics

Respondent stops survey if not eligible (under 16 and/or did not provide consent)

Module B: All eligible respondents complete ‘perceptions of health and wellbeing problems’ section

Module C: All eligible respondents complete ‘health, wellbeing, happiness and resilience’ section Module D: All eligible respondents complete ‘masculinity, emotionality and social connectedness’ section

Opt out option 1: Respondents provided opportunity to go directly to final modules (H & I)

Module E: All continuing respondents randomised to one of three conditions for information seeking

Condition 1. Physical Health

If yes, respondents complete questions about their helpseeking experience

Respondents who experienced a physical, mental health or substance use problem also completed questions about self-stigma

Condition 2. Mental Health

Condition 3. Substance Use

All respondents asked whether they had ever experienced a physical, mental health or substance use problem

Module F: All continuing respondents randomised to one of three conditions for stigma and discrimination section

Condition 1. Physical Health

Condition 2. Mental Health

Condition 3. Substance Use

Module G: All continuing respondents complete ‘health behaviours and perceptions’ section

Module H & I (Last section of survey): All continuing respondents complete ‘additional demographics and awareness of men’s health organisations’ and ‘future contact’ section

Figure 1. Participant journey through the Global Health & Wellbeing 2015 Survey

Opt out option 2: Respondents provided opportunity to go directly to final modules (H & I)

Opt out option 3: Respondents provided opportunity to go directly to final modules (H & I)

// SURVEY FATIGUE Survey fatigue is generally defined as the time and effort involved in participating in a survey (Sharp & Frankel 1983). The majority of research in this area has focused on interview length and has generally found that longer surveys result in lower response rates (Porter et al 2004). As the Global Health and Wellbeing 2015 Survey was of substantial length, a number of strategies were employed to maintain respondentsâ&#x20AC;&#x2122; engagement and address the potential for survey fatigue. Firstly, items of most interest were asked at the beginning of the survey. Additionally, at the end of Modules D, E and F respondents were asked whether they would like to continue. At each of these points respondents had the option to skip to the end (Module H and I) or continue. // RECRUITMENT OVERVIEW An online advertising methodology was used to recruit respondents across Australia, Canada, New Zealand, the UK and US. Online methods of recruitment have the advantage of generating greater exposure than other available methodologies, with household Internet availability ranging from 73% in the US to 86% in Australia and the UK (ABS 2016; ONS 2015, File & Ryan 2014). Given the widespread availability of Internet use, online surveys may be accessible to otherwise hard-to-reach groups, like those living in regional and rural areas (Currie et al 2014) and those living overseas. Respondents were recruited to commence the survey (www.globalhwsurvey.com) from paid advertising and organic (free) social media. In brief, multiple social media channels were used as platforms for survey dissemination (Facebook, Twitter, YouTube etc; See Appendix 1). Increased visibility and message exposure across multiple

social

media

channels

augment

traditional

online

recruitment

advertisements (Close et al 2013), and layering of recruitment messages optimise recruitment success (Merolli et al 2014). The use of social media as a recruitment tool has been shown to be an efficient, cost-effective way to recruit large numbers of respondents (Oâ&#x20AC;&#x2122;Connor et al 2014; Thornton et al 2015). Social media channels are cited as useful snowball sampling tools (Brickman-Bhutta 2012; Close et al 2013). The spread of study information across online social networks (through sharing, liking and tweeting) increases reach and visibility, ultimately yielding greater participant numbers. This approach was supplemented by a traditional snowballing technique (Biernacki & Waldorf 1981), in which our Global Consortia, respondents and wider networks forwarded the study to others to increase the participant pool.

This recruitment method has some benefits and costs. For example, despite the capacity for targeted advertisements using social media channels, research has demonstrated that the demographic characteristics of online survey respondents do not completely reflect the broader population. Like traditional surveys (Smith, 2008), online samples recruited via social media risk being biased towards those with a higher mean family income, younger average age, higher educational attainment, and a greater proportion of female respondents (Casler et al 2013). Nevertheless, online surveys continue to be used as an economically viable, easy to manage and generally reliable form of recruitment. Additionally, online survey approaches have other benefits. For example, these approaches have been suggested to reduce bias in response to sensitive or stigmatising topics (Ramo & Prochaska 2012; Temple & Brown 2011). This has also been reported when compared to telephone and face-toface interviews, which has been attributed to increased anonymity and social distance (see Crutzen & GĂśritz 2010; Newman et al 2002). // DATA ANALYSIS All survey data were prepared and analysed using IBM SPSS Statistics for Windows, Version 22.0 (IBM Corp. 2013). Survey data were prepared before analysis. This included the coding of conditional skips and missing values, outlier range checks, reverse scoring of items and scoring of measures. No imputations were completed for missing data, except where specifically stated. Internal consistency for each measure was assessed to determine reliability (see Cronbach 1951), and is reported in the results. Given the exploratory nature, the breadth of the survey questions, and pilot phase of the project the data presented within this Report were selected on the basis of a preliminary analysis of noteworthy findings. Analyses were run for the total sample and across all key demographic variables and where sub-sample sizes permitted (nâ&#x2030;Ľ30). Each chapter of this Report describes any relevant additional analyses that were applied including correlations, latent class analysis, linear and logistic regression. // DATA PRESENTATION All data presented in this Report have been summarised and reduced for ease of reading and interpretation. Percentages have been rounded to one decimal place. Depending on the analysis, mean values have been rounded to one or two decimal places. Statistical significance is reported by bolding of relevant items. In the tables, column percentages and sample sizes are shown. Some sample sizes vary due to missing data.

Demographics // PARTICIPATION Figure 2 presents the flow of engagement from the number of people exposed to survey advertising (through main paid advertising channels) to the number of respondents who completed each survey module (A to H presented here). Based on the available data, only 3% of individuals who viewed the survey advertisement (global impressions) subsequently visited the survey landing page (clicks/ conversions), however this figure is an overestimate of the true conversion rate as it does not include impressions via Twitter, Instagram or word of mouth. Fifty-nine percent of respondents who answered the question on survey recruitment indicated they were recruited through Facebook and YouTube (n=4,159). Six percent (n=445) were recruited through other social media channels, and the remaining 38% were recruited through friends, colleagues, organisations, emails and word of mouth (n=2,707). Compared to YouTube, Facebook was a more effective paid advertising channel. In total, 51% of eligible respondents completed the full survey (all Modules A to H; n=5,512). An additional 15% (n=1,602) elected to skip specific modules when given an opportunity to opt out (provided at the end of Modules D, E and F) but returned to complete the final modules (Modules H and I) of the survey. Full attrition was therefore 34% (n=3,651) of the total eligible sample. Other research has reported that the survey completion rate for all â&#x20AC;&#x2DC;requiredâ&#x20AC;&#x2122; questions in an online sample recruited through social media was 57% (O'Connor et al 2014). Thus, in comparison, our methods used to address survey fatigue appear to have demonstrated some success.

* Global impressions is the total number of times activity related to the Global Health and Wellbeing 2015 Survey is seen by people on Facebook/ YouTube, whether it be via organic (free) or paid advertisement. â&#x20AC; Conversions are the average number of conversions per ad click.

Figure 2. Respondentâ&#x20AC;&#x2122;s engagement from global impressions to survey completion

Table 1 gives a breakdown of consent and eligibility rates by gender and by country. This accounts for respondents who either did not provide consent or did not meet age eligibility criteria (over 16 years old).

Table 1. Sample and eligibility by gender and by country Survey starts

Sample remaining after removing ‘No Consent Given’*

n 16,510

% 69.5

Sample remaining after removing respondents aged ‘less than 16 years’* % 65.2

Male Female

6,554 9,956

70.5 68.9

65.6 64.9

Australia Canada New Zealand United Kingdom United States

4,586 3,244 2,555 3,257 2,868

77.3 62.1 73.3 65.0 67.3

73.0 58.2 68.6 59.5 64.1

Total sample Gender

Country

* % represents the number as a percentage of survey starts.

The total eligible sample was 10,765 respondents (65.2% of the total number that engaged with the survey). An average of 4.3% of respondents globally who wished to participate in the survey were not eligible to take part as they were under 16 years of age. There was little difference in the proportion of males and females who consented to participate. Across countries, consent rates were higher in Australia and New Zealand – which may be due to respondents in these countries having greater familiarity with the organisations and university leading the survey. // RESPONDENTS Table 2 presents participation by age and gender across each country for eligible respondents only. A larger proportion of female participation in this survey is consistent with previous online survey research (eg. Casler et al 2013), telephone and mail surveys (Curtin et al 2000; Moore & Tarnai 2002) and incentivised surveys (Singer et al 2000). An over representation of young people is also common in online surveys (Casler et al 2013) and mail surveys (Moore & Tarnai 2002). However, in this survey, the spread of participating age groups was reasonable; with the largest proportion of respondents being in the 45 to 64 year age bracket in the five-country sample.

Table 2. Basic demographics (age and gender) for each country Fivecountry sample

Australia

Canada

New Zealand

United Kingdom

United States

10,765

3,349

1,888

1,752

1,938

1,838

Male

40.0

38.3

41.9

39.8

39.3

41.8

Female

60.0

61.7

58.1

60.2

60.7

58.2

10,753

3,345

1,886

1,751

1,934

1,837

16 to 24

25.0

23.6

12.0

34.5

26.4

30.3

25 to 44

22.7

30.2

16.3

21.6

24.6

14.8

45 to 64

33.7

30.3

45.8

30.8

33.2

30.8

65+

18.5

15.8

25.9

13.1

15.8

24.1

Gender

Age-bands (years)

Questions regarding ethnicity were based on national census questionnaires and classifications from each of the respective five countries are presented in Table 3 to Table 7. There was good representation in the survey of people who identified with an Indigenous background. In Australia, there were 1.6% of respondents who identified as Indigenous in the survey compared to 3% from population data (ABS 2011). In Canada, there were 3.6% of respondents in the survey compared to 4% from population data (Statistics Canada 2011). In New Zealand, there were 11.9% of respondents in the survey compared to 15% from population data (Statistics New Zealand 2012). In the US, there were 3.9% of respondents in the survey compared to 2% from population data (United States Census Bureau 2012).

Table 3. Ethnic background of Australian respondents

2,245 % 98.4 1.5 <0.1 0.1

Are you of Aboriginal or Torres Strait Islander origin? No Yes, Aboriginal Yes, Torres Strait Islander Yes, both Aboriginal and Torres Strait Islander Is English the only language you speak? English only Other language(s) spoken

% 82.8 17.2

Arabic Cantonese Croatian German Greek French Italian Japanese Mandarin Macedonian Serbian Spanish Tagalog/Filipino Turkish Vietnamese Other Not specified

2.1 3.9 0.5 8.8 2.3 17.6 7.5 4.9 3.1 0.8 0.5 8.5 <0.1 0.8 2.3 5.8 39.4

Other language(s) spoken*

* Percentages are based on respondents who speak a language other than English.

Table 4. Ethnic background of Canadian respondents n Are you an Aboriginal person? No Yes, First Nations (North American Indian) Yes, Métis Yes, Inuk (Inuit) Not selected n To which ethnic group(s) do you identify with most?* White Chinese South Asian Black Filipino Latin American Southeast Asian Arab West Asian Japanese Korean Other

1,291 % 89.2 0.8 2.7 0.1 7.3 1,206 % 88.6 0.8 1.7 0.8 0.5 0.8 0.3 0.4 0.3 0.3 0.2 5.1

* Percentages have been calculated based on number of participants who responded ‘yes’ to at least one ethnicity item in the list.

Table 5. Ethnic background of New Zealand respondents

n To which ethnic group(s) do you identify with most?* New Zealand European Māori Samoan Cook Islands Māori Tongan Niuean Tokelauan Fijian Other Pacific Peoples British Other European Southeast Asian Chinese Indian Other Asian Middle Eastern Latin American African Other

1,225 % 75.9 11.9 1.7 0.5 0.8 0.4 0.1 0.4 0.3 8.7 5.3 0.8 1.5 1.3 0.8 0.2 0.8 1.1 6.8

*Percentages do not add to 100% as more than one response could be selected.

Table 6. Ethnic background of United Kingdom respondents n To which ethnic group(s) do you identify with most? White* British† Irish† Other† Mixed* White and Black Caribbean‡ White and Black African‡ White and Asian‡ Other‡ Asian or Asian British* § Indian § Pakistani § Bangladeshi § Chinese § Other Black or Black British* Caribbeanǁ Africanǁ Otherǁ Other ethnic group* Arab∆ Other∆

1,282 % (within UK sample)

1,282 % (within ethic group)

91.9 86.1 4.4 9.6 2.2 18.2 18.2 39.4 24.2 3.2 41.7 16.7 8.3 14.6 18.8 1.3 38.9 50.0 11.1 1.4 21.4 78.6

* Percentage of all UK respondents that completed ethnicity question (n=1,282). † Percentage of all UK respondents who identified as ‘white’. Note, respondents could select more than one option. ‡ Percentage of all UK respondents who identified as ‘mixed’. Note, respondents could select more than one option. § Percentage of all UK respondents who identified as ‘Asian or Asian British’. Note, respondents could select more than one option. ǁ Percentage of all UK respondents who identified as ‘Black or Black British’. Note, respondents could select more than one option. ∆ Percentage of all UK respondents who identified as ‘Other ethnic group’. Note: respondents could select more than one option.

Table 7. Ethnic background of respondents from the United States

n To which ethnic group(s) do you identify with most?* African-American Caribbean Caucasian Chinese Filipino Indian (South Asian) Japanese Korean Latino or Hispanic Native American or Aleut Native Hawaiian Middle Eastern Pacific Islanders Vietnamese Other

1,351 % 7.7 4.3 69.4 1.5 1.5 1.9 0.8 0.2 11.0 3.9 0.2 1.4 0.7 1.0 5.9

*Percentages do not add to 100% as more than one response could be selected.

There are complexities involved in making robust international comparisons due to the potential for methodological and cultural bias between countries (WHO 2015). This is due to the substantial differences present; for example, between cultural groups, between administrative sectors and within the health care system as a whole (Australian Institute of Health and Welfare 2012). An example of this can be found in the significant differences in the way the healthcare system is funded and delivered within the UK compared to Australia or the US. A ‘free at the point of access’ service may have considerable influence on how an individual responds to a question related to help-seeking behaviour. In addition to this, the political climate within each country will also have a significant influence on the health agendas and interventions, subsequently influencing public perceptions towards health. Therefore, all tables comparing sub-samples onward will present data in each column by ‘Fivecountry sample’, ‘Australia’ and ‘All other countries’. The country specific data will be presented in each country’s dedicated chapter. Table 8 presents additional demographics for all eligible respondents, including location, education level, employment status, marital status, living arrangements, sexual orientation, ethnicity and religious affiliation. The sample was predominantly city dwelling, tertiary educated and in paid employment. Census data for each country suggests that 67% to 81% of people live in urban areas (ABS 2008; ONS 2013; Statistics Canada 2011; Statistic New Zealand 2006; United States Census Bureau 2010), hence people living in non-urban areas were over represented in this survey. This is probably due to the benefits of online survey recruitment relating to respondents living in traditionally harder to reach areas. Labour participation rates

across countries range between 63% and 78% (Trading Economics 2016). As the current survey only asked respondents to record their ‘main activity’, these data cannot accurately be compared to census figures as “…according to established international standards, everyone who works for at least one hour or more for pay or profit is considered to be employed. This includes everyone from teenagers who work part-time after school, to a partially retired grandparent helping out at the school canteen…” (ABS 2016). The survey, however, does have strong representation from those who are not employed, retired and are students. Finally, research has shown that people who are more educated are more likely to participate in online, telephone and mail surveys (Curtin et al 2000; Goyder et al 2002; Singer et al 2000); which is in line with the demographic spread of respondents in this survey.

Table 8. Sample characteristics Five-country sample

Australia

Male Female

40.0 60.0

38.3 61.7

40.7 59.3

16 to 24 25 to 44 45 to 64 65+

25.0 22.7 33.7 18.5

23.6 30.2 30.3 15.8

25.6 19.4 35.2 19.8

Rural/remote † Regional/non-urban town Urban

6.7 29.9 63.4

9.3 17.9 72.8

5.5 35.4 59.1

Education Secondary school or less Certificate or diploma Tertiary degree

23.3 17.7 59.0

24.9 15.7 59.4

22.6 18.5 58.8

41.1 40.6 14.3 4.0

40.0 44.5 12.6 2.9

41.6 38.8 15.2 4.5

35.2 12.8 7.6 18.4 19.7 6.3

37.4 15.8 6.5 14.0 19.8 6.5

34.1 11.4 8.1 20.4 19.7 6.2

Lives alone Lives with partner (no children) Lives with children (no partner) Lives with partner and children Lives with parents Lives with others

18.7 31.5 3.6 15.9 18.3 17.9

17.8 30.4 4.0 18.9 19.8 15.2

19.2 32.1 3.4 14.5 17.7 19.2

Religious or spiritual affiliation Yes § No

48.6 51.4

55.3 44.7

45.4 54.6

83.2 16.8

83.3 16.7

83.2 16.8

49.3 50.7

48.3 51.6

49.7 50.3

Variable Gender

All other countries* %

Age (years)

Location

Marital status Never married Married/de facto Divorced or separated Widowed Employment status Full-time Part-time Not in paid employment Retired Student Other ‡

Living arrangements

Sexual orientation Heterosexual LGBTQIA Experienced a significant life event in the past 12 months Yes No

* All other countries include: Canada, New Zealand, the UK and US. † Question varied for each country. Australia: Do you live in a regional centre?; Canada: Do you live in a small town, village or suburban area?; New Zealand: Do you live in a rural area with an urban influence?; United Kingdom: Do you live in a small town or village?; United States: Do you live in a small town or suburban area? ‡ Does not add to 100% as multiple response options were available. § ‘no’ response included ‘agnostic’, ‘atheist’ and ‘no religious affiliation’ in line with Finlay & Walther (2003).

Perceptions of Health and Wellbeing // CHAPTER OVERVIEW This chapter reports on Module B of the survey, which examined perceptions of health and wellbeing in three main areas including:   

Physical health; Mental health; and, Alcohol and/or other substance misuse.

// OVERVIEW OF PERCEPTIONS OF HEALTH AND WELLBEING Perceptions of major health-related problems were assessed using adapted questions from previous research (Rong et al 2007). Respondents were asked to consider the major health problems and the major mental health problems faced by men across two different age groups. These were captured by four items: 1) major health problems for men 16 to 39 years of age; 2) major health problems for men aged 40 years and over; 3) major mental health problems for men 16 to 39 years of age; and, 4) major mental health problems for men aged 40 years and over. For each item, respondents were provided with 13 identifiable health or mental health problems and also given the opportunity to add their own qualitative responses, which were coded before analysis. // PERCEPTIONS OF MAJOR HEALTH PROBLEMS Figure 3 presents comparisons of all respondents in the five-country sample beliefs about the major health problems for younger men aged 16 to 39 years and for older men aged over 40 years.

Figure 3. Percentage of endorsed major health problems for younger men aged 16 to 39 years and older men aged over 40 years (older men) by all respondents in the five-country sample

Younger men and health The results showed three clear areas of perceived major health-related problems for younger men as reported by all respondents. These areas in order of response included: 1) brain, behavioural and mental health disorders; 2) accidental injury; and, 3) non-accidental injury. Although less frequently endorsed, diabetes and infectious diseases were also

consistently considered a major health problem by

approximately one quarter of all respondents. Analysis of the additional qualitative data reported by the respondents identified three other areas as diet, exercise and obesity. Of these, obesity was the most consistently reported area across age, gender and by sample. Table 9 presents additional comparisons across gender, age-band and sample for younger men. The above three areas were also consistently reported across ageband, gender and sample. Consideration of the results by gender showed three notable differences. There was approximately a 10% difference in rates of endorsement for brain, behavioural and mental health disorders and non-accidental injury items, with males endorsing these as major problems less frequently than females. For respondents aged 65 years and over, a lower percentage (41.5%) endorsed non-accidental injury compared to younger age groups. This is notably lower than for those aged 25 to 44 years, with 66.7% reporting this area as a major health problem. A lower percentage of respondents aged 65 years and older also rated accidental injuries and brain, behavioural and mental health disorders as major health problems; whereas 16 to 24 year olds viewed both cancer and infectious diseases as an issue for younger men more frequently than other age-bands.

Table 9. Perceptions of major health problems faced by men aged 16 to 39 years (comparative sub-samples) Gender

Age-bands (years)

Males

Females

16 to 24

25 to 44

3,892

5,761

2,312

Brain, behavioural and mental health disorders

% Yes

74.9

84.0

Accidental injuries

% Yes

69.6

Non-accidental injury

% Yes

Diabetes

Sample 65+

Australia

All other countries

2,217

45 to 64 3,325

1,799

3,037

6,616

80.6

88.0

81.0

69.5

87.8

76.9

75.7

67.5

75.3

76.6

71.7

77.8

71.1

51.4

61.1

58.7

66.7

58.3

41.5

66.3

53.0

% Yes

30.3

23.5

29.5

22.7

24.1

30.2

21.2

28.5

Infectious diseases

% Yes

27.6

23.2

31.9

22.6

23.0

22.7

19.5

27.5

Cancer

% Yes

19.1

17.0

24.9

18.9

13.3

15.9

15.4

19.0

Heart disease

% Yes

11.6

8.0

10.7

8.7

8.3

10.9

6.9

10.6

Lung and chest diseases

% Yes

8.2

7.2

10.9

6.8

5.7

7.9

5.2

8.7

Stomach, bowel and liver disease

% Yes

7.9

7.5

8.2

7.9

6.8

8.3

5.6

8.6

Vision or hearing impairment

% Yes

6.6

3.8

5.6

3.1

4.3

7.4

3.0

5.8

Muscle or joint diseases

% Yes

4.5

4.6

5.6

3.5

3.9

5.8

3.5

5.1

Lung and chest infections

% Yes

3.4

3.6

5.4

2.8

2.7

3.3

2.5

3.9

Stroke

% Yes

2.8

3.8

2.6

2.0

3.1

2.6

2.9

Other

% Yes

2.4

1.3

1.1

1.3

2.1

2.3

1.5

1.8

Note: Bolded font indicates the five highest endorsed major health problems faced by men aged 16 to 39 years.

Figure 4 presents the perceptions of major health problems for men aged 16 to 39 years by men of the same age. Like the other sub-samples, the three major health problems identified were brain, behavioural and mental health disorders followed by accidental injuries and then non-accidental injuries. Three in 10 men identified infectious diseases as a major health problem for men of their own age. Diabetes and cancer were viewed as an issue by a quarter of 16 to 39-year-old men.

Note: Percentages less than 10% are not presented in the figure.

Figure 4. Percentage of endorsed major health problems for men aged 16 to 39 years by men of the same age (n=1,292) Older men and health As shown above in Figure 3, major health problems for men in the older age group were notably different to that of younger men. The five major health problems for older men endorsed by the five-country sample were: 1) heart disease; 2) cancer; 3) diabetes; 4) brain, behavioural and mental health disorders; and, 5) stroke. Table 10 below presents comparisons of perceptions of major health problems faced by men over 40 years old across sub-samples (gender, age-band and sample). There was response consistency across gender and sample, but not across ageband. Fewer respondents in the youngest (16 to 24 years old) and oldest (65 years and over) age bands identified brain, behavioural and mental health problems as a major issue (35.4% and 35.8%), compared to respondents in the middle age-bands (25 to 44 years 51.4%; and 45 to 64 years 48.3%).

Table 10. Perceptions of major health problems faced by men aged 40 years and over (comparative sub-samples) Gender

Age-bands (years)

Sample

Males

Females

16 to 24

25 to 44

45 to 64

65+

Australia

3,890

5,760

2,312

2,215

3,326

1,797

3,035

All other countries 6,615

Heart disease

% Yes

69.2

68.9

63.9

72.2

70.5

68.9

68.0

69.5

Cancer

% Yes

65.4

62.3

65.9

68.4

61.5

58.5

63.9

63.4

Diabetes

% Yes

49.3

49.0

42.3

49.6

52.3

51.8

48.1

49.6

% Yes

43.5

43.7

35.4

51.4

48.3

35.8

54.6

38.6

% Yes

33.9

32.3

39.4

34.0

29.6

29.0

30.7

33.9

Muscle or joint diseases

% Yes

24.2

22.7

27.2

17.5

22.0

27.7

18.9

25.3

Vision or hearing impairment

% Yes

19.1

17.2

24.8

11.6

14.6

23.4

14.3

19.7

Stomach, bowel and liver disease

% Yes

18.2

20.1

21.1

21.3

18.1

16.9

20.9

18.6

Lung and chest diseases

% Yes

13.7

13.0

16.1

12.1

12.0

13.5

11.5

14.1

Accidental injuries

% Yes

11.8

13.5

12.8

12.2

12.8

13.7

12.0

13.2

Non-accidental injury

% Yes

7.9

9.7

6.1

11.8

10.9

5.3

13.1

7.1

Lung and chest infections

% Yes

5.9

5.4

9.0

4.6

4.2

5.1

5.0

5.9

Infectious diseases

% Yes

3.1

2.5

3.3

2.8

2.5

2.3

1.6

3.2

Other

% Yes

1.3

<1.0

1.2

<1.0

1.0

Brain, behavioural and mental health disorders Stroke

Note: Bolded font indicates the five highest endorsed major health problems faced by men aged 40 years and over.

As shown in Figure 5, men who were aged 40 years and over also identified the major health concerns for men of their same age group as heart disease, cancer, brain, behavioural and mental health-related concerns, and stroke. Muscle and joint diseases such as arthritis were considered an issue by a quarter of men in this age group. The endorsement of these major health problems for men aged 40 years and over followed the same order as respondentsâ&#x20AC;&#x2122; views in the five-country sample. Nonaccidental injuries for this group were below 10% endorsement (8.5%).

Note: Percentages less than 10% are not presented in the figure.

Figure 5. Percentage of endorsed major health problems for men aged 40 years and over by men of the same age (n=2,599)

// PERCEPTIONS OF MAJOR MENTAL HEALTH PROBLEMS Figure 6 presents perceptions of the major mental health problems for younger men aged 16 to 39 years and for men aged 40 years and over as reported by all respondents in the five-country sample.

Figure 6. Percentage of endorsed major mental health problems for younger men aged 16 to 39 years and older men aged 40 years and over by all respondents in the five-country sample

Young men and mental health As shown above in Figure 6, respondentsâ&#x20AC;&#x2122; views on major health problems for men in the younger age group were: 1) alcohol misuse; 2) drug misuse; 3) depressive illnesses; 4) anxiety, neurosis or panic disorder; and, 5) behavioural or emotional disorders. These were consistently reported as the top five issues across gender, age-band and sample (see Table 9). Figure 7 shows the major mental health problems of men aged 16 to 39 years as rated by men of their same age group. The most frequently endorsed problems were alcohol, depression, drug and anxiety-related issues. A notable difference was that compared to other groups (gender, age-bands, and sample), men aged 16 to 39 years (shown in Figure 7) and all respondents 25 to 44 years (shown in Table 11) endorsed depression more frequently than drug misuse or addiction as an issue. These younger men also endorsed eating disorders as an issue for men in their age group at 16.4%; which was high when compared to 11.0% of the five-country sample and 8.9% of the female sample.

Note: Percentages less than 10% are not presented in the figure.

Figure 7. Percentage of endorsed major mental health problems for men aged 16 to 39 years by men of the same age in a five-country sample (n=1,290)

Table 11. Perceptions of major mental health problems faced by men aged 16 to 39 years (comparative sub-samples) Gender

Age-bands (years)

Sample

Males

Females

16 to 24

25 to 44

45 to 64

65+

Australia

All other countries

3,884

5,750

2,311

2,213

3,316

1,794

3,031

6,603

Alcohol abuse or addiction

% Yes

74.3

78.7

74.5

76.9

79.4

75.7

79.6

75.7

Drug misuse or addiction

% Yes

66.8

69.1

66.8

64.5

69.9

71.3

70.3

67.2

Depressive illness

% Yes

58.4

62.5

62.1

72.9

60.6

45.0

68.7

57.3

Anxiety, neurosis or panic disorder

% Yes

40.1

40.2

47.3

49.9

35.4

27.7

43.9

38.5

Behavioural or emotional disorders

% Yes

31.3

34.2

36.8

31.9

32.5

30.5

26.5

36.0

Bipolar disorder

% Yes

17.0

15.7

14.9

16.5

16.6

16.7

13.9

17.3

Gambling addiction

% Yes

15.0

13.9

15.1

15.8

13.1

13.7

17.8

12.7

Personality disorders

% Yes

15.1

10.1

9.1

12.8

18.5

9.4

13.3

Eating disorders

% Yes

14.2

8.9

17.2

8.5

8.2

11.4

9.4

11.8

Schizophrenia or other psychoses

% Yes

8.4

10.7

6.2

11.6

10.9

10.0

10.7

9.3

Autism spectrum disorders

% Yes

6.3

8.7

8.4

6.8

8.0

7.4

6.0

8.5

Intellectual disability

% Yes

2.7

2.3

4.8

2.0

1.3

2.1

1.4

3.0

Dementia

% Yes

<1.0

1.2

<1.0

Other

% Yes

1.0

<1.0

1.1

<1.0

Note: Bold font indicates the five highest endorsed major mental health problems faced by men aged 16 to 39 years.

Older men and mental health As shown above in Figure 6 the five-country sample respondents identified the major mental health issues for men aged 40 years and over as: 1) alcohol misuse or addiction; 2) depressive illness; 3) dementia; 4) anxiety neurosis or panic disorder and, 5) drug misuse or addiction. As shown below in Table 12, these major mental health problems for men aged 40 years and over were also reported consistently across gender, age and sample. However, in the Australian sample and in the 16 to 24-year-old age-band, gambling addiction was endorsed more frequently than drug misuse or addiction. The order of endorsement of mental health problems faced by men aged 40 years and over is similar to the identified major mental health problems faced by younger men (see Figure 7 and Table 11 above); apart from dementia, which emerged as the third most reported major mental health problem for older men. When compared with views on younger menâ&#x20AC;&#x2122;s major mental health problems, there were also some notable differences in relation to the frequency of endorsement of particular items. For example, although drug misuse or addiction was reported by the five-country sample as a top major mental health problem for younger and older men, endorsement dropped from 68.2% to 26.2% with age.

Table 12. Perceptions of major mental health problems faced by men aged 40 years and over (comparative sub-samples) Gender

Age-bands (years) 25 to 44 45 to 64

Females

16 to 24

3,885

5,754

2,312

2,212

3,318

1,797

3,033

6,606

Alcohol abuse or addiction

% Yes

73.0

76.6

68.3

76.0

79.3

75.3

77.2

74.2

Depressive illness

% Yes

67.1

67.5

55.8

75.9

72.9

61.3

74.2

64.2

Dementia

% Yes

53.2

50.9

62.8

55.6

43.1

49.1

53.1

Anxiety, neurosis or panic disorder

% Yes

35.9

35.8

27.8

43.3

38.5

32.2

40.7

33.6

Drug misuse or addiction

% Yes

26.2

31.4

30.4

26.5

31.1

28.0

25.9

30.9

Gambling addiction

% Yes

22.8

27.4

36.9

26.5

20.8

18.5

32.3

22.4

Bipolar disorder

% Yes

15.7

14.8

15.8

15.7

12.6

14.2

15.6

Personality disorders

% Yes

10.6

8.5

7.0

6.5

11.2

12.5

7.3

10.3

Eating disorders

% Yes

9.5

4.4

5.1

3.1

6.7

12.0

5.2

7.0

Behavioural or emotional disorders

% Yes

7.7

7.3

8.8

7.1

6.8

7.4

5.1

8.6

Schizophrenia or other psychoses

% Yes

7.2

7.3

12.2

8.0

5.1

4.0

6.7

7.5

Intellectual disability

% Yes

3.8

2.6

5.8

2.3

1.7

3.2

2.2

3.5

Autism spectrum disorders

% Yes

1.7

3.4

1.1

1.4

1.0

1.2

2.0

Other

% Yes

1.2

<1.0

1.0

1.5

<1.0

1.0

Note: Bolded font indicates the five highest endorsed major mental health problems faced by men aged 40 years and over.

65+

Sample Australia All other countries

Males

Figure 8 presents what men aged 40 years and over believed were the major mental health problems their age group faces. Clearly alcohol, depression, dementia and anxiety-related issues were the major health concerns for men of their own age. This was followed by drug misuse or addiction, with almost three in 10 men aged 40 years and over reporting this as a major mental health problem. Gambling was a problem endorsed by 17.9% of men in the 40 years and over age group; which is a slightly lower endorsement rate than what all respondents in the five-country sample believed (25.5%). It is also noteworthy that 11.8% of men who were 40 years and over also endorsed eating disorders as a problem for men in their age group, especially when this is compared to 6.5% of the five-country sample and 4.4% of the female sample.

Note: Percentages less than 10% are not presented in the figure.

Figure 8. Percentage of endorsed major mental health problems for men aged 40 years and over by men of the same age in a five-country sample (n=2,596)

// AGE OF ONSET Figure 9 presents respondentsâ&#x20AC;&#x2122; views on the age of onset of major physical health problems, major mental health problems, and alcohol and/or other substance misuse. Both alcohol and/or other substance misuse and mental health problems were seen as commencing in adolescence and early adulthood. For these two major problems, respondentsâ&#x20AC;&#x2122; beliefs were tightly clustered around these age points, which is represented as a sharp spike in Figure 9 at 16 to 19 years of age (49.4% of respondents for alcohol and/or other substance misuse; 32% of respondents for major mental health problems). There was a wider spread of beliefs relating to the onset of major physical health problems, with the largest proportion of respondents (16.7%) suggesting they start between 40 and 44 years.

Figure 9. Respondentâ&#x20AC;&#x2122;s beliefs concerning the age of onset of major physical health, mental health, and alcohol and/or other substance misuse (%) in a five-country sample (physical health n=9,632; mental health n=9,625; alcohol and/or other substance misuse n=9,624)

// PERCEPTIONS OF HEALTH AND WELLBEING CHAPTER SUMMARY There is now global public recognition that mental health disorders are a serious issue for young men. In this survey, mental health problems were consistently endorsed most frequently as a ‘major health problem’ for young men across gender, age-band and country sub-samples. These findings are consistent with the literature, which suggests that young men have a higher risk of alcohol and/or other substance misuse, are more likely to die by suicide than young women, and are less likely to access services (Lawrence et al 2015; ABS 2011; Ministry of Youth Development 2004). In addition, there is clear evidence that suicide is a growing concern for young men globally; with studies highlighting suicide as among the top five causes of death for young men (White & Holmes 2006). The high incidence, combined with the lack of public awareness and reluctance of men to access services, have resulted in Bilsker and White (2011) referring to suicide in young men as a ’silent epidemic’. Accidental injury is also an issue of concern for young men. Other research has reported that compared to older men, young men are more likely to engage in hazardous alcohol and drug-related behaviours that result in injury. For example, they are seven times more likely to be involved in a car crash than older males (AIHW 2008; Ministry of Youth Development 2004). As we progress across the lifespan, it was clear that male respondents’ focus shifted from viewing mental health as a problem for young men, to viewing physical health as the major area of concern for men in middle and later years. Physical health problems were viewed as commencing much later in life; whereas mental health problems were seen as commencing during adolescence and early adulthood. Age of onset results for mental health problems were reasonably accurate; with research suggesting first onset of mental health problems usually occurs in childhood or adolescence (Jones 2013; Kessler et al 2007). However, there was a noticeable disconnect between young men’s views on their own major health problems and their views on what health problems might be for older men. In total 81% of respondents aged 16 to 24 years identified ‘brain, behavioural and mental health’ as a major health problem for younger men, however this figure reduced to 36% when considering older men. This pattern of response was repeated across gender, age and country sample. This suggests that all respondents, particularly those in the 16 to 24-year-old age group, viewed mental health problems as an issue affecting younger men to a greater extent than older men despite the high prevalence of mental health disorders in all ages across each participating country (AIHW 2015;

New Zealand Ministry of Health 2015; US Centre for Behavioural Health Statistics and Quality 2015; HSCIC 2009; Gravel & Beland 2005). Non-accidental injury is also important to consider for both younger and older men. The endorsement of nonaccidental injury as a major problem for older men was relatively low across all age groups, gender and country sub-samples when compared to national prevalence data. In reality, men aged 40 years and over are the highest risk age group for suicide across all countries (ABS 2016; CDC 2015; Statistics Canada 2015; ONS 2015). This national data also shows that the suicide rates for men aged over 40 years has risen significantly over the past decade. The current survey results clearly demonstrate a lack of public awareness regarding the risk of non-accidental injury in older men. In this study, older men (aged 40 years and over) under-estimated this as a major health problem for men in their own age group. Greater awareness is clearly needed that mental health and non-accidental injury are not just problems young men face, but are also major concerns for older men. For older men, the major physical health problems that were endorsed as an issue by respondents are consistent with international statistics on disease burden and causes of death. For example, heart disease is the largest cause of death globally (WHO 2014). These differences across age groups may be due to the type of health problems included in this survey, but also may reflect the public perspective that physical health problems are only a concern for older men. Importantly, research suggests that risk factors, such as obesity, that are present in adolescence have been shown to increase the risk of cardiovascular disease in adulthood (May, Kuklina & Yoon 2012). The views by respondents in this survey could relate to a perception that younger men are healthier and less at risk of physical health problems. This view may also be related to a perceived â&#x20AC;&#x2DC;reactiveâ&#x20AC;&#x2122; approach that men adopt towards health, whereby individuals only seek support or advice when something goes wrong (Mellor et al 2012), or unless the issue affects them directly. Internationally-focused recommendations aimed at improving physical health of men, suggest that early intervention to target behavioural risk factors are necessary (WHO 2014, 2015; Menâ&#x20AC;&#x2122;s Health forum 2011; Robinson et al 2010; US Department of Health and Human Services 2008; Government of Canada 2006; Johnson et al 2006). Many of these frequently endorsed physical health problems can be prevented or treated more effectively if identified at an earlier stage. Thus, an increased awareness of these higher risk physical healthcare areas, which could include an intergenerational dialogue, may be key to improving the long-term health and wellbeing of men.

Other areas were also important to consider. For example, in older men, when compared to alcohol abuse or addiction, dementia was under-reported by respondents as a major mental health problem for older men. Although alcohol misuse is reported to account for 5% of global burden and disease and injury, dementia is one of the fastest growing international public health concerns; with over 46 million people worldwide affected by dementia, which is projected to grow to 131.5 million by 2050 (WHO 2015). Additionally, the perception of eating disorders as an issue for men showed a small, but existent, gender divide. Both younger men and older men endorsed eating disorders as a major health problem for men in their age group (16.1% and 11.6%, respectively); whereas females endorsed this as a major health problem for men at a rate of 8.9% and 4.4% for younger and older men, respectively. Eating disorders can cause both severe impact on quality of life and mortality (Reas & Stedal 2015). Although eating disorders typically present in adolescence or young adulthood, these conditions can affect both genders irrespective of age (Lapid et al 2010; Newton 2013). Results from this survey demonstrate that this is viewed by one in 10 men as a major health problem and remains an issue for men across the generations. Reas & Stedal (2015) suggest that men of all ages with disordered eating remain an “…understudied, undertreated, and misunderstood population…”. This in itself is a major issue as research suggests that for older men and men in their middle years, eating disorders are met with depression, shame and stigma (Strother et al 2012), which is attributed to stereotypes in society that assume eating disorders are associated with females and youth populations (Reas & Stedal 2015).

Masculinity, Emotionality and Social Connectedness // CHAPTER OVERVIEW This chapter reports on three main areas measured in Module D of the survey, including:   

Masculinity; Emotionality; and, Social connectedness.

// OVERVIEW OF MASCULINITY, EMOTIONALITY AND SOCIAL CONNECTEDNESS CONFORMITY TO MASCULINE NORMS: Behavioural and affective adherence to stereotypical masculine gender roles was measured using an adapted version of the Conformity to Masculine Norms Scale (CMNI-22, Mahalik et al 2003). Twenty-two items were rated on a five-point Likert-scale (‘strongly disagree’ to ‘strongly agree’). A neutral response option was included. The CMNI-22 has 11 subscales including ‘winning’,

‘emotional

control’,

‘risk-taking’,

‘violence’,

‘power

over

women’,

‘dominance’, ‘playboy’, ‘self-reliance’, ‘primacy of work’, ‘disdain for homosexuality’, and ‘pursuit of status’. Nine items were reverse scored (eg. “I like to talk about my feelings”). Higher scores indicate a higher conformity to masculine norms. Internal consistency for the CMNI-22 was α=0.67 (n=8,201), with individual subscales ranging from α=0.43 (power over women subscale) to α=0.87 (emotional control subscale). EMOTIONALITY: The Toronto Empathy Questionnaire (TEQ, Spreng et al 2009) ‘emotionality’ subscale was used to measure emotional empathy on a 16-item fivepoint Likert-scale. Higher scores indicate higher levels of emotional empathy. Eight items that measured low empathy (eg. “I’m not really interested in how other people feel”) were reverse scored. Internal consistency for the TEQ was α=0.84 (n=8,170). SOCIAL CONNECTEDNESS: The 11-item Duke Social Support Index (DSSI, Koenig et al 1993) captured social support, including the ‘social interaction’ subscale and the ‘satisfaction with social support’ subscale. Internal consistency for the DSSI (Satisfaction with Social Support) was α=0.87 (n=8,147). Additionally, ‘perceived social support’ and ‘conflict in close relationships’ were measured by the five-item Schuster’s Social Support Scale (Schuster et al 1990). Respondents rated their social support from family and friends on a four-point Likert-scale (‘never’ to ‘often’). Higher 58

scores indicate higher social support. Internal consistency was α=0.71 (n=8,139) for the Schuster’s Social Support Scale. The 12-items on the ‘care dimension’ of the Intimate Bond Measure (IBM, Wilhelm & Parker 1988) were used as an indicator of perceived care from one’s partner. Items were rated on a four-point Likert-scale (‘not true at all’ to ‘very true’). Internal consistency was α=0.96 (n=8,102) for the IBM. // MASCULINITY, EMOTIONALITY AND SOCIAL CONNECTEDNESS Descriptive and frequency statistics for conformity to masculine norms, emotionality and social connectedness measures for the five-country sample are presented in Table 13 and Table 14. Table 13. Masculinity, emotionality and social connectedness scores (five-country sample) n

Mean scores [95%CI]

8,201 8,170 8,147 8,139 8,102

54.9 [54.7-55.0] 48.1 [47.9-48.2] 14.5 [14.5-14.6] 9.3 [9.2-9.3] 25.5 [25.3-25.7]

Indicator Conformity to Masculine Norms Index Toronto Empathy Questionnaire (Emotionality subscale) Duke Social Support Index (Satisfaction with Social Support) Schuster’s Social Support Scale Intimate Bond Measure

Table 14. Masculinity, emotional empathy and social connectedness scores (comparative sub-samples) Gender

Age-bands (years)

Sample

Males

Females

16 to 24

25 to 44

45 to 64

65+

Australia only

All other countries

57.6

53.0

58.2

54.7

53.0

54.3

53.6

55.4

[57.3-57.9]

[52.8-53.2]

[57.8-58.5]

[54.3-55.0]

[52.7-53.2]

[53.9-54.7]

[53.3-53.9]

[55.2-55.7]

45.8

49.6

47.6

48.2

48.7

47.4

48.4

47.9

[45.5-46.0]

[49.4-49.8]

[47.2-47.9]

[47.9-48.6]

[48.4-48.9]

[47.0-47.7]

[48.1-48.7]

[47.7-48.1]

Indicator Conformity to Masculine Norms Index (Adapted)

Mean

Toronto Empathy Questionnaire [emotionality subscale]

Mean

Duke Social Support Index [satisfaction with social support]

Mean

Schusterâ&#x20AC;&#x2122;s Social Support Scale

Mean

Intimate Bond Measure

Mean

[95% CI]

14.4

14.6

13.6

14.5

14.9

15.2

14.6

14.5

[14.3-14.5]

[14.6-14.7]

[13.4-13.7]

[14.4-14.7]

[14.7-15.0]

[15.1-15.4]

[14.5-14.7]

[14.4-14.6]

9.3

9.2

8.7

9.4

9.1

10.0

9.2

9.3

[9.2-9.4]

[9.1-9.3]

[8.6-8.8]

[9.3-9.6]

[9.0-9.2]

[9.8-10.1]

[9.1-9.3]

[9.2-9.3]

25.0

25.9

25.7

26.4

24.8

25.6

25.5

[24.7-25.3]

[25.6-26.1]

[25.3-26.1]

[25.9-26.8]

[24.4-25.2]

[25.1-26.1]

[25.2-25.9]

[25.3-25.8]

An exploratory latent class analysis was run with the five-country sample using all CMNI-22 items in R essentials (R Core Team 2013) for IBM SPSS version 22.0. This was carried out to identify subgroups of people based on their responses to the conformity to masculine norms index. Latent class analysis is a statistical method for identifying unobservable subgroups within a population. The categorical response options for the ‘strongly disagree’ and ‘disagree’ versus ‘neutral’ versus ‘agree’ and ‘strongly agree’ were combined and used in the analysis. No reverse score coding was used for this analysis. The exploratory latent structure was determined by identifying loadings higher than 0.4 on each item and resulted in three optimal classes. For each of these classes, significant loadings were imposed onto individual’s responses. Degree of class membership (i.e. membership to the subgroup) was then calculated as a mean of the highest loadings within each class. Final scores for each respondent represents maximum likelihood of class membership. Table 15 shows the probability of having an ‘agree’ or ‘strongly agree’ response to each CMNI-22 item based on class. Two items ‘violence is never justified’ and ‘men and women should respect each other as equals’ loaded across all three classes, whilst 10 items did not load at a level <0.4 onto any class. Class 1 (C1) loaded (>0.4) onto seven items. Respondents who were in this class endorsed views that reflected a preference for communication, risk aversion and a measured focus on life (i.e. work and winning were not their primary focus). Probabilities for Class 2 (C2) loaded onto seven items. Respondents who were in this class endorsed views that were more impulsive, driven, open to experience and self-reliant. Probabilities for Class 3 (C3) loaded onto seven items. Respondents who were in this class, endorsed views that were more risk adverse, avoided positions of status, and reported feeling bothered by the process of help-seeking. These findings must be considered as purely exploratory and require confirmation with additional latent class analysis statistical programs (such as MPlus 2010, Latent Gold 2010). Before fully defining these latent classes, the relationship with other variables (social connectedness, intimate bonds and emotionality) required exploration.

Table 15. Exploratory latent structure of CMNI-22 and its optimal three-class solution (fivecountry sample; n=8,201) Class loadings One (C1)

Two (C2)

Three (C3)

CMNI-22 Item My school work is the most important part of my life

0.24

0.43

0.24

I make sure people do as I say

0.06

0.21

0.05

In general, I don't like risky situations

0.49

0.36

0.53

It would be awful if someone thought I was gay

0.06

0.24

0.12

I love it when men are in charge of women

0.02

0.08

0.02

I like to talk about my feelings

0.83

0.38

0.06

I would feel good if I had many sexual partners

0.05

0.23

0.06

It is important to me that people think I'm heterosexual

0.09

0.29

0.12

I believe that violence is never justified

0.69

0.41

0.59

I tend to share my feelings

0.87

0.41

0.05

I should be in charge

0.13

0.36

0.06

I would hate to be important

0.09

0.06

0.23

Sometimes violent action is necessary

0.22

0.44

0.26

I don't like giving all my attention to school/work

0.52

0.40

0.47

More often than not, losing doesn't bother me

0.48

0.22

0.47

If I could, I would frequently change sexual partners

0.04

0.17

0.06

I never do things to be an important person

0.30

0.16

0.45

I never ask for help

0.08

0.19

0.32

I enjoy taking risks

0.27

0.43

0.19

Men and women should respect each other as equals

0.98

0.89

0.96

Winning is the most important thing

0.02

0.20

0.03

It bothers me when I have to ask for help

0.26

0.40

0.54

Figure 10 illustrates the relative magnitude of each class by mean loadings for all respondents; and simple linear regression demonstrated significant variance explained between these classes. It was found that 2.7% (AdjR2) of the variance in C1 was explained by C2: (F [1, 8,204]=230.5; p<0.01; β=-0.17, p<0.01); <1% (AdjR2) of the variance in C1 was explained by C3: (F[1, 8,205]=35.7; p<0.01; β=-0.07, p<0.01); and 5% (AdjR2) of the variance in C2 was explained by C3: (F[1, 8,206]=431.3; p<0.01; β=-0.22, p<0.01). Although significant explanation of variance was found, each class was relatively independent from the other (i.e. 95% or more of the variance in each class was not explained by another).

Figure 10. Heterogeneity of the optimal CMNI-22 three-class solution with associations using the five-country sample (n=8,205)

Table 16 demonstrates mean loadings for all respondents on each of the three classes for all sub-samples. Reflecting largest class membership, higher mean loadings were observed across all subgroups for C1. Table 17 presents the effect sizes for subgroup comparisons of the average mean difference. Across gender, the greatest difference in effect size was for C2 (small to medium effect size). Similarly, the 16 to 24-year-old age-band compared to other ages demonstrated the largest difference for C2 (medium effect size). For both the country sample and C3, results demonstrated relative stability in mean loadings across class and subgroups (with weak to small effect sizes).

Table 16. Mean loadings for the optimal CMNI-22 three-class solution for each latent class (comparative sub-samples) Gender

Age-bands (years)

Sample

Males

Females

16 to 24

25 to 44

45 to 64

65+

Australia only

All other countries

3,297

4,908

1,968

1,881

2,844

1,512

2,572

5,633

Mean [95% CI]

1.68

1.76

1.65

1.75

1.76

1.74

1.76

1.71

[1.67-1.69]

[1.75-1.77]

[1.64-1.66]

[1.73-1.76]

[1.75-1.78]

[1.72-1.75]

[1.75-1.77]

[1.71-1.72]

0.85

0.77

0.89

0.78

0.77

0.78

0.77

0.82

[0.84-0.85]

[0.77-0.78]

[0.88-0.90]

[0.77-0.79]

[0.76-0.78]

[0.77-0.79]

[0.76-0.78]

[0.81-0.82]

1.01

1.06

1.05

1.02

1.04

1.03

[1.00-1.02]

[1.05-1.06]

[1.04-1.06]

[1.01-1.03]

[1.03-1.05]

[1.02-1.05]

[1.03-1.05]

[1.03-1.04]

Mean [95% CI]

Table 17. Effect size of average mean differences (Cohenâ&#x20AC;&#x2122;s d; comparative sub-samples) Males vs Females

16 to 24 year olds vs

Australia vs

all other age-bands

all other countries

-0.31

-0.36

0.17

0.34

0.51

-0.23

-0.17

0.05

0.04

Note: Statistically significant items (p<0.01) are highlighted in bold.

Table 18 presents mean loadings for location (rural/ remote vs not rural/ remote) and education attainment. There were no significant differences (i.e. p>0.05) in class membership.

Table 18. Descriptive statistics for measured variables by location and education (five-country sample) Latent Class Mean [95% CI] C1

Rural/ remote

1.74 [1.71-1.76]

0.79 [0.77-0.81]

1.05 [1.02-1.07]

Non Rural/ remote

1.72 [1.72-1.73]

0.81 [0.80-0.81]

1.03 [1.03-1.04]

Secondary school or less

1.66 [1.64-1.67]

0.83 [0.82-0.84]

1.10 [1.09-1.11]

Location

Education Certificate or diploma

1.73 [1.71-1.74]

0.79 [0.77-0.80]

1.08 [1.06-1.09]

Tertiary degree

1.75 [1.74-1.76]

0.80 [0.80-0.81]

1.00 [0.99-1.01]

Table 19 provides additional information for employment types including NEET (not in education or training), employment status and student status comparison groups for all three class memberships. Respondents who were unemployed significantly loaded onto C3 more than those who were employed (medium effect; Cohen’s d=0.38). Students demonstrated strongest class membership to C2, which was significantly different to respondents who were not students (medium to strong effect; Cohen’s d=0.57). Table 19. Descriptives and Cohen’s d statistics for employment and education types (five-country sample)

NEET

Yes

Cohen’s d

Mean [95% CI]

1.73 [1.72-1.74]

1.71 [1.70-1.73]

0.05

0.82 [0.82-0.83]

0.76 [0.75-0.77]

0.28

1.01 [1.01-1.01]

1.09 [1.08-1.10]

-0.28

1.73 [1.72-1.74]

1.68 [1.65-1.70]

0.19

0.81 [0.80-.81]

0.77 [0.75-0.78]

0.18

1.03 [1.02-1.03]

1.13 [1.11-1.15]

-0.38

1.74 [1.74-1.75]

1.66 [1.64-1.67]

0.31

0.78 [0.78-0.79]

0.90 [0.89-0.91]

0.56

1.03 [1.03-1.04]

1.04 [1.03-1.06]

0.04

Unemployed

Student

Note: Statistically significant items (p ≤0.05) are highlighted in bold.

Further to the above employment analyses, military service status and type of industry were examined, as displayed in Table 20. For military service, there were significant differences for all class membership groups; however, effect sizes were small (C1: d=0.26, C2: d=-0.28) and weak (C3: d=0.11). For industries that were classified as ‘male dominated’ or ‘female dominated’, there were significant differences for all class membership groups; however, effect sizes were small (C1: d=-0.30) to weak (C2: d=0.14, C3: d=0.13). Table 20. Descriptives and Cohen’s d statistics for military service and type of employment (five-country sample)

Military

Yes

Cohen’s d

Mean [95% CI]

1.66 [1.63-1.68]

1.73 [1.73-1.74]

0.86 [0.84-0.87]

0.80 [0.79-0.80]

0.28

1.01 [0.99-1.03]

1.04 [1.03-1.05]

-0.11

Male dominated

Female dominated

1.67 [1.64-1.69]

1.77 [1.76-1.78]

0.82 [0.80-0.84]

0.79 [0.78-0.80]

0.14

1.06 [1.04-1.08]

1.02 [1.01-1.03]

-0.13

-0.26

Industry -0.37

Note: Statistically significant items (p ≤0.05) are highlighted in bold.

A series of linear regressions were calculated to predict social connectedness based on CMNI-22 classes. Age and gender were included in the analysis to control for their potential confounding effects. This was conducted with the dependent variable being: 1) intimate relationships with partner (or someone very close to the respondent – IBM); 2) social support from family and friends (SSSS); and 3) satisfaction with social support (DUKE). Additionally, a secondary series of linear regressions were calculated using the male only sub-sample. For intimate relationships a significant regression was found (F [5, 8,091]=100.9, p<0.01) explaining 5.8% of the variance (AdjR2). Age (β=-0.05, p<0.01) and C3 (β=0.15, p<0.01) were significant negative predictors, whilst C1 (β=0.17, p<0.01) was a significant positive predictor. Gender and C2 did not significantly explain variance. This indicated that higher ratings on intimate relationships were more common for respondents who identified more closely with C1 and were younger. Lower intimate relationship ratings were associated with respondents who identified more strongly

with C3. This finding was relatively consistent when run again with data from the male sub-sample only, however, age did not significantly explain variance. For social support, a significant regression was found (F [5, 8,127]=117.4, p<0.01) explaining 6.7% of the variance (AdjR2). Age (β=0.10, p<0.01) and C1 (β=0.12, p<0.01) were significant positive predictors of the variance in social support scores. C2 (β=-0.06, p<0.01) and C3 (β=-0.18, p<0.01) were significant negative predictors. Gender did not significantly explain variance. This indicated that higher social support ratings were more common for respondents who identified more strongly with C1 and for those who were older. Lower social support ratings were associated with respondents who identified more strongly with C2 and C3. This finding was consistent when run again with data from the male sub-sample only. For satisfaction with social support, a significant regression was found (F [5, 8,135]=325.2, p<0.01) explaining 16.6% of the variance (AdjR2). Age (β=0.17, p<0.01), gender (β=0.05, p<0.01) and C1 (β=0.24, p<0.01), were significant positive predictors of the variance in social support satisfaction scores. C3 (β=-0.25, p<0.01) was a significant negative predictor. C2 did not significantly explain variance. This indicated that higher social support satisfaction was more common for respondents who were female, older and identified more strongly with C1. Lower social support satisfaction scores were associated with respondents who identified more closely with C3. This finding was consistent when run again with data from the male subsample only. A linear regression was calculated to predict emotionality (TEQ emotionality subscale) based on the identified CMNI-22 classes. Age and gender were included in the analysis to control for their potential confounding effects. A significant regression was found (F [5, 8,158]=367.9, p<0.01) explaining 18.3% of the variance (AdjR2). Gender (β=0.20, p<0.01) and C1 (β=0.32, p<0.01) were significant positive predictors of the variance in higher emotionality scores (indicating higher emotional empathy). C2 (β=-0.06, p<0.01) and C3 (β=-0.12, p<0.01) were significant negative predictors. Age did not significantly explain variance. This indicated that higher emotionality ratings were more common for respondents who were female and for those who identified more strongly with C1. Lower emotionality ratings were associated with respondents who identified more strongly with C2 and C3. The initial findings from the five-country sample (using male and female data) were consistent when a linear regression was calculated with data from the male sub-sample only, however, age significantly explained variance (F [4, 3,272]=161.8, β=0.045, p<0.01).

Figure 11 shows significant beta scores for class, age and gender for social connectedness measures (IBM, SSSS and DUKE satisfaction with social support subscale). Figure 12 shows the equivalent results for the male sub-sample only. Overall, although the total variance explained by these independent variables within each social support measure is small, it is still significant. C1 had a positive relationship with overall social connectedness and emotionality. C2 had no relationship with intimate relationships and satisfaction with social support, however, there was a small negative relationship with actual social support and emotionality. C3 showed a negative relationship with overall social connectedness and emotionality. It was consistently demonstrated that those who were older had higher ratings of actual social support (from friends and family) and satisfaction with social support. The influence of age varied when considering intimate relationships and emotionality, and was non-significant for the male sub-sample. A relationship between gender and social connectedness was present for only one measure, with males having lower satisfaction with social support scores than females. Males as a whole also had lower emotionality scores.

Figure 11. Significant beta scores for social connectedness measures (IBM, SSSS and Duke satisfaction with Social Support Subscale) and emotionality by class, age and gender for the five-country sample

Figure 12. Significant beta scores for social connectedness measures (IBM, SSSS and Duke satisfaction with Social Support Subscale) and emotionality by class and age for the male sub-sample

// MASCULINITY, EMOTIONALITY AND SOCIAL CONNECTEDNESS CHAPTER SUMMARY Perceptions regarding gender roles often focus on the influence of socialisation including families, peer groups and the general public (Wilkins 2010). As a result, it is thought that people treat boys and girls differently and it is argued that they therefore develop different attitudes and behaviours. In this study, these ‘attitudes’ relating to conformity to masculine norms grouped into latent classes reflected three differing clusters, which included both men and women. There is a potential here to move away from more traditional or stereotyped views on masculinity and masculine conformity towards acknowledging the influences of different environments and contexts. This rethinking of masculinity has been called for, by Connell and Messerschmidt (2005). This series of analyses demonstrate that thinking might be re-framed to be more aligned to reflect those who are ‘socially connected’ and have more emotionality (C1), those who were ‘self-reliant risk takers’ as they displayed boyish type views and behaviours (i.e. express views that are more impulsive, driven, open to experience and self-reliant, but demonstrate less emotionality and social connectedness; C2), and those who are more ‘isolated’ and have lower emotionality (C3). This argument is supported by the results of the regression analyses for social connectedness measures. As the results show, this ‘socially connected’ group (C1) was most frequently identified with by respondents, followed by those who were ‘isolated’ (C3) and then those who held more ‘self-reliant risk taking’ beliefs (C2). These results were also consistent for gender with an equal split of men and women identifying with the ‘socially connected’ group (C1). Interestingly, when reviewing those who held ‘selfreliant risk taking’ beliefs (C2) and those who were more socially ‘isolated’ (C3) differences between gender and age emerged. More males identified with ‘selfreliant risk taking’ views (C2), whereas more females identified with those who were more socially ‘isolated’ (C3). It is not surprising that men may associate more with ‘self-reliant risk taking’ beliefs (C2) given the types of beliefs and their similarity to societal depictions of masculinity which emphasise self-reliance, stoicism and strength, as well as the desire to manage personal problems independently (Emslie et al 2006; Evans et al 2011; Moller-Leimkuhler 2002). Younger respondents were also more likely to identify with this ‘self-reliant risk taking’ group and were less likely to identify with the ‘socially connected' group (C1). Again younger respondents identifying more closely with ‘self-reliant risk taking’ views (C2) is consistent with the

literature highlighting younger people are more likely to engage in ‘risk’ taking behaviour due to a number of social and environmental factors (Thomas et al 2007). Employment and industry also showed an interesting relationship to the latent classes. Those who were unemployed were more likely to identify with those who were more socially ‘isolated’ (C3). As will be demonstrated in later chapters, this is consistent in the literature as health outcomes for those that are unemployed as significantly worse than for those in work (Norstrom et al 2014). For work industry, those who work in male dominated environments were more likely to identify with C2 and C3, whereas those who worked in female dominated environments were more likely to identify with the more ‘socially connected’ group (C1). The influence of work industry is discussed further within the report related to stigma, help-seeking and psychological distress. As well as representing individual beliefs, these latent classes could also be viewed in the context of different life stages and experiences, with C1 demonstrating more socially connected individuals, C2 as being seen as a transition or ‘testing’ stage, and C3 as representing those who are more marginalised potentially due to a number of personal, social and economic factors. The factors that influence which latent class an individual identifies with becomes of interest in enabling the association with positive or negative health outcomes and social functioning. The impact of these three latent classes will be explored in more depth in the following chapters to consider health, mental health, stigma and help-seeking behaviour. By doing this, services and the community as a whole may be able to consider how they can be more responsive to gender specific health and wellbeing needs.

Mental Health, Wellbeing, Happiness and Resilience // CHAPTER OVERVIEW This chapter reports on three main areas measured in Module C of the survey, including:   

Overall health; Mental health, wellbeing and suicidality; and, Happiness and resilience.

// OVERALL HEALTH OVERALL HEALTH MEASURE: Respondent’s overall health was measured by a single-item measure (Blanchard et al 2014). Respondents were asked “How would you rate your overall health in the past 4 weeks?” and were provided with a five-point Likert-scale ranging from ‘very bad’ to ‘very good’. Frequency statistics for overall health items from the five-country sample are presented in Table 21. Table 21. Frequency statistics for overall health ratings (five-country sample) Five-country sample Overall health rating

9,025

Very good

18.7

Good

38.8

Moderate

27.8

Bad

11.8

Very bad

2.8

The results indicate that in general, respondents appear to perceive their own health as good with 57.5% reporting either good or very good. A total of 27.8% of the respondents reported moderate overall health with only 14.6% reporting bad or very bad. Frequency statistics for overall health items for sub-samples are shown in Table 22.

Table 22. Frequency statistics for overall health rating (comparative sub-samples) Gender

Sample

Males

Females

16 to 24

25 to 44

45 to 64

65+

Australia

All other countries

3,628

5,397

2,174

2,075

3,095

1,681

2,847

6,178

Very good

20.8

17.3

15.0

18.2

20.1

21.8

18.5

18.8

Good

40.9

37.4

37.3

40.5

37.7

40.7

39.6

38.5

Moderate

25.2

29.5

30.0

27.8

26.9

26.5

27.5

27.9

Bad

10.6

12.6

13.9

10.3

12.3

10.1

11.2

12.1

Very bad

2.4

3.2

3.8

3.2

3.0

<1.0

3.2

2.7

Overall health rating

Age-bands (years)

Results demonstrate key differences across age and gender. Respondents aged 16 to 24 years had the poorest overall health rating with 17.7% reporting either bad or very bad overall health, as opposed to those 65 years and older with only 10.9% reporting bad or very bad overall health. In addition to this, perceptions of positive health appear to increase with age. The results indicate that men rated their overall health as higher than women; with 61.7% of men reporting good or very good overall health as opposed to 54.7% of women. DAYS OUT OF ROLE: A further question to investigate burden was asked about ‘days out of role’ which was extracted from the Brief Disability Questionnaire (BDQ, von Korff et al 1996). Respondents were asked “During the past month, how many days in total were you unable to carry out your usual daily activities fully (eg. going to work or school)?”. Descriptive and frequency statistics for ‘days out of role’ from the five-country sample are presented in Table 23 to Table 25. Table 23. Descriptive statistics for ‘days out of role’ (five-country sample)

Days out of role

Mean

95% CI

Min

Max

9,018

4.9

[4.6-5.0]

Table 24. Frequency statistics for ‘days out of role’ (five-country sample)

Days out of role

9018

0 days

50.2

1 to 2 days

17.0

3 to 4 days

7.9

5 to 7 days

6.2

8 to 14 days

5.1

15 to 21 days

4.3

22 days or more

9.4

Most respondents reported taking very few days out of role per month; with 67.1% of all respondents reporting either none or one to two days out of role.

Table 25. Frequency statistics for â&#x20AC;&#x2DC;days out of roleâ&#x20AC;&#x2122; (comparative sub-samples) Gender

Days out of role

Age-bands (years)

Sample

Males

Females

16 to 24

25 to 44

45 to 64

65+

Australia

3,624

5,394

2,173

2,073

3,094

1,678

2,845

All other countries 6,173

0 days

57.1

45.6

38.5

50.1

53.5

59.6

48.4

51.1

1 to 2 days

14.2

18.8

20.2

19.9

14.9

12.8

18.9

16.0

3 to 4 days

6.0

9.1

10.2

7.9

6.5

7.4

8.5

7.6

5 to 7 days

5.0

6.9

9.3

5.5

4.7

5.5

6.9

5.8

8 to 14 days

4.1

5.7

7.7

3.2

5.1

3.9

5.0

5.1

15 to 21 days

3.9

4.5

5.5

3.7

4.4

3.1

4.6

4.1

22 days or more

9.7

9.3

8.5

9.6

10.9

7.7

7.8

10.2

The results in Table 25 above present ‘days out of role’ by age, gender and country sub-samples. When considering gender, men reported fewer days out of role than women; with 57.1% reporting zero days in comparison to 45.6% of women. There are also some notable differences when considering the impact of age on the results. Respondents aged 16 to 24 years reported the highest number of days out of role; with 41.2% reporting three or more days out of role. This is considerably more than those aged 65 and older; with only 27.6% of respondents in this age group reporting three or more days out of role.

// OVERALL HEALTH SUMMARY The international literature has shown that individuals are more likely to self-report that they are experiencing good health (Schermel et al 2014). The current results regarding overall health ratings reflect this; with the majority of respondents in this sample reporting good or very good health. Women are also more likely to report poorer health than men (Schermel et al 2014), which is consistent with the current survey’s findings. The differences in the current results related to ‘days out of role’ may also be indicative of life stages; for example, women may be more likely to have days out of role due to childcare responsibilities. Additionally, people over 65 years of age reported taking fewer days out of role. This was not unexpected, as a higher number of respondents in the age group of 65 years and over are likely to be retired, and therefore may perceive their role differently to those in employment. The respondents in this survey over the age of 65 years also had notably high endorsement of good overall health. Research has also reported that older people report their health as good despite experiencing health problems (Chen et al 2015), which may explain some of these differences in age-related responses. This has been attributed to a change in perceived standards of health by older people whereby they expect health to deteriorate with age and therefore measure health in the context of these expectations (Chen et al 2015; Vuorisalmi et al 2005). Further discussion regarding the link between psychological distress and days out of role is included below.

// MENTAL HEALTH PSYCHOLOGICAL DISTRESS: Respondents’ current psychological distress was measured using the 10-item Kessler Psychological Distress Scale (K10, Kessler et al 2003). The K10 assesses the frequency with which an individual experiences symptoms

general

psychological

distress

(eg.

nervousness,

tiredness,

hopelessness and restlessness) on a five-point Likert-scale (1=‘none of the time’ to 5=‘all of the time’) during the past month. Scores were summed to create total scores, which were then grouped into four levels of psychological distress (1=low; 2=moderate; 3=high; 4=very high). Internal consistency in the current study (n=9,011) for the K10 was α=0.93.

SUICIDALITY: Respondents’ level of suicidality and suicidal ideation over the past month was measured using the suicidality subscale from The Psychiatric Symptom Frequency Scale (PSFS, Lindelow et al 1997). The subscale consists of five items concerning suicidal ideation and acts which are each presented with dichotomous response options (1=no, 2=yes). The suicidality items were scored to contrast individuals who reported low suicidality (at most stating that at some point over the past year they had “felt that life was hardly worth living”) with those who admitted having “thought that they would be better off dead”, “thought of taking their own life”, “made plans to take their own life”, or “attempted to take their own life”. Internal consistency in the current study (n=8,705) for the PSFS was α=0.82.

SELF-HARM: Respondents level of self-harm behaviour was measured using a single item measure. Respondents were asked if they had harmed or hurt themselves on purpose to experience pain within the past 12 months. For each question, respondents were provided a dichotomous ‘yes’ or ‘no’ response option.

Descriptive and frequency statistics for mental health, suicidality and self-harm items from the five-country sample are presented in Table 26 and Table 27. Table 26. Descriptive statistics for psychological distress (K10; five-country sample)

K10 (psychological distress)

Mean

95% CI

Min

Max

9,011

19.8-21.2

Table 27. Frequency statistics for psychological distress (K10), suicidal ideation (PSFS) and self-harm measures (five-country sample) Five-country sample Indicator K10

9,011

Low

38.9

Moderate

27.2

High

18.6

Very high

15.3

8,707

Suicidal ideation

30.7

No suicidal ideation

69.3

8,707

Yes

10.6

89.4

PSFS

Self-harm

The results for the K10 show that 33.9% of the sample reported high to very high psychological distress. In addition to this, 30.7% reported suicidal ideation. Over a third of respondents reported low psychological distress and 27.2% reported moderate distress. The results for self-harm indicate that 10.6% of the sample reported that they had hurt themselves on purpose within the past 12 months.

Table 28. Frequency statistics for psychological distress (K10), suicidal ideation (PSFS) and self-harm (comparative sub-samples) Gender

Age-bands (years)

Sample

Males

Females

16 to 24

25 to 44

45 to 64

65+

Australia

All other countries

Indicator K10

3,622

5,389

2,174

2,072

3,091

1,674

2,841

6,170

Low

43.7

35.6

18.4

34.3

45.6

58.6

39.1

38.7

Moderate

27.0

27.3

26.7

28.7

26.7

26.9

25.7

27.9

High

17.0

19.7

25.3

20.8

16.9

10.6

18.9

18.5

Very high

12.3

17.4

29.6

16.2

10.8

3.9

16.3

14.9

3,490

5,217

2,066

2,010

3,015

1,616

2,767

5,940

Suicidal ideation

28.3

32.3

45.5

31.8

27.5

16.3

34.7

28.8

No suicidal ideation

71.7

67.7

54.5

68.2

72.5

83.7

65.3

71.2

n % %

3,490 6.0 94.0

5,217 13.6 86.4

2,066 28.6 71.4

2,010 11.2 88.8

3,015 3.1 96.9

1,616 <1.0 99.1

2,767 11.2 88.8

5,940 10.3 89.7

PSFS

Self-harm Yes No

The sub-sample results presented above in Table 28 indicate that men reported lower levels of psychological distress than women; with 29.3% of men reporting high or very high levels in comparison to 37.1% of women. Those aged 16 to 24 years reported the highest levels of psychological distress. Levels of distress appear to decrease with age, with only 14.5% of respondents aged 65 years and over reporting high or very high distress in comparison to 54.9% of 16 to 24 year olds. When considering the results for suicidal ideation, men and women reported similar levels of suicidal ideation with 28.3% and 32.3%, respectively. Reported levels of suicidal ideation also appear to decrease with age. Those aged 16 to 24 years reported the highest levels of suicidal ideation across all age groups at 45.5%.

The self-harm scores show that 6.0% of men and 13.6% of women reported hurting themselves on purpose within the past 12 months. The influence of age on selfharm shows that young people aged 16 to 24 years report the highest levels of selfharm with 28.6% reporting self-harm behaviour. When considering the influence of gender on young people and self-harm, the results show that 20.6% of men aged 16 to 24 years reported self-harm. This figure reduces to 7.2% when considering those men aged 25 to 44 years of age. This reduction with age is consistent with the rest of the five-country sample results, with self-harm behaviour decreasing considerably with those aged 65 years and over reporting less than 1%.

Table 29 presents ‘days out of role’ by psychological distress (K10) for the fivecountry sample. Respondents with high to very high psychological distress had an average of 7.7 days out of role (SD=9.3, range 0-30).

Table 29. BDQ ‘days out of role’ by psychological distress (K10; five-country sample) Low

Moderate

High

Very high

Days out of role 0 days

72.1

51.1

30.5

17.4

1 to 2 days

13.1

21.2

22.6

12.0

3 to 4 days

3.5

8.7

12.8

11.5

5 to 7 days

1.6

5.4

11.0

13.1

8 to 14 days

1.2

3.1

7.9

14.8

15 to 21 days

<1.0

2.2

5.7

14.7

22 days or more

7.6

8.2

9.5

16.3

Note: Percentages may not add to 100% due to rounding.

// MENTAL HEALTH SUMMARY According to the World Health Organisation (2001), approximately one in four people in the world will experience a mental health condition at some point in their lives. At a country level, prevalence rates are similar. For example, within Australia one in five Australians aged 16 to 85 years had experienced a mental disorder in the preceding 12 months, which is consistent with data from Canada (Smetanin et al 2012), and the US (Centre for Behavioural Statistics and Quality 2015). In New Zealand, lifetime prevalence rates of mental illness and/or addiction are one in five people (Oakley et al 2006) and one in four in the UK (The Health and Social Care Information Centre 2014; McManus et al 2009). Young people in this survey reported the highest levels of psychological distress, suicidal ideation and required more days out of role. Young people have been identified as a high risk population group for the development of mental health problems. Young people aged 15 to 24 years are more likely to experience mental illness and/or alcohol or other substance misuse than any other age group (Statistics Canada 2013) and have higher prevalence rates than adults 25 years of age and over (Oakley et al 2006). Mental health problems often commence at a young age and persist into adulthood (Government of Canada 2006; Costello et al 2006). The results regarding suicidal ideation are consistent with previous research; with evidence suggesting that levels of suicidal ideation increase during adolescence and are therefore more common in young people (Nock et al 2008). The Australian Bureau of Statistics reports the extent of suicide, showing that suicide remains the leading cause of death in young Australians (ABS 2016). In New Zealand, suicide was also the leading cause of death in the age group of 15 to 24 years (NZ Ministry of Health 2015). In Canada, suicide is the second leading cause of death for those aged 15 to 34, although over half of suicides involve people aged 45 years and over (Statistics Canada 2015). Despite evidence clearly showing that suicide is prevalent in young people, it is actually middle-aged men who are the highest at risk group. In addition to this, risk of suicide, particularly in men increases with age, with men aged 40 years and over most at risk (ABS 2016; CDC 2015; Statistics Canada 2015; ONS 2015). This is contradictory to the results here whereby suicidal ideation is lower in men and appears to reduce with age. It is also consistently reported that women express higher levels of suicidal ideation; however, it is men who have higher levels of taking their own life. Country-wide reporting of data related to suicide has also indicated

that males show a suicide rate of three to 7.5 times that of women (Nock et al 2008). In New Zealand, 75% of all suicides are male (NZ Ministry of Health 2015). A recent review has reported that the duration of the suicidal process is shorter in men than in women (Schrijvers et al 2012). They suggest that as the process may be shorter, there may be less time to identify help and as a result, opportunity for intervention is limited (Schrijvers et al 2012). Beaton & Forster (2012) also suggest that men have a higher level of acquired capability for suicide in that they choose more lethal methods and therefore are more likely to die by suicide. This combination of a shorter suicidal process and more lethal methods may result in more challenges in assessing suicide risk in men due to the limited time to intervene. Further work is required to explore suicidal ideation assessment specifically for men.

Young people in this survey also reported the highest levels of self-harm behaviour. Hawton et al (2012) argue that self-harm in adolescence is a major public health concern with around 10% of adolescents reporting self-harm. In these results, 10.6% of the five-country sample reported self-harm behaviour in the past 12 months, however, for young people in this sample, the rates are higher. Over a fifth of young men and just under a third of young women report self-harm behaviour in the past 12 months. These results may be influenced by sample bias based on individuals selecting to engage in the survey due to a personal interest in the subject matter. Despite this, the results are still substantial given the potential for longer term self-harm into adulthood. Moran et al (2012) argue that although self-harm behaviour may appear to reduce with age, it may continue into adulthood if the young person experiences mental health symptoms, engages in self-harm behaviours and does not access appropriate treatment. This research highlights the pivotal moment in adolescence where intervention is needed to reduce longer-term self-harm behaviour and other related mental health problems.

// WELLBEING, HAPPINESS AND RESILIENCE WELLBEING: The Personal Wellbeing Index scale contains seven items and was used to assess subjective wellbeing. Items were rated on a 10-point scale (1=‘Completely dissatisfied’ to 10=‘Completely satisfied’). Internal consistency in the current study (n=9,022) for the PWI was α=0.90. HAPPINESS: Four items from the Oxford Happiness Questionnaire (Hills & Argyle 2002) were used. Respondents rated the extent to which they agreed or disagreed with each statement on a five-point Likert-scale (1=‘Strongly disagree’ to 5=‘Strongly agree’). High scores indicate greater happiness. Internal consistency in the current study was α=0.83 (n=9,028). RESILIENCE: Four items of the Brief Resilience Coping Scale (Sinclair & Wallston 2004) were included. Respondents rated the extent to which they agreed or disagreed with each statement on a five-point Likert-scale (1=‘Strongly disagree’ to 5=‘Strongly agree’). Internal consistency in the current study was α=0.75 (n=9,024). A score between four and 13 indicated low resilience copers, 14 to 16 indicated copers with medium levels of resilience and a score of 17 to 20 indicated copers who were highly resilient.

Descriptive and frequency statistics for wellbeing, happiness and resilience items from the five-country sample are presented in Table 30 and Table 31.

Table 30. Descriptive statistics for wellbeing (PWI), happiness (OHQ) and resilience (BRCS) variables (five-country sample)

Indicator Personal Wellbeing Index Oxford Happiness Questionnaire Brief Resilient Coping Scale

Mean

95% CI

Min

Max

9,028 9,028 9,024

67.4 13.1 14.6

67.1-67.8 13.0-13.2 14.5-14.6

10 4 4

100 20 20

Table 31. Frequency statistics for wellbeing (PWI), happiness (OHQ) and resilience (BRCS) variables (comparative sub-samples) Gender

Age-bands (years)

Sample

Males

Females

16 to 24

25 to 44

45 to 64

65+

Australia

All other countries

3,629

5,399

2,175

2,075

3,097

1,681

2,847

6,181

Mean

67.2

67.6

65.8

66.5

66.3

72.8

68.2

67.1

66.6-67.8

67.1-68.1

65.0-66.6

65.7-67.3

65.6-67.0

72.0-73.6

67.5-68.8

66.7-67.6

3,626

5,402

2,174

2,076

3,098

1,680

2,847

6,181

Indicator Personal Wellbeing Index

95% CI

Oxford Happiness Questionnaire

Mean 95% CI Brief Resilient Coping Scale

n Mean 95% CI

13.3

12.9

12.2

12.5

13.1

14.7

13.0

13.1

13.2-13.4

12.8-13.0

12.1-12.4

12.4-12.7

13.0-13.3

14.6-14.9

12.8-13.1

13.1-13.2

3,624

5,400

2,174

2,074

3,097

1,679

2,846

6,178

14.7

14.5

13.9

14.5

14.8

15.3

14.4

14.7

14.6-14.8

14.4-14.6

13.8-14.0

14.4-14.6

14.7-14.9

15.1-15.4

14.3-14.5

14.6-14.7

Results showed that as age increased, personal wellbeing, happiness and resilience also increased. Young people aged 16 to 24 years reported the lowest scores on personal wellbeing, happiness and resilience. Respondents aged 65 years and older consistently reported the highest scores on these items. There are minor gender differences. Men reported a slightly higher happiness score than women.

// WELLBEING, HAPPINESS AND RESILIENCE SUMMARY When considering the Personal Wellbeing Index, recent evidence developed in Australia has demonstrated that the normal range for subjective wellbeing lies at 73.9-76.8 (Capic et al 2015). The results above show that in all groups, scoring for the PWI was lower than average. Those aged 65 years and older reported only slightly lower scores than population averages; however, young peopleâ&#x20AC;&#x2122;s wellbeing rating was 8.1% lower than this average. There is a well-established relationship between various demographic circumstances and subjective wellbeing. For example, it is commonly found that older adults report higher wellbeing than other age groups, particularly those in their middle years (Capic et al 2015; Melbourne Institute 2012; ONS 2015). The UK Office for National Statistics (2015) data regarding wellbeing demonstrated that the majority of young people were happy with their lives, and there was an increase in the proportion of young people reporting high or very high life satisfaction since 2011/12. This is interesting given the views of young people within this survey demonstrating the lowest scores of wellbeing, which is not surprising given the high level of psychological distress scores for this group in the current survey. In the following section (predictors of health and wellbeing), these variables are examined in context of each other. Resilience and coping are also factors that impact the mental health and functioning of individuals. The five-country sample results showed that young people aged 16 to 24 years reported the lowest levels of resilience and coping. This is consistent with recent evidence highlighting the influence of age on resilience, whereby higher resilience is associated with older age (Gooding et al 2012). These results may be linked to the higher levels of psychological distress young people experience and the influence of this on their ability to cope within different circumstances.

// PREDICTORS OF HEALTH, MENTAL HEALTH AND WELLBEING Logistic multiple regression analysis was used to identify predictors of good overall health, good mental health and high personal wellbeing. Good overall health was determined if a respondent indicated a score above four (good or very good) on the item “How would you rate your overall health in the past 4 weeks?” and was compared to those that rated their health under four (very bad, bad or moderate). For good mental health, the low/ mild distress K10 categories were compared to high/ very distress. Good personal wellbeing was determined if respondents had a score of 70 or above on the Personal Wellbeing Index. Analysis followed previously established procedures (Hickie et al 2001). Variables were entered into multiple regression models in discrete blocks of related variables to allow for the examination of potential effects of: 1) demographics; 2) education and employment characteristics; 3) masculinity/ ’socially connected’ latent class; 4) health, mental health and wellbeing status; and, 5) major life events separately. Variables were also entered simultaneously into regression models to explore these three areas (overall health, mental health and personal wellbeing) further, whilst controlling for all other variables. No missing data were imputed. Odds ratios and 95% confidence intervals are reported. Table 32 presents results from the five-country sample, and Table 33 presents results from the male sub-sample.

Table 32. Odds ratio relating to overall health, mental health and wellbeing by demographic, employment, masculinity, emotionality and social connectedness, and health characteristics (five-country sample; n=4,722) Predictor variable

Overall health (good/ very good)

PWI (>70)

OR [95% CI]

Mental health K10 (low/ very low psychological distress) OR [95% CI]

OR [95% CI]

16 to 24

1.39 [1.03-1.88]

0.19 [0.13-0.28]

1.81 [1.27-2.58]

25 to 44

1.39 [1.06-1.82]

0.38 [0.26-0.54]

1.18 [0.87-1.61]

45 to 64

1.12 [0.90-1.40]

0.68 [0.50-0.94]

0.86 [0.66-1.10]

1.00

1.39 [1.19-1.64]

1.34 [1.10-1.64]

0.82 [0.68-0.99]

1.00

Yes

1.02 [0.77-1.35]

0.91 [0.63-1.31]

1.18 [0.85-1.62]

1.00

1.08 [0.90-1.31]

1.71 [1.37-2.15]

1.30 [1.04-1.62]

1.00

Secondary or less

0.97 [0.79-1.19]

0.64 [0.50-0.82]

0.78 [0.62-0.99]

Certificate / diploma

0.98 [0.81-1.20]

0.77 [0.60-0.98]

0.91 [0.73-1.13]

1.00

Demographic characteristics Age-bands (years)

65+ Sex Males Females Rural

Sexual orientation Heterosexual LGBTQIA Education and employment characteristics Education

Tertiary

Predictor variable

Overall health (good/ very good)

Mental health K10 (high/ very high)

PWI (>70)

1.52 [1.24-1.85]

1.22 [0.94-1.59]

1.47 [1.16-1.85]

1.00

Male dominated

0.87 [0.66-1.15]

1.32 [0.92-1.90]

1.11 [0.81-1.51]

Female dominated

0.92 [0.79-1.08]

0.98 [0.80-1.19]

1.27 [1.06-1.52]

1.00

Yes

1.07 [0.81-1.43]

0.76 [0.53-1.09]

0.72 [0.52-0.98]

1.00

Yes

0.82 [0.62-1.07]

1.13 [0.80-1.60]

0.97 [0.71-1.32]

1.00

Socially connected (C1)

0.77 [0.59-1.00]

0.88 [0.63-1.22]

1.66 [1.23-2.25]

Self-reliant risk taking (C2)

1.07 [0.75-1.52]

0.46 [0.29-0.71]

0.69 [0.46-1.03]

Isolated (C3)

1.09 [0.82-1.45]

0.37 [0.26-0.52]

0.49 [0.35-0.67]

1.0 1.49 [1.13-1.96] 2.85 [2.16-3.75]

1.00 1.61 [1.19-2.19] 3.05 [2.27-4.10]

EET EET NEET Work industry

Mixed Military service

Emergency service

Masculinity, emotionality and social connectedness Latent class

Health, wellbeing, happiness and resilience Overall health Bad / very bad Moderate Good / very good

Predictor variable

Overall health (good/ very good)

Mental health K10 (high/ very high)

PWI (>70)

K10 1.00

1.00

Moderate

0.43 [0.36-0.52]

0.75 [0.61-0.91]

High

0.30 [0.24-0.38]

0.45 [0.35-0.57]

Very high

0.22 [0.16-0.29]

0.49 [0.34-0.72]

1.00

Possible

0.96 [0.81-1.14]

0.88 [0.71-1.09]

0.98 [0.81-1.19]

Probable

0.83 [0.67-1.04]

0.60 [0.46-0.79]

0.82 [0.63-1.06]

PWI (wellbeing)

1.04 [1.03-1.04]

1.03 [1.02-1.04]

OHQ (happiness)

1.09 [1.06-1.13]

1.37 [1.31-1.42]

1.52 [1.46-1.57]

BRCS (resilience)

1.01 [0.98-1.04]

1.05 [1.01-1.09]

1.07 [1.03-1.11]

1.00

Yes, not stressful

0.99 [0.79-1.23]

1.03 [0.77-1.37]

1.02 [0.80-1.31]

Yes, stressful

1.09 [0.93-1.29]

0.55 [0.46-0.67]

0.69 [0.57-0.83]

Low

Alcohol or other drug misuse None

Wellbeing, happiness and resilience

Major life event None

Note: Statistically significant items (p < 0.05) are highlighted in bold. For more conservative estimates, if the significance level could be rounded to p=0.05 items were not bolded.

As presented in Table 32, results from the five-country sample show that when controlling for the other variables, those aged 16 to 24 years had significantly higher odds of reporting good or very good overall health compared to those aged over 65 years. Males and people in education and training were also more likely to report better overall health. Additionally, those who reported higher personal wellbeing, higher happiness and low psychological distress were more likely to report good to very good overall health.

Results also show that people were more likely to report good mental health (i.e. low/ very low psychological distress) if they were male, reported moderate or good/ very good overall health, or had higher personal wellbeing, happiness or resilience scores. All younger age groups (16 to 64 years) had lower odds of reporting good mental health compared to those aged over 65 years. Additionally, people were less likely to report good mental health if they had not undertaken tertiary education. This was also the case if they identified more closely with the ‘self-reliant risk taking’ or ‘isolated’ latent class, had probable alcohol and/or other substance misuse, or had experienced a life event that was stressful to them.

When controlling for all presented variables, the odds of reporting good personal wellbeing (i.e. a score of over 70) was more likely for those who were 16 to 24 years old compared to those over the age of 65 years. People were also more likely to report good personal wellbeing if they worked in a female-dominated industry. This was also the case if an individual identified as heterosexual, identified more closely with the ‘socially-connected’ latent class, had higher overall health scores (moderate or good/ very good) or reported higher happiness or resilience. Lower personal wellbeing was more likely to be reported for individuals who were male or if they identified more closely with the ‘isolated’ latent class. Additionally, lower personal wellbeing was more likely if a respondent reported probable alcohol and/or other substance misuse, had moderate, high or very high psychological distress, or they had experienced a life event that was stressful to them. Compared to those who were tertiary educated, individuals who had attained secondary education or less had lower odds of reporting personal wellbeing scores over 70.

Table 33. Odds ratio relating to overall health, mental health and wellbeing by demographic, employment, masculinity, emotionality and social connectedness, and health characteristics (male sub-sample; n=1,940) Predictor variable

Overall health (good/ very good) OR [95% CI]

Mental health K10 (low/ very low psychological distress) OR [95% CI]

OR [95% CI]

16 to 24

2.01 [1.24-3.25]

0.19 [0.10-0.33]

2.24 [1.24-4.03]

25 to 44

1.89 [1.26-2.83]

0.42 [0.25-0.70]

0.89 [0.56-1.42]

45 to 64

1.33 [0.96-1.84]

0.58 [0.38-0.90]

0.86 [0.59-1.24]

65+

1.00

Yes

1.02 [0.67-1.57]

0.91 [0.53-1.58]

1.27 [0.79-2.04]

1.00

0.93 [0.70-1.24]

1.60 [1.14-2.24]

1.55 [1.11-2.15]

1.00

Secondary or less

0.87 [0.63-1.20]

0.94 [0.63-1.41]

0.49 [0.34-0.72]

Certificate/ diploma

0.97 [0.72-1.30]

0.90 [0.62-1.32]

0.66 [0.47-0.94]

1.00

1.48 [1.11-1.96]

1.53 [1.06-2.22]

1.39 [0.99-1.97]

1.00

Demographic characteristics Age-bands (years)

PWI (>70)

Rural

Sexual orientation Heterosexual LGBTQIA Education and employment characteristics Education

Tertiary EET Yes NEET

Predictor variable

Overall health (good/ very good)

Mental health K10 (low/ very low psychological distress)

PWI (>70)

Male dominated

0.94 [0.68-1.30]

1.17 [0.76-1.79]

1.06 [0.73-1.54]

Female dominated

0.99 [0.76-1.29]

0.86 [0.61-1.2]

0.94 [0.70-1.28]

1.00

Yes

1.18 [0.85-1.63]

0.76 [0.51-1.14]

0.70 [0.49-1.01]

1.00

Yes

0.74 [0.51-1.07]

1.07 [0.67-1.71]

0.96 [0.63-1.48]

1.00

Socially connected [C1]

0.72 [0.48-1.07]

0.96 [0.58-1.58]

1.53 [0.97-2.42]

Self-reliant risk taking [C2]

1.17 [0.68-2.02]

0.39 [0.20-0.78]

0.63 [0.34-1.20]

Isolated [C3]

1.11 [0.71-1.72]

0.42 [0.24-0.74]

0.59 [0.36-0.96]

Bad / very bad

1.00

Moderate

1.61 [1.03-2.49]

1.29 [0.76-2.17]

Good / very good

2.48 [1.60-3.84]

2.30 [1.40-3.80]

Work industry

Mixed Military service

Emergency service

Masculinity, emotionality and social connectedness Latent class

Health, wellbeing, happiness and resilience Overall health

Predictor variable

Overall health (good/ very good)

Mental health K10 (low/ very low psychological distress)

PWI (>70)

1.00

Moderate

0.42 [0.32-0.56]

0.79 [0.58-1.07]

High

0.34 [0.24-0.49]

0.41 [0.27-0.62]

Very high

0.28 [0.17-0.46]

0.47 [0.24-0.92]

1.00

Possible

0.98 [0.75-1.27]

0.83 [0.60-1.15]

1.10 [0.81-1.49]

Probable

0.81 [0.59-1.11]

0.61 [0.42-0.90]

0.86 [0.59-1.25]

PWI [wellbeing]

1.03 [1.02-1.04]

1.03 [1.02-1.05]

OHQ [happiness]

1.15 [1.09-1.21]

1.33 [1.25-1.42]

1.58 [1.49-1.68]

BRCS [resilience]

1.00 [0.95-1.05]

1.04 [0.98-1.11]

1.09 [1.02-1.16]

1.00

Yes, not stressful

0.66 [0.47-0.93]

1.29 [0.80-2.08]

1.04 [0.71-1.52]

Yes, stressful

1.08 [0.82-1.41]

0.58 [0.42-0.78]

0.62 [0.45-0.85]

K10 Low

Alcohol and/or other substance misuse None

Wellbeing, happiness and resilience

Major life event None

Note: Statistically significant items (p < 0.05) are highlighted in bold. For more conservative estimates, if the significance level could be rounded to p=0.05 items were not bolded.

For age effects on health, mental health and wellbeing, results were similar to the five-country sample. As shown in Table 33, when controlling for all other presented variables, the results for men showed that those aged 16 to 44 years had significantly higher odds of reporting good or very good overall health and high personal wellbeing compared to men over 65 years of age. When considering good mental health, men aged 16 to 64 years were less likely to report low to very low psychological distress compared to those aged over 65 years. Young men aged 16 to 24 were more likely to report personal wellbeing scores over 70, which reflects that this group reported better personal wellbeing than men aged over 65 years, once all other presented variables were controlled for. Male respondents who identified as heterosexual had significantly higher odds of reporting lower psychological distress and higher personal wellbeing scores than men who identified as LGBTQIA. When considering the impact of education, employment and training, male respondents who reported being engaged in these activities had significantly higher odds for reporting good or very good overall health and low or very low psychological distress. Additionally, men who had not completed tertiary education were less likely to report personal wellbeing scores over 70 than those who had completed tertiary education. Men who identified more closely with the ‘self-reliant risk taking’ latent class had significantly lower odds for reporting good mental health. Men who identified more closely with the ‘isolated’ class were less likely to report good mental health and higher personal wellbeing. A clear linear relationship between men’s psychological distress and their overall health can be seen in the results. As the level of psychological distress increased, the odds for reporting good or very good overall health decreased; with those reporting high psychological distress having the lowest odds of good overall health. Again this relationship can be seen when considering the impact of overall health on psychological distress scores. Men who reported moderate or good/ very good overall health scores had significantly higher odds of reporting good mental health compared to men who reported bad/ very bad overall health. Men who reported good/ very good overall health had significantly high odds for reporting high personal wellbeing as opposed to men who reported bad/ very bad overall health. In

addition to this, men who reported high or very high psychological distress scores had significantly lower odds of high personal wellbeing scores compared to men who reported low psychological distress. The odds for men who met the criteria for â&#x20AC;&#x2DC;probableâ&#x20AC;&#x2122; alcohol and/or other substance misuse reporting low to very low psychological distress was significantly lower than those who did not report alcohol and/or other substance misuse. Men who reported high happiness scores (as measured by the OHQ), were significantly more likely to report good overall health, good mental health and high personal wellbeing. Men who reported higher resilience scores were also significantly more likely to report higher personal wellbeing. High personal wellbeing scores also increased the odds of men reporting good overall health and low psychological distress. Men who had experienced a major life event, which was not stressful, were significantly less likely to rate their overall health as good compared to those who did not experience a life event. There were significantly lower odds of men reporting good mental health or high personal wellbeing when a life event was experienced and was perceived as stressful, compared to men who had not experienced a major life event. // PREDICTORS OF HEALTH, MENTAL HEALTH AND WELLBEING SUMMARY In these findings it was clear that for men, younger age groups are the most at risk of experiencing high psychological distress. These findings are consistent with recent research, where young men report high levels of distress and stress (Burns et al 2013; Mission Australia 2015). The current survey found some interesting interactions between overall health, mental health and personal wellbeing of young men. Once all other variables (such as psychological distress), were controlled for in the analysis, the odds of reporting good overall health and/or high personal wellbeing scores were actually higher for young men compared to older age groups. This demonstrates the importance of good mental health for young menâ&#x20AC;&#x2122;s health and personal wellbeing; and the negative impact poor mental health can have on other areas of life. Levels of psychological distress in young men are a concern, and the potential for long-term impact of this distress is significant, particularly related to the development of longer-term mental health problems. The evidence consistently shows that mental health problems most likely commence during adolescence and

continue into late life (Kessler et al 2005; Costello et al 2006; Government of Canada 2006). Understanding the causal factors for psychological distress is key to supporting effective interventions for this vulnerable group. This research suggests that better mental health may have wider positive impacts on young men’s health and personal wellbeing.

In this sample, men reporting ‘probable’ alcohol and/or other substance misuse was associated with lower odds for reporting good mental health. Men have higher rates of alcohol and/or other substance misuse problems and are more likely to engage with hazardous drinking behaviour than women (Statistics Canada 2013; ABS 2011; Ministry of Youth Development 2004). Alcohol and/or other substance misuse as a key health issue has raised a number of interesting findings throughout this Report. For example, the use of alcohol and/or other substances appears to be of concern to men through their identification of them as ‘major health problems’; however, as shown in the next chapter these substances were also consistently used as coping strategies when experiencing a major life event. In later chapters, it is also reported that men in this study had higher levels of alcohol and/or other substance misuse compared to women. Additionally, more men reported using other substances whilst consuming alcohol. Overall, there is clear evidence in the literature of the detrimental impact of alcohol and/or other substance use on health, and support for men is clearly needed in this area. Considering the findings relating to the stigma of alcohol and/or other substance use in later chapters, a multi-faceted approach to interventions may be needed that consider this complex interplay of factors.

Men who identified more closely with the ‘isolated’ latent class are clearly at risk of worse mental health outcomes and experiencing poorer wellbeing. A recent systematic review has highlighted that the risk associated with social isolation is comparable with well-established risk factors for mortality, including those such as physical inactivity, obesity, alcohol and/or other substance misuse and mental health (Holt-Lunstad et al 2015). They suggest that in light of mounting evidence that social isolation is increasing in society, it should be addressed as a major public health concern.

Globally, marginalisation is an important issue, particularly for young people (see Walsh 2016). In our ‘masculinity, emotionality and social connectedness’ chapter we found that the largest effect size group difference for the ‘isolated’ latent class was between those who were unemployed and those who were employed. Education and employment also play a key role in men’s wellbeing and mental health. For men, being less formally educated appears to decrease the likelihood of scoring high on personal wellbeing. For men of working age, less formal education may also be reflective of lower socio-economic status, as research has shown that those with higher income up to a point report higher average wellbeing (Capic et al 2015). Not being in education, employment or training (NEET) was associated with worse overall health and mental health compared to those in employment or training. This requires attention as a recent meta-analysis has reported that unemployment may act as a factor that increases the vulnerability to suicide for people with pre-existing mental health problems and also after adjusting for a prior mental disorder (Milner et al 2014). Their research group has recommended that job loss should be prioritised for prevention intervention as it is a particularly high-risk time for suicide (Milner et al 2012, 2013). Further consideration of how complex stressful life events such as job loss coupled with relationship breakdown may compound risk is also discussed in detail in the following chapter, particularly as men who experienced stressful life events in this sample were significantly more likely to report worse mental health.

Major Life Events // CHAPTER OVERVIEW This chapter reports on major life events, which was measured in Module C of the survey. // MAJOR LIFE EVENTS LIFE EVENTS: Questions about experiencing and coping with major life events were developed by the research group upon request from Movember. Respondents were asked to indicate whether or not they had experienced any of seven specific life events over the past 12 months; including “become a parent for the first time”; “finished high school/ secondary school”; “started university or college”; “started a new job”; “suddenly or unexpectedly becoming unemployed”; “retired”; and “experienced a relationship breakdown with someone important to you”. Those who affirmed they had experienced a major life event were then asked whether they found this experience stressful, which again required a ‘yes’ or ‘no’ response. If the respondent indicated they had found the experience stressful, they were subsequently presented with an additional 16 items and asked to report whether or not they had coped or behaved in that way because of the experience. Items included a range of options such as ‘became aggressive’ or ‘got professional help’. Frequency statistics for major life events from the five-country sample are presented in Figure 13.

Figure 13. Percentage of major life events experienced by the five-country sample within the past 12 months (n=9,034 to n=9,037) Of the seven major life events, starting a new job and relationship breakdown were the most commonly reported; with 23.8% and 22.9% experiencing these events respectively. As shown in Figure 14, when asked to identify experiences of major life events, 29.4% reported experiencing one of the seven identified events and 19.9% reported experiencing two or more events. For those respondents experiencing one major life event, 62.6% identified finding the experience stressful. This figure increased to 82.0% for those experiencing two or more major life events, suggesting a cumulative effect of stress. This cumulative effect of stress (when experiencing more major life events) was consistent for men. Of the men responding to the question â&#x20AC;&#x153;did you find this experience stressfulâ&#x20AC;? (n=1637), 58.4% who experienced one major life event reported that the event was stressful, whereas 76.6% who experienced two or more life events reported these events as stressful.

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Figure 14. Percentage of respondents who experienced a major life event over the past 12 months (including percentage who found the experience stressful) The major life event that was rated as stressful by the largest proportion of respondents was a relationship breakdown; with 90.7% of those who experienced this situation (n=2,073) reporting that it was stressful. Becoming unemployed followed, with 85.6% of those who experienced this event (n=793) reporting it as stressful. Retirement was the least endorsed stressful event; however, almost half (45.3%) of all people that recently retired (n=598) reported that they found the experience stressful. This pattern was consistent across gender.

Table 34 provides the frequency statistics of suicidal ideation (as rated by the PSFS) by menâ&#x20AC;&#x2122;s experience of at least one of the seven major life events in the last 12 months and their perceptions of stress if they experienced such an event.

Table 34. Frequency statistics of suicidal ideation (PSFS) reported by men in relation to major life event over the past 12 months and perceived experience of this event (male sub-sample) Suicidal Ideation

No Suicidal Ideation

Total

427

1497

1924

22.2

77.8

100.0

440

529

16.8

83.2

100.0

471

563

1034

45.6

54.4

100.0

987

2500

3487

28.3

71.7

100.0

Major life event experience No major life event experienced

Major life event experienced (rated as not stressful)

Major life event experienced (rated as stressful)

Total male sample

As shown in Table 34, 45.6% of the men surveyed who had experienced at least one of these major life events in the last 12 months (which they found stressful) reported suicidal ideation. Whereas, 16.8% of men who did experience a major life event but did not find it stressful reported suicidal ideation. Of the men who did not experience a major life event over the past year, 22% reported suicidal ideation. Table 35 presents a breakdown of the frequency statistics of suicidal ideation (as rated by the PSFS) by experience and type of major life event for the male subsample. The data presents the proportion of men who reported suicidal ideation and experienced a major life event compared to those who reported suicidal ideation but had not experienced a major life event. Of the 693 males who experienced a relationship breakdown in the last 12 months and completed the PSFS, 48.9% reported suicidal ideation. Of the 316 males who suddenly or unexpectedly became unemployed in the last 12 months and completed the PSFS, 47.5% reported suicidal ideation. Of the 172 males who finished high school/ secondary school in the last 12 months and completed the PSFS, 40.1% reported suicidal ideation. Of the 248 males who started university or college in the last 12 months and completed the PSFS, 37.5% reported suicidal ideation. Of the 716 males who started a new job in the last 12 months and completed the PSFS, 33.0% of reported suicidal ideation. Of the 262 males who retired in the last 12 months and completed the PSFS, 21.0% reported suicidal ideation. There were only 56 men who became a parent for the first time in the last 12 months and completed the PSFS, thus, cell sizes are too small for reporting suicidal ideation for this group.

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Table 35. Frequency statistics of suicidal ideation (PSFS) reported by men after a major life event or no major life event (male sub-sample) Over the past 12 monthsâ&#x20AC;Ś

DID NOT experience specified

DID experience specified major

major life event

life event

Suicidal

No Suicidal

Suicidal

No Suicidal

Ideation

974

2456

3430

28.4

71.6

100.0

918

2397

3315

103

172

27.7

72.3

100.0

40.1

59.9

100.0

893

2345

3238

155

248

27.6

72.4

100.0

37.5

62.5

100.0

751

2021

2772

236

480

716

27.1

72.9

100.0

33.0

67.0

100.0

836

2334

3170

150

166

316

26.4

73.6

100.0

47.5

52.5

100.0

931

2293

3224

207

262

28.9

71.1

100.0

21.0

79.0

100.0

648

2147

2795

339

354

693

23.2

76.8

100.0

48.9

51.1

100.0

Total

Major life event Became a parent for the first time

Finished high school/ secondary school

Started university/ college

Started a new job

Suddenly or unexpectedly become unemployed

Retired

Relationship breakdown

Note: Cell marked with â&#x20AC;&#x201C; indicates at least one cell count under n=30 for the specific life event.

Table 36 shows the percentage of all respondents and male respondents reporting they engaged in each of the listed coping or behavioural methods (if they found the life event(s) stressful) as a function of how many major life events they experienced.

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Table 36. Frequency statistics for methods of coping with “stressful” major life events based on number of life events experienced (five-country sample and male sub-sample) Five-country sample Number of “stressful” major life events

Male sample only

One

Two or more

One

Two or more

1661

1474

582

488

Became aggressive

13.0

20.1

17.4

24.8

Bossy/ inflexible/ angry

31.9

39.0

30.0

36.3

Eat more/ less

61.6

71.2

52.6

61.5

n Coping response

Spiritual activity

29.8

32.4

29.1

30.9

Got professional help

31.0

38.5

29.3

42.4

Increased tobacco/ alcohol/ drugs

25.5

35.6

28.6

41.4

Isolated self

50.3

65.0

53.0

63.7

Overdo activities

24.2

30.9

22.1

24.2

Sleep too much/ too little

68.6

77.6

67.0

73.8

Spend time with friends/ loved ones

30.4

32.6

27.8

28.7

Work less/ more

32.3

41.9

31.7

42.4

Take more risks

18.1

29.7

21.9

33.8

Talk to someone about feelings

68.9

70.2

63.4

67.2

Talk to someone for advice

53.5

61.3

48.4

58.4

Do nothing

24.3

28.0

26.6

29.8

Other

14.1

16.2

14.4

15.3

The number of stressful life events men experienced resulted in some variation in coping responses. Men who experienced two or more stressful major life events reported engaging in all types of coping responses more frequently than those who experienced only one stressful major life event. The greatest difference was observed for both adaptive coping strategies (including getting professional help and talking to someone for advice), and also maladaptive strategies (including increasing tobacco, alcohol or drugs, taking more risks, and isolating themselves). Table 37 presents frequency statistics for experience of major life events, response to experiencing stress, and subsequent coping if the experience was “stressful’ via gender, age and country. Differences in age emerged with only 6.7% of people aged 65 years and over reporting two or more events in comparison to 42.0% of 16 to 24 year olds. This is not surprising given that some events (eg. becoming a parent for the first time, finishing high school/ secondary school) are more likely to be experienced by younger people. The findings also demonstrated differences across the age groups with regard to how stressful the events were perceived. Though 75.7% of 25 to 44 year olds reported finding major life events stressful, this figure was reduced to only 53.0% of respondents aged 65 years and older.

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Table 37. Frequency and experience of major life events, and response to experiencing stress (comparative sub-samples) Gender Males

Age-bands

Females

Number of major life events experienced in the past 12 months: N 3,628 5,401 None (%) 55.0 47.8 (%) 27.5 30.7 One (%) 17.6 21.5 Two or more

Experienced at least one major life eventâ&#x20AC;Ś

Sample

16 to 24 years

25 to 44 years

45 to 64 years

65+

Australian sample

Other countries sample

2,175 25.2 32.8 42.0

2,081 44.3 36.6 19.1

3,096 62.7 25.2 12.1

1,677 69.7 23.6 6.7

2,850 51.6 30.8 17.5

6,179 50.3 28.7 21.0

1,637

2,821

1,629

1,159

511

1,377

3,076

(% Yes)

65.6

73.3

71.3

75.7

71.8

53.0

72.0

69.8

(% No)

34.4

26.7

28.7

24.3

28.2

47.0

28.0

30.2

Response based on stressful experience: N (% Yes) Became aggressive (% Yes) Bossy/ inflexible/ angry Eat more/ less (% Yes) (% Yes) Spiritual activity (% Yes) Got professional help (% Yes) Increased tobacco/ alcohol/ drugs Isolated self (% Yes) (% Yes) Overdo activities Sleep too much/ too little (% Yes) (% Yes) Spend time with friends/loved ones (% Yes) Work less/ more (% Yes) Take more risks Talk to someone about feelings (% Yes) Talk to someone for advice (% Yes) (% Yes) Do nothing (% Yes) Other

1,069 20.8 33.0 56.6 29.8 35.4 34.5 57.9 23.0 70.0 28.2 36.6 27.4 65.2 53.0 28.0 14.8

2,062 14.1 36.5 71.0 31.7 34.1 28.0 56.9 29.6 74.2 33.2 36.9 21.5 71.7 59.3 25.1 15.2

1,159 19.3 40.7 73.4 25.5 28.6 28.9 63.7 33.2 79.3 35.9 39.3 32.6 63.7 56.8 31.9 15.0

876 21.0 41.7 68.8 32.4 39.0 37.7 55.9 29.0 72.0 30.4 44.1 23.3 77.6 66.4 21.2 12.4

828 10.1 27.3 59.3 36.5 39.2 29.6 54.9 20.7 69.0 26.1 32.5 15.1 69.7 53.0 22.9 17.4

269 7.8 16.0 45.9 33.6 31.3 13.4 40.9 17.5 58.4 32.2 15.4 11.2 67.4 41.4 26.8 16.8

991 17.6 36.1 66.2 27.1 45.2 31.7 60.2 28.3 71.5 27.4 40.6 22.8 71.6 62.3 24.6 13.5

2,141 15.8 34.9 66.0 32.9 29.7 29.5 55.9 27.0 73.4 33.4 35.0 23.9 68.5 54.8 26.8 15.8

Did you find this experience stressful?

Note: Bolded font indicates the five most endorsed coping stratgeies.

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When considering the ways in which individuals coped with major life events they perceived as stressful, there were five areas consistently identified by all respondents in the five-country sample irrespective of number of life events including: sleep too much/ little (72.8% of all respondents); talk to someone about feelings (69.5%); eat more/ less (66.1%); isolate self (57.2%); and, talk to someone for advice (57.2%).

The coping strategies ‘sleep too much/ little’ and ‘talk to someone about feelings’ remained the two highest reported coping strategies for both men and women. However, men were more likely to report that they isolate themselves whereas women were more likely to eat more or less. There are also some notable differences amongst age groups and identified coping strategies. For respondents aged 25 years and over, the most utilised coping strategy is ‘talk to someone about feelings’; with respondents aged 25 to 44 years and 45 to 64 years more likely to seek professional help compared to all other age groups. Young people endorsed seeking professional help the least. Table 38 presents a breakdown of responses to stress after experiencing a major life event that was perceived as stressful for male respondents only.

106

268

103

634

24.3

22.3

23.4

25.4

16.5

22.9

Relationship breakdown

465

Retired

175

Suddenly become unemployed

107

Started a new job

Started university/ college

Became a parent

Finished high school

Table 38. Male responses based on stressful life event (male sub-sample)

Coping strategy (% Yes) Became aggressive Bossy/ inflexible/ angry

43.2

25.9

37.1

35.6

36.2

23.3

34.2

Eat more/ less

56.8

61.3

65.7

59.7

50.0

56.5

Spiritual activity

27.0

17.9

32.0

28.7

30.2

29.4

32.2

Got professional help

37.8

39.3

44.0

35.1

44.0

43.7

36.8

Increased tobacco/ alcohol/ drugs

35.1

38.0

41.7

37.3

42.2

24.0

38.6

Isolated self

45.9

62.6

57.7

56.8

65.3

49.0

63.8

Overdo activities

29.7

16.0

22.3

23.7

20.9

24.5

26.0

Sleep too much/ too little

67.6

70.8

74.9

72.2

75.0

63.7

70.2

Spend time with friends/loved ones

18.9

29.9

30.9

29.2

25.0

36.3

30.1

Work less/ more

35.1

36.8

41.7

41.8

28.4

37.7

Take more risks

18.9

41.1

30.3

29.1

19.6

32.8

Talk to someone about feelings

56.8

60.4

70.9

69.9

64.9

61.2

66.4

Talk to someone for advice

56.8

56.6

60.0

57.8

54.9

44.1

54.7

Do nothing

16.2

27.4

23.4

25.2

34.5

27.7

29.3

Other

8.1

9.4

15.0

11.9

19.9

14.0

17.3

Note: Bolded font indicates the five most endorsed coping strategies.

There were some notable variations in coping behaviours based on the type of major life event experienced. For male respondents, retirees endorsed becoming aggressive, talking to someone for advice and using substances (tobacco, alcohol and/or other substances) less often as a reaction to stress compared to the other major life events, but more frequently reported that they spent time with friends/ loved ones. Along with new fathers, retirees also endorsed isolating themselves less frequently than other major life event situations. However, new fathers less frequently endorsed spending time with friends or loved ones and talking to someone about their feelings. New fathers also endorsed becoming bossy/ inflexible and angry compared to the other major life events, but seemed to less frequently endorse taking more risks. Males who had finished high school were less likely to endorse engaging in spiritual activities and overdoing activities, but they more frequently endorsed risk-taking behaviour compared to other groups. A third (34.5%)

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who reported suddenly or unexpectedly becoming unemployed in the last 12 months endorsed doing nothing; which was at the highest rate across all presented life events. Additionally, men with this experience of unemployment had the highest rates of endorsing becoming aggressive, increased drug and alcohol use, isolation and sleeping too much/too little. Importantly, almost half (44%) reported getting professional help to cope with the stressful life event; which again was the highest endorsement rate by life event alongside men who stated university or college. // MAJOR LIFE EVENTS SUMMARY The findings indicate that compared to women, men often reported engaging in coping strategies that have a detrimental impact on health and wellbeing including alcohol and/or other substance use, becoming aggressive, taking more risks and isolating themselves. This is consistent with the literature whereby men are at higher risk of alcohol and/or other substance misuse and are more likely to engage in risk taking behaviours (ABS 2011; Deverill & King 2009; Thomas et al 2007; Gravel & BĂŠland 2005; Ministry of Youth Development 2004). Courtenay (2000) proposed that men are more likely than women to adopt beliefs and consequently behaviours that increase their risk of ill-health, and are less likely to engage in positive healthrelated behaviours. This finding is also interesting in the context of earlier findings, where alcohol abuse or addiction was the highest rated major mental health problem for both younger and older men. Taken together, these findings suggest that alcohol is perceived both as a major mental health issue and as a coping mechanism. This is concerning given the role alcohol plays in the physical health outcomes for men; with the long-term effects of excessive drinking increasing the risk of heart disease and cancer (Ronksley et al 2011; Bagnardi et al 2013). These results present a significant challenge for health promotion and prevention initiatives in shaping the development of more effective coping strategies.

Interestingly, age has a considerable impact on the results related to levels of stress associated with major life events and use of coping strategies. Older men appear to find major life events less stressful, even when experiencing more than one event. This may be related to the stage of life of the individual but may also be as a result of more developed coping strategies for managing stress over time. For example, retirees who still found retirement â&#x20AC;&#x2DC;stressfulâ&#x20AC;&#x2122; were less likely to endorse becoming aggressive, using substances or isolating themselves, but more frequently reported spending time with friends/ loved ones. This may be a result of increased leisure time that comes with retirement. However, may also be due to developing better

108

coping strategies. Reynolds & Turner (2008) suggest that individuals may cope differently or perceive a range of stress levels dependent on their previous experience of either success or failure in coping with stressors. In essence, those who have successfully worked through prior stressors in life are more likely to develop more effective coping skills.

Relationship breakdown was rated consistently as the most stressful life event for both men and women, followed by suddenly or unexpectedly becoming unemployed. These were found to be key risk factors for elevated suicidal ideation. A recent systematic review of gender, suicide and relationship breakdown tentatively found support that men were at greater risk of suicide compared to women following relationship problems, divorce or separation (Evans et al 2014). Another recent meta-analysis reported that unemployment was associated with greater risk of suicide even after adjusting for prior mental disorders (Milner et al 2014). Support for men during and after these major life events is clearly needed. Community and health practitioner awareness of these life stages as a major stressor is vital, particularly if this stress is compounded for men if they experience more than one major life event. Qualitative research, focused on men who have experienced a suicide attempt, has reported that a cumulative stress build up can occur when faced with compounding stressors, including those that are ordinarily manageable (Player et al 2015). This can degrade mood and functioning further and increases the risk of acute, actively suicidal behaviour. Additionally, how the major life event was perceived by men also played a significant part in their reported levels of suicidal ideation. Almost half of all men reported suicidal ideation when they had experienced at least one of the seven presented major life in the past year and reported that it was â&#x20AC;&#x153;stressfulâ&#x20AC;?. Whereas, men who experienced a major life event, but did not find it stressful, had lower levels of suicidal ideation than men who did not experience any of these life event in the past year. Further research will consider how resilience, coping and other protective factors such as social connectedness play a part in these findings. It is important to acknowledge that only seven life events were considered in this survey, and other life events also warrant further research. There is a large body of research regarding individuals and their attitudes towards help-seeking, and in recent years this has moved to focus on men and their perceived reluctance to seek help. Interestingly, in these results, men and women show similar levels of reporting seeking professional help as a way of coping with

109

major life events. Differences in seeking professional help appear to relate more to age than gender with young people being least likely to seek professional help for a major life event than all other age groups. This could be related to perspectives about the type of major life event and the potential for stress. Young people may perceive these identified major life events as part of their life experience; and as a result, normalise stressful experiences rather than monitor responses and seek help if high levels of stress are present. These results could also be related to how young people choose to seek support for major life events. Smith & Skrbis (2014) found that positive peer relationships have the most influence on young people and their ability to manage life events. This is consistent with the findings whereby â&#x20AC;&#x2DC;talking to someone about feelingsâ&#x20AC;&#x2122; was the second highest rated coping strategy, which can be viewed as a very positive method of managing stress. There is also increasing research regarding how young people access support and the important role of online interventions (Burns et al 2014; Clarke et al 2014). In a review of literature regarding utilisation of technology to deliver mental health services for young people, results indicated a preference for use of online interventions and as a result young people were more likely to engage with services (Boydell et al 2014). Significant work has been conducted in Australia to develop services with a youth perspective in mind including headspace National Youth Mental Health Foundation and ReachOut.com Australia. Young people may have a preference for seeking support via these types of interventions rather than seeking professional help through traditional health service methods.

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Health Behaviours and Perceptions // CHAPTER OVERVIEW This chapter reports on three main areas measured in Module G of the survey, including:        

Physical activity; Sleep behaviours; Eating behaviours; Weight, weight perceptions and dieting behaviours; Body image concern; Weight lifting and steroid use; Alcohol and/or other substance mis/use; and Gambling.

// PHYSICAL ACTIVITY The International Physical Activity Questionnaire short form (IPAQ; Craig et al 2003) was used to determine a respondent’s physical activity level and asks about specific types of activity undertaken in three domains (walking, moderate-intensity and vigorous-intensity activities). Responses for these items determined level of activity, which are represented in Table 39 (Category 1, 2 and 3). Category 1 represents very low levels of activity, classified as ‘inactive’. Category 2, classified as ‘sufficiently active’ or ‘minimally active’, includes individuals with more than the minimum level of activity recommended for adults in current public health recommendations, but is not enough for ‘total physical activity’ when all domains are considered. Category 3, classified as ‘HEPA’ (health-enhancing physical activity), is the most active category. This level includes people who exceed the minimum public health physical activity recommendations, and are considered to be accumulating enough activity for a healthy lifestyle.

Table 39 shows the percentage of respondents who were classified into each of the three IPAQ categories. Table 39. Frequency statistics for measures of physical activity (five-country sample) IPAQ (physical activity) Inactive (CATEGORY 1) Minimally active (CATEGORY 2) HEPA active (CATEGORY 3)

5,570

% % %

24.0 34.3 41.7

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The results for physical activity show that less than a quarter of respondents reported being inactive and 41.7% reporting healthy levels of activity. Table

shows

physical

activity

results

sub-sample

characteristics,

demonstrating some variations between age and gender. A higher proportion of men report healthy levels of activity with 44.9% as opposed to 39.5% of women. When considering age, young people 16 to 24 years report the highest levels of activity with an approximate 5% higher rate than the other age-bands. Table 40. Frequency statistics for physical activity (comparative sub-samples) Gender

IPAQ Inactive

Age-bands (years)

Sample

Males

Females

16 to 24

25 to 44

45 to 64

65+

Australia

All other countries

2,190

3,380

1,193

1,274

2,039

1,064

1,852

3,718

22.6

24.9

19.4

24.4

25.6

25.8

23.3

24.4

32.4

35.5

35.3

34.9

33.8

33.4

35.7

33.6

44.9

39.5

45.3

40.7

40.5

40.9

42.0

(CATERGORY 1)

Minimally active (CATERGORY 2)

HEPA active (CATERGORY 3)

Table 41 presents Pearson correlations between physical activity and health and wellbeing measures and masculine conformity/ social connectedness latent classes for the male sample. For men, more physical activity (HEPA activity) was associated with lower body mass index (BMI) and lower psychological distress and suicidality scores. More physical activity was also associated with better sleep quality, and higher wellbeing, happiness and resilience scores. Those who identified with the ‘socially connected’ and the ‘self-reliant risk taking’ latent classes were more physically active, whilst those who identified more with the ‘isolated’ latent class were less physically active. The strongest correlations for physical activity were with wellbeing and happiness.

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Table 41. Pearson correlations of menâ&#x20AC;&#x2122;s physical activity with health and wellbeing measures and masculine conformity/ social connectedness latent classes (male sub-sample)

Physical activity

Body mass index (BMI)

Sleep quality

Psychological distress (K10)

Suicidal ideation (PSFS)

Wellbeing (PWI)

Happiness (OHQ)

Resilience (BRCS)

Socially connected (C1)

Selfreliant risk taking (C2)

Isolated (C3)

2,115

2,176

2,190

2,162

2,190

Pearson correlation

-0.154

0.132

-0.196

-0.151

0.224

0.223

0.187

0.043

0.087

-0.162

Note: Items that were statistically significant (p<0.05) are presented in bold.

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// PHYSICAL ACTIVITY SUMMARY There is clear evidence to support the relationship between physical activity and quality of life (Henchoz et al 2014; Powell et al 2011; Bize et al 2007). Evidence has also shown that increased physical activity in younger men can prevent substance misuse dependence, depression and abnormal weight (Henchoz et al 2014). The results from this survey support the research showing a relationship between higher levels of activity and improved health outcomes such as lower psychological distress and higher personal wellbeing. However, in spite of the knowledge regarding the positive impact of exercise, less than 50% (41.7%) of the sample reported the HEPA level of activity. In addition to this, levels of inactivity increase with age with more than a quarter of over 65 year olds reporting as inactive. Younger people report the highest levels of activity, with 45.3% of 16 to 24 year olds reporting being HEPA active. Exercise was also highlighted as a major health concern for men within the ‘perceptions of men’s health and wellbeing’ chapter suggesting that there is some awareness of the influence of exercise on overall health. However, these results suggest that although people are engaged in exercise, levels of activity could be increased, and support to sustain this activity with age is required.

// SLEEP BEHAVIOURS Three items relating to sleep were measured, which included average number of hours of sleep per night over the past week (from 1=‘less than 6 hours per night’ to 6=‘10 or more hours per night’), sleep quality (adapted from Buysse et al 1989) and feeling refreshed upon waking (Bayer & Pathy 1985). To assess sleep quality, respondents were asked to rate the quality of their sleep on a five-point Likert scale ranging from ‘very bad’ to ‘very good’. Respondents were then asked to rate how refreshed they felt, on average over the past week, on a five-point Likert scale ranging from ‘very tired’ to ‘very awake’. An additional item gauging late night Internet use was also included, which was derived from the Young and Well National Surveys (Burns et al 2013). For this question, respondents were asked whether they use the Internet after 11pm at night (‘yes’ or ‘no’). Table 42 displays frequency statistics for measures of sleep in the five-country sample, and Table 43 shows this for each sub-sample.

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Table 42. Frequency statistics for measures of sleep (five-country sample) Average hours of sleep per night Less than 6 hours 6 hours 7 hours 8 hours 9 hours 10 or more hours

n % % % % % %

5,513 13.5 21.1 33.4 22.3 6.7 3.1

Very bad Bad Average Good Very good

n % % % % %

5,515 3.4 19.9 42.0 27.0 7.6

Very tired Tired Average Awake Very awake

n % % % % %

5,515 9.8 33.2 31.7 21.3 4.0

Yes No

n % %

5,513 44.7 55.3

Of respondents who do use Internet after 11pm… How often do you use the Internet after 11pm at night? Less than once a week 1-3 nights a week 4-5 nights a week 6-7 nights a week

n % % % %

2,467 3.7 35.5 27.0 33.7

Quality of sleep

How refreshed did you feel?

Do you use the Internet after 11pm at night?

The results for sleep highlight that most respondents reported having an average of seven hours of sleep per night. Quality and outcome of sleep is most commonly reported as ‘average’; with only 34.6% of respondents reporting good or very good quality of sleep and only 25.3% reporting feeling awake or very awake after sleep. Nearly half of respondents (44.7%) identified using the Internet after 11pm at night. Of those who indicated use after 11pm at night, the most commonly reported frequency of use was one to three nights a week (35.5%) followed by six to seven nights a week (33.7%).

115

Table 43. Frequency statistics for measures of sleep (comparative sub-samples) Gender

Sample

Female

16 to 24

25 to 44

45 to 64

65+

Australia

2,175

3,338

1,184

1,266

2,015

1,048

1,836

Other countries 3,677

Less than 6 hours

13.8

13.3

11.8

12.7

16.0

11.5

14.5

6 hours

22.6

20.2

18.8

20.1

22.4

20.7

21.3

7 hours

33.9

33.0

32.2

34.0

33.5

33.6

33.7

33.2

8 hours

20.9

23.2

22.6

24.2

20.2

23.6

21.6

9 hours

5.9

7.2

8.8

6.3

5.3

7.3

7.5

6.2

10 or more hours

2.9

3.2

5.7

2.5

2.6

1.7

2.9

3.2

2,176

3,339

1,184

1,266

2,017

1,048

1,836

3,679

Average hours of sleep per night

Quality of sleep Very bad

3.4

3.5

4.5

4.3

3.5

1.1

3.5

3.4

Bad

19.2

20.4

19.6

20.1

22.0

15.9

17.6

21.0

Average

40.8

42.8

42.7

41.8

40.9

42.2

42.0

Good

29.5

25.4

27.1

25.7

31.4

29.1

26.0

Very good

7.1

7.9

5.8

7.0

10.6

7.5

7.6

2,176

3,339

1,184

1,266

2,017

1,048

1,836

3,679

6.8

11.8

17.2

12.2

8.0

2.1

10.5

9.5

How refreshed did you feel? Very tired

116

Age-bands (years)

Male

Tired

31.0

34.6

40.7

38.6

30.4

23.4

31.2

34.2

Average

32.1

31.5

28.0

32.4

32.6

33.5

31.7

Awake

25.0

18.8

12.8

14.6

24.9

31.9

22.6

20.6

Very awake

5.1

3.3

1.4

2.1

4.1

9.2

4.0

Gender

Age-bands (years)

Sample

Male

Female

16 to 24

25 to 44

45 to 64

65+

Australia

2,175

3,338

1,184

1,266

2,017

1,046

1,836

Other countries 3,677

Yes

47.2

43.1

69.9

44.9

35.8

33.2

37.8

48.2

Do you use the Internet after 11pm at night? No

52.8

56.9

30.1

55.1

64.2

66.8

62.2

51.8

Of respondents who do use the Internet after 11pmâ&#x20AC;Ś How often do you use the Internet after 11pm at night?

1,027

1,440

828

568

724

347

695

1,772

Less than once a week

2.3

4.7

2.5

2.1

5.9

4.6

4.3

3.5

1-3 nights a week

32.7

37.6

32.5

38.4

36.5

36.3

35.7

35.5

4-5 nights a week

27.6

26.7

27.1

26.2

25.6

31.4

29.2

26.2

6-7 nights a week

37.4

31.0

37.9

33.3

32.0

27.7

30.8

34.8

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Results related to gender regarding amount and quality of sleep were similar, with men and women reporting comparable results. The most endorsed average hours of sleep per night remains at seven hours across all age groups. Again, across all agebands, respondents reported their quality of sleep as ‘average’; with levels of good or very good sleep increasing slightly for those over 65 years of age. Men reported higher levels of Internet use after 11pm at night with 47.2%, as opposed to 43.1% for women. Men also reported higher levels of frequency with six to seven nights a week being the most commonly reported frequency (37.4%). Females most frequently reported ‘one to three nights per week’ of late night Internet use (37.6%). Young people also report the highest level of Internet use after 11pm. Use of the Internet decreased as age increased. Young people reported the highest frequency of use, with 37.9% using the Internet after 11pm, six to seven nights a week. Table 44 presents Pearson correlations between quality of sleep and late night Internet use with health and wellbeing measures and masculine conformity/ social connectedness latent classes for the male sample. For men, better sleep quality was associated with lower BMIs, and lower psychological distress and suicidality scores. Better sleep quality was also associated with more physical activity, higher wellbeing, happiness and resilience scores. Those who identified with the ‘socially connected’ latent class reported better sleep quality, whilst those who identified more with the ‘isolated’ latent class reported worse sleep quality. The strongest correlation for men’s sleep quality was with psychological distress, followed by wellbeing and happiness; which showed moderate strength correlations. For men, late night Internet use (and higher frequency of late night Internet use) was associated with poorer sleep quality and mental health, as well as lower happiness, resilience and personal wellbeing. Men who identified more with the ‘socially connected’ latent class reported less late night Internet use; whereas those who identified more closely with the ‘self-reliant risk taking’ latent class reported more late night Internet use. Higher frequency of late night Internet use was associated with closer identification with the ‘isolated’ latent class. All late night Internet use and frequency of late night Internet use correlations had very low to low correlation strength.

118

Table 44. Pearson correlations of menâ&#x20AC;&#x2122;s sleep quality and late night Internet use with health and wellbeing measures and masculine conformity/ social connectedness latent classes (male sub-sample) Body mass index (BMI)

Sleep quality

Psychological distress (K10)

Suicidal ideation (PSFS)

Wellbeing (PWI)

Happiness (OHQ)

Resilience (BRCS)

Socially connected (C1)

Self-reliant risk taking (C2)

Isolated (C3)

2,144

2,176

2,148

2,176

Pearson correlation

0.10

-0.44

-0.25

0.41

0.42

0.29

0.11

<0.01

-0.11

Do you use the Internet after 11pm at night?

2,144

2,175

2,147

2,175

Pearson correlation

0.02

-0.13

0.26

0.18

-0.23

-0.10

-0.04

0.08

0.02

How often do you use the Internet after 11pm at night?

1,018

1,027

1,012

1,027

Pearson correlation

-0.02

-0.08

0.13

0.08

-0.14

-0.13

-0.03

0.00

0.10

Sleep quality

Note: Items that were statistically significant (p<0.05) are presented in bold.

119

// SLEEP BEHAVIOURS SUMMARY Evidence suggests there are considerable health risks both for those who sleep for too long and for those who do not have enough sleep (Gallicchio & Kalesan 2009). These results indicate the average number of hours sleep per night is seven, which is consistent with recommendations for the required number of hours sleep (Hirshkowitz 2015). However, despite reporting appropriate levels of sleep, a high percentage of respondents identified their quality of sleep as poor and reported feeling tired on waking. Gender appears to show limited variation in the results with men and women reporting similar levels and quality of sleep. However, when considering the relationship between sleep quality and health in the male sample, higher quality sleep was associated with lower BMIs and lower psychological distress and suicidality. Better sleep was also related to more physical activity and higher personal wellbeing, happiness and resilience scores in men. These results indicate the clear benefit of sleep quality on both physical and mental health.

Late night use of the Internet and technology has been shown to have a negative impact on sleep (Gamble et al 2014; Lin & Shur-Fen Gau 2013). In addition to this, the use of technology including the Internet late in the evening is also negatively associated with morningness (Fossum 2013). The results from this survey show that nearly half of respondents reported using the Internet late at night, and of those respondents over a third engaged in late night Internet use six to seven nights a week. Higher frequency of use is associated with younger age, with those aged 16 to 24 years reporting the highest levels of usage six to seven nights a week. Lin & Shur-Fen Gau (2013) found that late night Internet use by young people increases alertness in the evening and subsequently affects sleep. The results for Internet use may go some way to explain the quality of sleep individuals reported experiencing, despite on average having the recommended amount of sleep.

When considering gender, men were more likely than women to engage in frequent late night Internet use. In the male sample, late night Internet use was associated with poor sleep quality, and low mental health and personal wellbeing, happiness and resilience scores. These findings support the evidence regarding the impact of late night Internet use on health showing worse physical and mental health outcomes (Gamble et al 2014). Those who identified more closely with the ‘socially connected’ latent class reported less late night Internet use, whereas those identifying within the ‘self-reliant risk taking’ latent class reported higher levels of

120

use. These results highlight the need to increase awareness of the impact of late night Internet use and the potential impact on sleep and health. There may be a lack of awareness regarding the potential for late night Internet use to impact on sleep and subsequently influence physical and mental health outcomes. The importance of maintaining appropriate sleep levels should also be reinforced to ensure individuals are aware of the age specific requirements (Hirshkowitz 2015).

// EATING BEHAVIOURS Frequency of healthy or unhealthy eating behaviours by food group was measured using items from the Young and Well National Surveys (Burns et al 2013). Examples of food groups were provided with descriptions, such as ‘fast food (eg. fish and chips, hot chips, pizza, hot dogs, meat pies)’. Respondents rated their weekly frequency of consumption of each of the food groups on a six-point Likert scale (‘Not at all’ to ‘Several times a day’). Current dieting status (see Blashill 2014) was determined using three response options (‘no’, ‘yes to lose weight’ or ‘yes to gain weight’). Table 45 displays frequency statistics for diet choices for the fivecountry sample.

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Table 45. Frequency statistics for measures of diet choices (five-country sample) Energy dense snacks

5,519

Not at all

14.5

Once a week A few times a week Most days Once a day Several times a day

% % % % %

22.0 37.5 14.6 7.0 4.5

5,519

Fast food Not at all

34.1

Once a week A few times a week Most days Once a day Several times a day Fruit and vegetables

% % % % % N

42.6 18.5 3.3 1.1 <1.0 5,518

Not at all Once a week A few times a week Most days Once a day Several times a day

% % % % % %

1.2 3.1 13.5 22.4 15.0 44.8

Not at all Once a week A few times a week Most days Once a day Several times a day

N % % % % % %

5,516 10.3 8.0 32.0 27.7 16.5 5.6

Not at all Once a week A few times a week Most days Once a day

N % % % %

5,517 28.1 35.0 29.3 4.8 2.1

Several times a day

<1.0

Meat

Fish

The results for eating behaviours indicate that the most often consumed food items were fruit and vegetables with 44.8% of respondents reporting consumption several times a day of both food items. Fast food was the item consumed least with 34.1% reporting no consumption of this food item.

Table 46 displays frequency statistics for diet choices by gender, age-bands and country sample.

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Table 46. Frequency statistics for measures of dieting choices (comparative sub-samples) Gender

Energy dense snacks

Age-bands (years)

Sample

Males

Females

16 to 24

25 to 44

45 to 64

65+

Australia

Other countries

2,179

3,340

1,183

1,266

2,019

1,051

1,838

3,681

Not at all

16.7

13.0

7.2

10.6

17.0

22.4

14.1

14.6

Once a week

22.1

21.9

16.1

18.9

25.1

26.3

23.3

21.3

A few times a week

37.6

37.4

35.4

43.1

38.6

30.7

37.0

37.7

Most days

14.4

14.7

22.4

15.6

11.4

10.8

14.6

Once a day

6.1

7.6

10.2

6.2

5.1

7.9

6.6

7.1

Several times a day

3.1

5.5

8.7

5.6

2.8

2.0

4.2

4.7

Fast food

2,179

3,340

1,183

1,266

2,019

1,051

1,838

3,681

Not at all

30.5

36.4

19.5

24.6

41.2

48.1

35.1

33.6

Once a week

42.5

42.7

41.9

45.8

42.5

39.8

44.1

41.9

A few times a week

21.8

16.4

27.0

25.1

14.0

9.7

16.8

19.4

Most days

3.8

2.9

8.3

3.0

1.5

1.3

2.7

3.5

Once a day

1.1

1.9

1.0

<1.0

1.0

<1.0

1.2

Several times a day

<1.0

1.4

<1.0

Fruit and vegetables

2,179

3,339

1,183

1,266

2,018

1,051

1,837

3,681

Not at all

1.5

1.0

1.9

1.5

<1.0

1.4

Once a week

4.5

2.2

4.9

2.9

2.7

2.0

3.6

A few times a week

15.8

12.1

16.5

14.0

12.1

12.5

11.7

14.5

Most days

26.6

19.7

18.9

20.4

24.7

24.4

21.7

22.8

Once a day

16.8

13.7

18.3

13.5

12.7

17.3

15.8

14.5

Several times a day

34.8

51.4

39.5

47.7

47.1

43.0

48.1

43.2

123

Gender

Meat

Age-bands (years)

Sample

Males

Females

16 to 24

25 to 44

45 to 64

65+

Australia

Other countries

2,179

3,337

1,183

1,266

2,017

1,050

1,836

3,680

Not at all

6.6

12.8

13.4

12.8

9.0

6.4

10.8

10.1

Once a week

7.0

8.6

7.1

6.1

8.8

9.6

6.8

8.6

A few times a week

31.4

32.3

20.7

29.1

36.4

39.4

31.9

32.0

Most days

30.9

25.6

28.6

27.1

27.6

27.7

29.5

26.8

Once a day

17.1

16.1

22.1

17.3

13.7

14.5

16.1

16.7

Several times a day

7.0

4.6

8.0

7.6

4.5

2.4

5.0

5.9

2,179

3,338

1,183

1,266

2,018

1,050

1,837

3,680

Not at all

22.7

31.6

42.3

34.0

22.7

15.1

28.4

27.9

Once a week

36.5

34.1

30.3

33.0

37.6

37.8

32.7

36.2

A few times a week

32.6

27.2

19.0

25.7

32.3

39.8

30.7

28.7

Most days

5.3

4.5

5.2

5.0

4.8

4.4

5.4

4.6

Once a day

2.2

2.1

2.5

1.8

1.9

2.5

2.3

2.0

Several times a day

<1.0

Fish

124

The results regarding eating behaviours show some variations between age and gender. Males report eating more fast food, meat, fish and less fruit and vegetables than women. Those aged 16 to 24 years report higher levels of unhealthy eating choices.

Table 47 presents Pearson correlations between diet choice and health and wellbeing measures and masculine conformity/ social connectedness latent classes for the male sample. For men, having a poorer diet (energy dense snacks and fast food) was associated with poorer mental health, sleep, happiness and personal wellbeing. Fish and fruit and vegetables were associated with better mental health, sleep, happiness and personal wellbeing. Higher BMI was associated with meat and fast food consumption; whereas lower BMI was associated with more fruit and vegetables consumption. Those who identified with the ‘socially connected’ latent class ate less fast food and meat and more fish, fruit and vegetables. Respondents who identified with the ‘self-reliant risk taking’ latent class ate more fast food and meat. Respondents who identified more closely with the ‘isolated’ latent class ate more energy dense snacks and less fish, fruit and vegetables. The strongest correlations were between psychological distress and fast food consumption, and also fruit and vegetable intake with psychological distress, personal wellbeing and happiness. However, all correlation strength was very low.

125

Table 47. Pearson correlations of menâ&#x20AC;&#x2122;s diet choices with health and wellbeing measures and masculine conformity/ social connectedness latent classes (male sub-sample) Sleep quality

Psychological distress (K10)

Suicidal ideation (PSFS)

Wellbeing (PWI)

Happiness (OHQ)

Resilience (BRCS)

Socially connected (C1)

Selfreliant risk taking (C2)

Isolated (C3)

2144

2176

2179

2151

2179

Body mass index (BMI)

Over the past week, how often have you consumed: Energy dense snacks (eg. confectionary, cakes, sweet biscuits, potato crisps)

Pearson correlation

-0.02

-0.08

0.18

0.12

-0.09

-0.15

-0.12

-0.02

0.01

0.06

Fast food (eg. fish and chips, hot chips, pizza, hot dogs, meat pies)

Pearson correlation

0.05

-0.10

0.21

0.09

-0.12

-0.16

-0.11

-0.08

0.09

0.04

Fruit and vegetables (either canned, fresh, frozen or dried)

Pearson correlation

-0.11

0.18

-0.24

-0.13

0.24

0.22

0.19

0.09

<0.01

-0.13

Meat (including red meat, pork or chicken)

Pearson correlation

0.08

0.01

<0.01

-0.04

0.04

-0.02

0.03

-0.05

0.08

<0.01

Fish (including canned, fresh, frozen or dried)

Pearson correlation

0.01

0.11

-.013

-0.12

0.16

0.13

0.06

0.04

-0.07

Note: Items that were statistically significant (p<0.05) are presented in bold.

126

// EATING BEHAVIOURS SUMMARY Eating behaviours have been shown to have an impact on overall health and wellbeing. A study in the UK reported that eating less than five portions of fruit and vegetable a day was associated with higher risk of mortality (Atkins & Michie 2015). There has also been an increase in research focused on the link between poor diet and an increased risk of common mental health problems, with some evidence showing a link between poor dietary habits and an increased risk of depression (Hiles et al 2013). This is supported in the results here, where less healthy eating habits were found to be associated with poorer mental health and sleep, and lower happiness and resilience scores. Interestingly, those who identified with the ‘socially connected’ latent class also reported healthier eating behaviours, while those identifying with the ‘isolated’ latent class reported the least healthy behaviours.

The results from this survey also show that less than half of respondents reported consuming fruit and vegetables several times a day. Interestingly, women reported higher consumption than men and young people reported the lowest levels of consumption several times a day. Low levels of fast food consumption were found, with most respondents reportedly eating these less than once a week or not at all. Interestingly, diet was also raised within the qualitative data (provided within the ‘perceptions of men’s health and wellbeing’ chapter) as a major health concern for men. This indicates some awareness regarding the impact of poor diet on health. These findings highlight that interventions need to focus on increasing the awareness of the influence of diet choices on both physical and mental health. This should extend to supporting individuals to make healthy choices regarding their diet. For this sample, interventions targeting the increase of healthy choices may be more beneficial than those aimed solely at decreasing poor dietary choices.

// WEIGHT, WEIGHT PERCEPTIONS AND DIETING BEHAVIOURS BMI was determined by asking respondents their weight (kg or Lb) and height (metres or feet and inches). A question determining waist circumference was presented to males only (cm or inches). Respondents with a BMI under 18.5 were classified as ‘underweight’, a BMI between 18.5 to 24.9 was classified as ‘healthy weight’, a BMI between 25.0 to 29.9 was classified as ‘overweight’ and a BMI over 30.0 was classified as ‘obese’.

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Table 48 presents frequency statistics for BMI, self-evaluation of weight and dieting behaviour for the five-country sample, and Table 49, presents BMI and dieting data by gender, age-band and sample.

Table 48. Frequency statistics for body mass index (BMI), self-evaluation of weight and dieting behaviour (five-country sample) BMI

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Underweight Healthy weight Overweight Obese

n % % % %

5,379 3.2 37.5 30.2 29.1

Self-evaluation of weight Very underweight Slightly underweight About the right weight Slightly overweight Very overweight

n % % % % %

5,515 <1.0 5.4 30.9 42.6 20.4

Currently dieting Yes, to lose weight Yes, to gain weight No

n % % %

5,517 23.5 1.4 75.1

Table 49. Frequency statistics for BMI, self-evaluation of weight and dieting behaviour (comparative sub-samples) Gender

Age-bands (years)

Sample

Males

Females

16 to 24

25 to 44

45 to 64

65+

Australia

Other countries

2,115

3,264

1,156

1,231

1,975

1,017

1,795

3,584

1.4

4.3

8.1

3.2

1.3

1.2

3.0

3.3

Healthy weight

31.7

41.2

57.9

43.2

27.9

26.0

39.2

36.6

Overweight

37.4

25.6

17.8

28.9

34.2

38.2

30.6

30.1

Obese

29.5

28.9

16.2

24.7

36.6

34.6

27.2

30.0

Self-evaluation of weight

2,176

3,339

1,184

1,266

2,017

1,048

1,836

3,679

Very underweight

1.1

<1.0

1.4

<1.0

Slightly underweight

7.1

4.3

10.6

5.2

3.7

3.0

5.4

About the right weight

29.7

31.7

44.6

38.0

21.6

24.6

33.9

29.4

Slightly overweight

46.0

40.4

33.3

40.0

47.0

47.7

40.6

43.5

Very overweight

16.1

23.1

10.1

16.3

27.1

24.0

19.2

20.9

BMI Underweight

Currently dieting

2,178

3,339

1,183

1,266

2,018

1,050

1,837

3,680

Yes, to lose weight

19.6

26.0

20.5

25.4

24.9

21.8

24.4

23.0

Yes, to gain weight

2.2

<1.0

3.2

1.0

<1.0

1.2

1.5

78.1

73.2

76.2

73.6

74.3

77.3

74.4

75.5

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Over half (59.3%) of the respondents identified being either overweight or obese, with only 37.5% being within the healthy BMI weight range. Men reported higher levels of overweight and obese BMI ratings. As age increased, a higher percentage of overweight and obese BMI ratings were seen in the results. Respondents aged 65 years and older reported the highest percentage of overweight and obese ratings at 72.8%. Figure 15 presents menâ&#x20AC;&#x2122;s BMI changes across the age-bands.

Figure 15. Changes in BMI categories across age-bands for male respondents

Men were less likely to view themselves as overweight in comparison to women; with 23.1% of women considering themselves to be very overweight in comparison to 16.1% of males. This was found despite men having a higher percentage of overweight or obese BMI ratings than women. Less than a quarter of respondents reported dieting behaviour at 23.5%. Women had more concern regarding weight issues; more females reported engaging in dieting behaviour to lose weight (26.0% as opposed to 19.6% for men). Although percentages were low, men and 16 to 24 year olds reported trying to gain weight though diet more frequently than women and older age groups.

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// WEIGHT, WEIGHT PERCEPTIONS AND DIETING BEHAVIOURS SUMMARY Over half of the sample were categorised as either ‘overweight’ or ‘obese’. This was concerning, and was a particular issue for men. Men reported higher levels of obesity. Statistics have shown a significant increase in obesity rates in men over the past 20 years (Fryar et al 2012). Obesity was also raised within the qualitative data (provided within the ‘perceptions of men’s health and wellbeing’ chapter) as a major health concern for men, indicating some awareness of obesity as an issue in men’s health. In addition to this, men reported lower levels of dieting behaviour compared to females. Campaigns to support men to reach and sustain a health weight is clearly needed.

Age also had an influence on the findings, with the percentage of people within overweight and obese categories increasing considerably with age, and healthy weight ratings reducing from 56.4% to 24.2% across the age-bands. The increase in obesity associated with age is concerning given the long-term effects and links to other physical health conditions. These results can also be viewed in relation to the physical activity results above, and also in relation to evidence showing that low levels of physical activity are linked to higher risk of obesity (Maher et al 2013). Obesity and dietary interventions focused on awareness of preventative measures for obesity, are required to support individuals to increase exercise and to eat healthily.

// BODY IMAGE CONCERN In order to assess body image concern, respondents were asked over the past three months “How much has your shape influenced how you feel as a person?” which was answered with a six-point Likert scale ranging from ‘not at all’ to ‘a great deal’. Whether this was distressing to them (or a preoccupation) was determined using a dichotomous ‘yes’ or ‘no’ response. If respondents indicated distress/ preoccupation a follow-up question asked which particular areas of their body they are concerned about (Burns et al 2013). Table 50 displays frequency statistics for measures of body image distress in a five-country sample, and Table 51 displays this for the compared sub-samples.

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Table 50. Frequency statistics for measures of body image distress (five-country sample) How much does weight/ shape influence how n you think of yourself as a person? Not at all % A great deal % Body image distress Yes

n %

5,513 50.9

Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

n % % % % % % % % % % % %

2,803 32.1 11.7 45.2 37.9 20.9 42.8 27.4 39.6 35.7 65.3 25.4 70.6

Area of concern Facial feature Height Body Hips Chest General overall Arms or legs Skin imperfections Hair Weight Muscles Stomach

5,496 17.0 18.7

As shown in Table 50, only 17% of respondents indicated that their weight and shape did not influence how they thought of themselves as a person. Over half of respondents reported getting very distressed with their body image. When asked to identify which areas in particular, the most commonly reported by these respondents included stomach then weight and then body.

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Table 51. Frequency statistics for measures of body image distress (comparative sub-samples) Gender

Age-bands (years)

Sample

How much does weight/ shape influence how you think of yourself as a person? Not at all

2,169

3,327

1,181

1,260

2,012

1,043

1,829

Other countries 3,667

23.5

12.8

10.5

11.3

17.5

30.3

16.3

17.4

A great deal

11.8

23.2

26.2

22.5

16.3

10.2

19.5

18.3

2,175

3,338

1,184

1,265

2,016

1,048

1,835

3,678

Yes %

39.2

58.5

75.7

59.5

42.9

28.0

51.2

50.7

Area of concern

850

1,953

896

753

861

293

937

1,866

Facial feature

Yes %

27.3

34.3

45.3

29.9

25.1

18.4

32.0

32.2

Height

Yes %

12.7

11.3

23.0

7.0

6.4

4.8

12.1

11.5

Body

Yes %

41.2

47.0

53.3

43.8

40.9

36.9

46.5

44.6

Hips

Yes %

14.2

48.2

54.7

39.2

25.4

20.1

38.8

37.5

Chest

Yes %

23.2

19.9

33.4

20.1

12.9

8.2

20.3

21.2

General overall

Yes %

40.9

43.7

49.3

44.1

38.7

31.7

44.2

42.1

Arms or legs

Yes %

16.3

32.2

38.8

27.5

20.0

13.7

29.3

26.4

Skin imperfections

Yes %

28.9

44.2

60.5

42.5

23.1

16.4

39.5

39.6

Hair

Yes %

31.8

37.4

47.4

37.5

26.7

21.8

36.1

35.5

Weight

Yes %

59.8

67.7

64.6

66.1

64.1

68.6

67.8

64.0

Muscles

Yes %

38.7

19.6

32.0

24.6

22.0

17.4

22.1

27.1

Stomach

Yes %

66.7

72.4

71.5

73.0

68.6

67.6

69.3

71.3

Body image distress

Males

Females

16 to 24

25 to 44

45 to 64

65+

Australia

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Women reported higher levels of distress related to body image than men with 58.5% and 39.2% respectively. However, although men reported lower levels of distress than women, the results are still considerable; with over a third of men reporting distress with their body image. Young people in the 16 to 24-year-old age bracket had higher levels of distress than other age groups; with 65.1% of young men and 79.5% of young women in this age group reporting distress. Stomach and weight remained the top two items of concern for both men and women, however following these items men were most concerned about body, whereas women were most concerned with hips. Those aged 16 to 24 years old showed higher levels of body image distress than all other age groups at 75.7%.

Table 52 presents Pearson correlations between body image items and the health and wellbeing measures and masculine conformity/ social connectedness latent classes for the male sample. For men higher scores on both body image items (weight or shape influence on self-perception; body image distress or preoccupation) were associated with poorer mental health and sleep quality as well as lower wellbeing, happiness and resilience. Male respondents who identified more closely with the ‘self-reliant risk taking’ or ‘isolated’ latent classes had higher scores on both body image items indicating body image influenced their self-perceptions more and that they had higher body image distress. For weight or shape influence on men’s self-perception the strongest correlation was with psychological distress, followed by BMI, wellbeing and happiness. For men’s body image distress or preoccupation, strongest correlation was also with psychological distress, followed by wellbeing and happiness. However, the correlation strength was low.

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Table 52. Pearson correlations of body image items with health and wellbeing measures and masculine conformity/ social connectedness latent classes (male sub-sample)

Weight or shape influence on selfperception Body image distress or preoccupation

Body mass index (BMI) 2,137

Sleep quality

Psychological distress (K10)

Suicidal ideation (PSFS)

Wellbeing (PWI)

Happiness (OHQ)

Resilience (BRCS)

Socially connected (C1)

Self-reliant risk taking (C2)

Isolated (C3)

2,169

2,141

2,169

Pearson correlation

0.23

-0.15

0.28

0.17

-0.22

-0.12

0.03

0.06

2,143

2,175

2,147

2,175

Pearson correlation

0.08

-0.21

0.37

0.23

-0.32

-0.31

-0.18

-0.02

0.04

0.10

Note: Items that were statistically significant (p<0.05) are presented in bold.

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// BODY IMAGE CONCERN SUMMARY Research regarding body image has mainly focused on women, however it is clear that men also experience levels of dissatisfaction or distress with their body image, which can result in emotional consequences (De Jesus et al 2015). The results from this survey support these findings showing that over a third of men reported distress with their body image. Evidence has shown that higher body image dissatisfaction has a direct association with poorer mental health-related quality of life and psychosocial functioning (Wilson et al 2013). The results above for men support this, highlighting that higher scores related to shape influencing self-perception and high body image distress were associated with poor mental health and sleep, lower personal wellbeing, happiness and resilience. This demonstrates the significant influence body image distress and high levels of weight-related self-perception has on mental health and wellbeing. Interestingly, although there were some differences in the areas of concern identified by gender, within these results, men and women were most concerned with similar parts of the body, with stomach and weight being the two highest areas of concern.

Evidence has shown that body dissatisfaction in men is one of the most consistent risk factors for the development of eating disorders (Dakanalis & Riva 2013). This indicates that high prevalence of body image distress and influence of weight selfperception could suggest the potential for eating disorder symptomology. The results within the ‘perceptions of men’s health and wellbeing’ chapter highlighted that eating disorders were identified by 16.1% of respondents as a major mental health problem for younger men aged 16 to 39 years. These results and those presented in this chapter highlight an awareness of eating disorders as a concern for young men. When considering the influence of the latent classes, those who identified with the ‘self-reliant risk taking’ or ‘isolated’ latent classes had higher body image distress scores and were more influenced by weight. This may indicate that these individuals are more at risk of developing longer-term health-related problems associated with body image and weight self-perception.

When considering the effect of age, there were some clear generational differences; with young people reporting the highest levels of distress. McCabe & Ricciardelli’s (2004) research supports this, as they highlight that body dissatisfaction is a concern for young men and boys – even from as early as six years old. They go on to highlight the association between high body dissatisfaction and other health risk

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behaviours such as disordered eating (McCabe & Ricciardelli 2004). Markey & Markey’s (2005) research has demonstrated an association between high body dissatisfaction and its contribution to unhealthy dieting behaviour. This highlights the relationship between body dissatisfaction and other areas discussed within this chapter, emphasising the need to consider all aspects of health behaviours in combination. As a result of this, preventative measures need to focus on enabling individuals to be aware of, and understand the complex interplay between, all health-related behaviours. // WEIGHT LIFTING AND STERIOD USE Weight training Weight training questions were adapted from Hale and colleagues (2013). Questions concerning weight training frequency over the past week were determined using a six-item response scale (from ‘none’ to ‘5 or more times per week’). For respondents who indicated that they had completed weight training over the past week, duration (typical length of session) and intensity of the session (light, moderate or heavy) were established. Table 53 shows the percentage of respondents who indicated they lifted weights as a form of exercise and Table 54 presents this for the compared sub-samples. Table 53. Frequency statistics for measures of weight training (five-country sample) Weight training Yes No

5,521

% %

21.6 78.4

Table 54. Frequency statistics for weight lifting training (comparative sub-samples) Gender

Age-bands (years)

Sample

Males

Females

16 to 24

25 to 44

45 to 64

65+

Australia

All other countries

2,178

3,343

1,183

1,267

2,020

1,051

1,840

3,681

Yes

24.1

19.9

23.9

26.7

17.8

20.0

24.7

20.0

75.9

80.1

76.1

73.3

82.2

80.0

75.3

80.0

Weight Training

Less than a quarter of respondents reported being involved in weight training at 21.6%. A higher proportion of men reported weight lifting as an exercise activity with 24.1% as opposed to 19.9% of women. Respondents aged 16 to 24 years also

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reported the highest level of engagement in weight training at 23.9%. Those aged 65 years and over reported higher levels than those aged 45 to 64 years.

Analysis of correlation data for the male sub-sample found that although weight lifting frequency was not associated with body image distress, it was positively associated with how weight/ shape influenced men’s self-perceptions (Pearson correlation=0.06; p=0.01). A greater weightlifting frequency was also correlated with dissatisfaction with some specific areas on the body including arms/ legs (Pearson correlation=0.09; p=0.01) and muscles (Pearson correlation=0.11; p≤0.01). But was negatively associated with weight (Pearson correlation=-0.10; p≤0.01). All correlation sizes were very low. Anabolic steroid use Anabolic steroid use, adapted from Blashill (2014), was initially assessed by one item determining lifetime use. This item provided six response options (ranging from ‘0 times’ to ‘40 or more times’). If the respondent indicated any lifetime use, a followup question determined whether this was done without a doctor’s prescription (‘yes’ or ‘no’). Male respondents reported low anabolic steroid use (n=2,179), with only 1.5% of men reporting lifetime use. Over the past 12 months, 12.5% of these men had taken steroids without a doctor’s prescription. // WEIGHT LIFTING AND STEROID USE SUMMARY The results above indicate that in general, respondents reported a low level of weight lifting activity. Men were more likely than women to lift weights as a form of exercise. These findings are somewhat consistent with the research suggesting that men are concerned with muscularity, and often experience dissatisfaction in this area (Stratton et al 2015). Stratton et al (2015) go on to argue that this is as a result of peer influence and body comparison, which highlights the need for more interventions aimed at reducing these influencing factors.

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// ALCOHOL AND/OR OTHER SUBSTANCE MIS/USE Lifetime alcohol, tobacco and/or other substance mis/use items were included and derived from the Young and Well National Surveys (Burns et al 2013). For respondents who identified using any substance(s), they were asked further questions concerning frequency of use, age of first use, dependency, desire to cut back, social or professional encouragement to stop, and reason for use. For alcohol, average number of standard drinks consumed in a drinking session, and selfidentified current drinking status was determined. For tobacco products, frequency and amount of tobacco used within the past three months was also gauged. Table 55 to Table 57 display frequency statistics for substance use in the five-country sample.

Table 55. Life time substance use (five-country sample) n

5,511

Alcohol

Yes %

91.8

Tobacco

Yes %

63.6

Cannabis

Yes %

54.2

Cocaine

Yes %

15.8

Methamphetamines

Yes %

18.9

Inhalants

Yes %

7.3

Sedatives/ sleeping pills

Yes %

22.6

Hallucinogens

Yes %

19.6

Opioids

Yes %

8.1

The results in Table 55 show that for lifetime use, alcohol was the most commonly used substance with 91.8%. Tobacco and cannabis were the most common following alcohol with 63.6% and 54.2%, respectively.

139

Table 56. Frequency statistics of recent alcohol, tobacco and/or other substance use and behaviours for respondents who had indicated recent use (five-country sample) Recent alcohol use (in the past 12 months)

5,054

Not had alcohol in the past 12 months

10.6

Less often

15.6

About 1 day a month

10.2

2-3 days a month

15.7

1-2 days a week

20.0

3-4 days a week

12.7

5-6 days a week

9.1

Everyday

6.1

4,510

A non-drinker

9.4

An ex-drinker

1.3

An occasional drinker

35.5

A light drinker

21.7

A social drinker

24.3

At present do you consider yourself to be

A heavy drinker

5.5

A binge drinker

2.3

3,505

Not at all and I have not smoked in the past 12 months

68.3

Not at all but I have smoked in the past 12 months

9.2

Less often than weekly

4.4

Recent tobacco use (in the past three months)

Al least weekly

3.0

Daily

15.1

Substance use behaviours

4,012

Yes %

52.4

5,171

Recently thought that you should cut down

Yes %

32.1

Another person suggested you should cut down

Yes %

14.7

Helps me enjoy a party

n Yes %

5,156 38.3

It is a habit

Yes %

26.4

It makes social gatherings more fun

Yes %

45.7

I cannot stop myself

Yes %

11.0

To cheer myself up

Yes %

23.1

To forget my problems

Yes %

20.0

To forget my worries

Yes %

22.2

Used substances while drinking alcohol

Reasons for substance use

As shown in Table 56, frequency of tobacco use in the past 12 months was reported at 31.7%. Over the past three months 22.5% of respondents reported using tobacco

140

products; with 15.1% of all respondents reporting daily use. The results for alcohol use were higher with only 10.6% of respondents reporting no use at all over the past year, and the most common frequency being one to two days a week at 20.0%. The majority of respondents considered themselves to be ‘occasional’, ‘social’ or ‘light’ drinkers respectively.

When asked if they engaged in substance use whilst consuming alcohol, over half of respondents (52.4%) answered ‘yes’. When reviewing the results regarding reasons for substance use, the two most commonly reported responses included ‘it makes social gatherings more fun’ (45.7%) and ‘helps me enjoy a party’ (38.3%) suggesting that substance use is perceived as a resource to aid social situations. Following these two items, ‘it is a habit’ was the next highest reported item.

Likelihood of substance misuse was calculated using two items. If respondents positively endorsed one of either item “…recently thought that you should cut down…” or “…another person suggested you should cut down…” they were categorised as having a ‘possible’ substance misuse. Endorsement of both items resulted in ‘probable’ substance misuse. Endorsement of neither item resulted in being placed in the ‘not likely’ category. Table 57 shows the likelihood of substance misuse for the five-country sample.

Table 57. Likelihood of substance misuse (five-country sample)

Not likely (none) Possible Probable

n % % %

5,169 65.2 22.7 12.1

As shown above in Table 57, across the five-country sample 12.1% of those who had used substances were grouped into the ‘probable’ substance misuse category, 22.7% were grouped into the ‘possible’ substance misuse category.

Table 58 to Table 61 displays frequency statistics for substance misuse compared sub-samples of gender, age-band and sample.

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Table 58. Frequency statistics for life-time substance use (comparative sub-samples) Gender

Age-bands (years)

Sample

Male

Female

16 to 24

25 to 44

45 to 64

65+

Australia

Other countries

2,175

3,335

1,184

1,264

2,016

1,046

1,834

3,677

Yes %

93.5

90.7

81.8

94.9

94.8

93.5

92.5

91.5

Tobacco

Yes %

69.7

59.6

41.5

70.6

68.2

71.3

62.4

64.2

Cannabis

Yes %

61.5

49.5

39.2

67.4

62.4

39.7

53.6

54.5

Cocaine

Yes %

20.9

12.5

7.7

24.3

19.1

8.4

15.4

16.0

Methamphetamines

Yes %

21.6

17.1

11.2

32.3

19.4

10.3

21.5

17.6

Inhalants

Yes %

11.4

4.6

5.2

13.2

7.6

2.0

7.6

7.2

Sedatives/ sleeping pills

Yes %

23.1

22.3

12.0

24.4

25.5

26.7

24.2

21.8

Hallucinogens

Yes %

25.0

16.0

9.9

28.1

24.0

11.7

17.8

20.4

Opioids

Yes %

10.2

6.8

5.2

10.0

10.5

4.7

7.6

8.4

Alcohol

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Table 59. Frequency statistics for recent alcohol and tobacco use for respondents who had indicated some life-time use (comparative sub-samples) Gender Female 3,021 10.2

Age-bands (years) 25 to 44 45 to 64 1,200 1,907 7.6 12.3

Recent alcohol use Not had alcohol in the past 12 months

n %

Less often

11.4

About 1 day a month

9.2

10.8

16.3

9.2

8.4

8.7

9.5

10.5

2-3 days a month

13.8

17.0

23.1

16.5

13.3

12.2

15.3

15.9

1-2 days a week

20.4

19.8

19.4

28.8

18.0

13.8

21.8

19.1

3-4 days a week

14.6

11.5

5.5

14.8

15.9

11.1

13.9

12.1

5-6 days a week

11.7

7.3

2.9

8.3

11.0

12.3

10.9

8.1

Everyday

7.7

5.0

<1.0

2.8

7.0

13.3

6.7

5.7

At present do you consider yourself to be A non-drinker

n %

1,803 7.7

2,707 10.5

912 16.0

1,108 7.4

1,669 7.6

821 8.3

1,558 8.3

2,952 10.0

18.4

16 to 24 969 5.8 26.1

12.0

14.1

65+ 978 16.1

Australia 1,696 8.0

12.6

13.9

Sample Other countries 3,358 12.0

Male 2,033 11.3

16.5

An ex-drinker

2.0

<1.0

1.2

1.1

1.5

1.3

An occasional drinker

32.2

37.7

33.1

34.5

36.2

38.2

33.0

36.9

A light drinker

22.7

21.0

11.0

18.4

26.8

27.5

22.4

21.3

A social drinker

25.1

23.8

33.7

28.9

19.2

18.3

25.5

23.7

A heavy drinker

7.4

4.2

2.2

6.5

6.9

5.1

7.1

4.7

A binge drinker

2.8

1.9

2.9

3.2

1.8

1.2

2.4

2.2

1,517

1,988

491

894

1,374

746

1,146

2,359

Not at all & I have not smoked in the past 12 months Not at all but I have smoked in the past 12 months Less often than weekly

% % %

69.1 7.6 4.4

67.7 10.5 4.4

37.5 29.5 14.5

63.9 12.1 4.9

73.1 4.2 2.0

85.1 1.7 1.6

69.8 10.3 4.4

67.6 8.7 4.4

Al least weekly Daily

% %

2.8 16.1

3.1 14.3

7.9 10.6

3.1 16.0

2.3 18.5

<1.0 10.7

2.8 12.7

3.1 16.2

Recent tobacco use

143

Table 60. Substance use behaviours and reasons for use of respondents who had indicated some life-time use (comparative sub-samples) Gender

Age-bands (years)

Sample

Male

Female

16 to 24

25 to 44

45 to 64

65+

Australia

Other countries

1,715

2,297

605

1,011

1,581

815

1,322

2,690

Yes %

56.2

49.6

60.2

71.7

50.0

27.5

55.7

50.8

2,074

3,097

983

1,216

1,967

1,005

1,721

3,449

Recently thought that you should cut down

Yes %

37.6

28.5

24.2

37.0

35.5

27.4

35.6

30.4

Another person suggested you should cut down

Yes %

18.5

12.1

11.2

16.7

16.5

12.1

15.8

14.1

Reasons for substance use Helps me enjoy a party

n Yes %

2,066 39.4

3,090 37.5

983 52.4

1,215 48.6

1,963 31.8

995 24.5

1,718 41.9

3,438 36.5

It is a habit

Yes %

31.3

23.1

13.5

28.6

31.9

25.4

28.5

25.3

It makes social gatherings more fun

Yes %

47.3

44.6

57.3

56.4

40.5

31.7

47.9

44.6

I cannot stop myself

Yes %

12.2

10.1

7.5

13.7

12.7

7.5

12.2

10.4

To cheer myself up

Yes %

23.3

23.0

27.9

32.4

20.9

11.4

25.6

21.9

To forget my problems

Yes %

20.3

19.7

25.4

27.2

18.1

9.3

23.1

18.4

To forget my worries

Yes %

22.4

22.1

28.9

30.6

19.8

10.2

25.6

20.6

Used substances while drinking alcohol

Table 61. Likelihood of alcohol and/or other substance misuse (comparative sub-samples) Age-bands (years)

Gender Male

Female

16 to 24

25 to 44

45 to 64

65+

Australia

Other countries

2,074

3,095

983

1,215

1,967

1,004

1,721

3,448

Not likely

58.8

69.5

72.1

60.2

62.2

70.5

62.1

66.8

Possible

26.2

20.4

20.3

25.8

23.6

19.4

24.5

21.8

Probable

14.9

10.1

7.5

13.9

14.2

10.1

13.5

11.3

Substance misuse likelihood

144

Sample

The above results related to age and gender highlight variations in substance use. Men reported higher levels in all types of substance use over a life-time. Alcohol was the highest reported substance used by men over a life-time (93.5%), followed by tobacco (69.7%) then cannabis (61.5%). Young people reported the lowest levels of alcohol and tobacco use and those aged 25 to 64 years reported the highest level of substance use overall. A higher percentage of men reported using other substances whilst consuming alcohol with 56.2%, as opposed to 49.6% of women. When asked to consider views regarding reducing substance use, men reported higher levels of thinking about cutting back and also that another person had suggested cutting back. The two most commonly reported reasons for substance use (‘it makes social gatherings more fun’ and ‘helps me enjoy a party’) remain the same across all age groups except those aged 65 years and older who reported higher levels of use related to habit. Men also reported a higher percentage of use due to habit than women with 31.3%, in comparison to 23.1%. Younger age groups (those under 45 years) reported using alcohol for coping more often than older age groups; reasons included ‘to forget problems’, ‘to forget worries’ and ‘to cheer myself up’. Frequency of tobacco use is similar for men and women; however, men showed higher levels of alcohol consumption. Those aged 45 to 64 years reported the most frequent use of tobacco; whereas more frequent alcohol use increased with age, with those aged 65 years and older reporting the highest daily intake. // ALCOHOL AND/OR OTHER SUBSTANCE MIS/USE SUMMARY As may be expected, alcohol was the highest consumed substance across the sample. Interestingly, over 50% of respondents reported using cannabis within their lifetime, and of those individuals who reported drinking alcohol, over 50% also reported using other substances whilst drinking. The results also indicate that 12.1% of the sample could be viewed as having probable substance misuse and 22.7% as possible substance misuse. These results highlight that over a third (34.8%) of the sample could be viewed as having substance misuse concerns. These reported levels were high and may account towards some of the other findings in this Report related to alcohol and/or other substance misuse as a clear health concern.

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When considering the influence of gender on the results, men clearly reported higher levels of alcohol consumption and were more likely to engage in other substance use alongside alcohol. Alcohol-related problems were also most frequently raised as a major mental health problem in both younger and older men across the five-country sample. This highlights existing awareness regarding alcohol as a concern for men’s health.

Men reported higher levels of possible and probable substance misuse than women, and those who were younger were less likely to have possible or probable substance misuse. Men also reported higher levels of habitual substance use indicating less control over consumption. Evidence suggests that men are more likely to have higher levels of substance misuse than females during late adolescence and early adulthood with substance misuse increasing with age (Chen & Jacobson 2011). This is consistent with the results here with young people aged 16 to 24 years, reporting the lowest lifetime substance use statistics for alcohol, tobacco and cannabis. When considering the recency of use and perceived type of drinker, young people again reported the lowest levels of frequent consumption; however, the highest proportion of young people also identified as ‘social drinkers’. This is consistent with the results related to reasons for using all substances with young people reporting ‘it makes social gatherings more fun’ and ‘helps me enjoy a party’ as the two highest reasons. These findings highlight a difference in the relationship to substances for different age groups. Those who were older were more likely to report habit as a reason for use; whereas younger people reported use as a means of amusement and sociability.

Evidence suggests that although substance misuse is most commonly associated with adults, the development of these problems can often commence in adolescence (Bellis et al 2009; Chen & Jacobson 2011). Therefore, consideration for the potential risk of long-term substance misuse should still be a concern and interventions aimed at preventing this issue should commence early. In addition to this, studies show that in particular, alcohol places the drinker at increased risk of injury, with young men more likely to experience injuries than young women (Thomas et al 2007). Patton et al (2013) highlight a global lack of specific guidelines for alcohol consumption for young people. They go on to highlight inconsistency in guidance regarding consumption or measures of a ‘standard drink’. This highlights a need for alcohol-related guidance for young people alongside health promotion initiatives highlighting the risks associated with drinking.

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// GAMBLING There were four items measuring gambling, which followed the format of the alcohol and/or other substance use questions: 1) incidence during the past 12 months; 2) gambling frequency (‘everyday’ to ‘once or twice a year’); 3) personal desire to cut back (‘yes’ or ‘no’); and, 4) social encouragement to stop (from a friend, relative or doctor). Table 62 displays frequency statistics for measures of gambling in the fivecountry sample. Table 62. Frequency statistics for measures of gambling (five-country sample) Have you gambled in the past 12 months?

5,512

Yes

22.6

77.4

How often?

1,244

Everyday

<1.0

Once a week

24.8

About once a month

16.0

Every few months

19.9

Once or twice a year

38.4

1,244

Yes

9.5

90.5

1,243

Yes

2.0

98.0

Of those that gambled in the past 12 months…

Have you recently thought that you should cut down on gambling?

Have you recently had a friend relative or doctor suggest that you should cut down on gambling?

The results related to gambling highlight that less than a quarter (22.6%) of respondents had engaged in gambling within the past year. The most common level of frequency was reported as once or twice a year with 38.4%. Less than 1% of those who gambled reported gambling every day; however, a quarter of the respondents who indicated some gambling in the past 12 months gambled once a week. Table 63 displays frequency statistics for measures of gambling by the sub-samples of gender, age-band and sample.

147

Table 63. Frequency statistics for measures of gambling (comparative sub-samples) Gender

Age-bands (years)

Sample

Males

Females

16 to 24

25 to 44

45 to 64

65+

Australia

Other countries

2,176

3,336

1,184

1,266

2,014

1,048

1,836

3,676

Yes

27.8

19.1

13.2

25.0

25.5

24.5

26.4

20.7

72.2

80.9

86.8

75.0

74.5

75.5

73.6

79.3

How often?

606

638

156

317

514

257

484

760

Everyday

1.5

<1.0

1.0

1.9

1.0

<1.0

Once a week

30.5

19.3

5.1

14.8

28.6

41.2

21.1

27.1

About once a month

18.6

13.5

15.1

16.1

18.3

15.7

16.2

Every few months

19.0

20.8

24.4

24.0

17.3

17.5

20.7

19.5

Once or twice a year

30.4

46.1

57.1

45.7

37.0

21.0

41.5

36.4

606

638

156

317

514

257

484

760

Yes

12.0

7.1

7.6

10.7

10.9

9.5

88.0

92.9

92.4

89.3

89.1

90.5

Have you recently had a friend relative or doctor suggest that you should cut down on gambling? Yes

605

638

156

317

514

256

484

759

3.1

<1.0

1.3

2.8

2.1

1.2

2.3

1.8

96.9

99.1

98.7

97.2

97.9

98.8

97.7

98.2

Have you gambled in the past 12 months

Of those that gambled in the past 12 monthsâ&#x20AC;Ś

Have you recently thought that you should cut down on gambling?

148

Men reported higher levels of gambling than women; with 27.8% of men reporting that they had gambled in the past 12 months as opposed to 19.1% of women. Men also reported a higher frequency of gambling with ‘once a week’ and ‘once a month’ being the most commonly reported frequencies. Young people reported much lower levels of engagement in gambling with only 13.2% identifying that they gambled in the past 12 months. Those aged 65 years and older reported the highest gambling rates; with 41.2% reporting gambling once a week. // GAMBLING SUMMARY The results for gambling indicate low levels of engagement with gambling behaviour across the sample. Age and gender had an influence on the results; with men reporting higher levels and more frequency than women. In addition to this, frequency of gambling appears to increase with age with those respondents aged 65 years and over reporting the highest level of everyday and once a week engagement in gambling. Interestingly, as shown within the perspectives of health and wellbeing chapter, 16.4% of respondents identified gambling as a major mental health problem for men aged 16 to 39 years of age. This figure increased slightly to 17.9% when referring to major mental health problems in men aged 40 years and over. Evidence has shown that engagement in online gambling is associated with poor mental health and use of substances (Scholes-Balog & Hemphill 2012). This suggests that although levels of gambling in this survey were reported as low, the health impact of gambling should not be underestimated and therefore be included within health promotion information.

149

Perceptions and Experience of Stigma and Discrimination // CHAPTER OVERVIEW This chapter reports on Module F of the survey, which examined perceptions and experiences of stigma and discrimination in three main areas including:   

Physical health; Mental health; and, Alcohol and/or other substance mis/use.

// PERCEPTIONS AND EXPERIENCES OF STIGMA AND DISCRIMINATION This module commenced with randomising respondents to one of three health conditions (1. physical health; 2. mental health; or 3. alcohol and/or other substance misuse). A brief stigma vignette was presented which was adapted from Corrigan et al (2009): “Chris is a person with [CONDITION 1, 2 or 3] who recently attended a community meeting. The community meeting was a discussion about the [CONDITION 1, 2 or 3] Chris experiences, and the role it plays in education, training and the workforce”. After the vignette, to assess respondents’ levels of stigma and discrimination, respondents were asked to rate four statements on a nine-point Likert scale (1=Strongly disagree to 9=Strongly agree) regarding their beliefs about Chris (eg. “Chris should be given assistance related to education, training or work”). An additional 15 items adapted from Corrigan et al (2009) and Rong et al (2007) were rated by respondents on a five-point Likert scale (1=Strongly disagree to 5=Strongly agree) and further assessed respondents’ attitudes towards people who had experienced these conditions (eg. “People like Chris are hard to talk to”). For these 15 items, three exploratory factor analyses using principal axis factor (PAF) analysis with orthogonal, promax rotation was completed for physical health, mental health and alcohol and/or other substance misuse. A priori alpha was set at 0.05. PAF analysis was chosen as it attempts to explain the shared variance and the initial factor solution was rotated using the PROMAX technique as components were correlated (>0.2) and thus assumed oblique. Prior to conducting the PAF analysis, the Kaiser-Meyer-Olkin (KMO) value (physical health KMO=0.88; mental health KMO=0.85; alcohol and/or other substance misuse KMO=0.84) and Bartlett Test of Sphericity (physical health: chi-square

150

[105]=10,740; p<0.01; mental health: chi-square [105]=7,855; p<0.01; alcohol and/or other substance misuse: chi-square [105]=7,636; p<0.01) were established. The KMO value exceeded the recommended minimum of 0.6 (Hair et al 2006; Kaiser 1974) and the Bartlett Test of Sphericity also reached statistical significance (Bartlett 1954), supporting the appropriateness of dimensionality analyses of the correlation matrix. Scree plots of eigenvalues were examined to determine the number of factors to extract. Three factors were identified for each analyses (see Appendix 2). Subsequent exploratory linear regression analyses were used to examine the association between the standardised demographic/ biographic items and the factor subscales for each condition (physical health, mental health, alcohol and/or other substance misuse) for the five-country sample and for the male sub-sample only. A priori alpha was set at 0.05. All models were significant (see Appendix 2). Nine additional questions from Rong et al (2007) assessed beliefs concerning the stigma and discrimination associated with these conditions (eg. “And, do you think people like Chris would be discriminated against by a bank, insurance company or other financial institution?”). These discrimination items were rated on a five-point Likert scale (1=Definitely unlikely to 5=Definitely likely). Discrimination items presented in this Report describe frequency data only. For all individuals who identified earlier in the survey as having experienced a physical health problem, mental health problem or alcohol and/or other substance misuse, an additional series of questions adapted from the Self-Stigma of Depression Scale (SSDS; Barney et al 2010) to suit each health condition were included. These items, rated on a five-point Likert scale (1=Strongly disagree to 5=Strongly agree), assessed feelings of shame, self-blame, social inadequacy and help-seeking inhibition (eg. ‘I feel ashamed’). Total self-stigma scores were calculated by taking the mean self-stigma score for each condition (physical health problem, mental health problem or alcohol and/or other substance misuse). Subsequent exploratory linear regression analyses were used to examine the association between the standardised demographic/ biographic items and the total self-stigma score for the five-country sample and for the male sub-sample only. A priori alpha was set at 0.05. // PERCEPTIONS OF PUBLIC STIGMA AND DISCRIMINATION The frequency data presented in Table 64 show the percentage of respondents in each of the health conditions (physical health problem, mental health problem or alcohol and/or other substance misuse) who agreed with the statements relating to different areas of stigma and discrimination beliefs.

151

Table 64. Stigma and discrimination ratings for each randomised condition (physical health problem, mental health problem or alcohol and/or other substance misuse; five-country sample) Physical health problem

Mental health problem

2,191

2,103

Alcohol and/or other substance misuse 2,151

1. Chris is responsible for this health condition

% agree

24.8

20.5

54.8

Personal beliefs about Chris*

2. Chris is able to overcome problems related to it

% agree

52.5

54.6

65.0

3. Chris should be able to receive help from the community

% agree

89.1

92.6

92.2

4. Chris should be given assistance related to education, training or work

% agree

91.3

91.8

89.3

2,178

2,102

2,148

% agree/ strongly agree % agree/ strongly agree % agree/ strongly agree % agree/ strongly agree % agree/ strongly agree % agree/ strongly agree % agree/ strongly agree % agree/ strongly agree % agree/ strongly agree % agree/ strongly agree % agree/ strongly agree % agree/ strongly agree % agree/ strongly agree

12.4

21.6

37.4

1.9

5.0

21.9

9.1

21.5

28.1

41.4

67.0

65.3

58.8

82.1

73.2

34.2

53.2

42.6

68.5

79.1

68.9

25.1

36.7

41.6

22.2

44.4

52.0

2.3

2.8

17.1

65.3

75.4

64.7

4.6

15.2

34.2

62.1

65.2

56.3

†

Stigma-related beliefs about ‘people like Chris’

5. Are a burden to their relatives 6. Are dangerous to others 7. Are hard to talk to 8. Are kept at a distance by others 9. Are not understood by other people 10. Are often artistic or creative people when they are well 11. Are often very productive people when they are well 12. Find it difficult to get married or to live with a partner 13. Frighten other people 14. Have themselves to blame 15. Often make good employees when they are well 16. Often perform poorly as parents 17. Often try even harder to contribute to their families or work when they are well

152

18. Should pull themselves together 19. Shouldn’t have children in case they pass on the problem

7.1

8.9

25.5

5.7

6.2

7.1

2,177

2,101

2,144

% probably likely/ definitely likely % probably likely/ definitely likely % probably likely/ definitely likely % probably likely/ definitely likely % probably likely/ definitely likely % probably likely/ definitely likely % probably likely/ definitely likely % probably likely/ definitely likely

66.1

72.7

79.8

40.9

48.7

54.7

17.3

24.9

32.2

68.0

86.2

85.8

11.0

18.4

24.4

65.0

76.7

82.9

14.0

28.9

40.8

16.4

30.0

36.3

% agree/ strongly agree % agree/ strongly agree

†

Perceptions of discrimination in the community ‘People like Chris would be discriminated against by’: 20. A bank, insurance company or other financial institution 21. A government or other public welfare agency 22. A public or private hospital 23. Other people they don’t know well 24. A doctor or other health professional 25. An employer 26. Family 27. Friends *Percent equals combined positive endorsements (Likert scale scores 6-9). † Percent equals combined positive endorsements (Likert scale scores 4-5).

153

As shown in Table 64, respondents agreed with more negative stigmatising beliefs about people with alcohol and/or other substance misuse, than those with a mental health problem. The physical health problem scenario consistently elicited the least stigmatising stereotypes from respondents apart from the ‘Chris is responsible’ item. The majority of people (65%) randomised to the alcohol and/or other substance misuse scenario believed that this was a condition that individuals could ‘overcome’, whereas less respondents endorsed this view for physical health and mental health problems. The overwhelming majority of respondents (approximately nine in 10) in each randomised scenario believed that people should receive community support and should be provided with vocational and educational assistance. There was very low endorsement of the item ‘shouldn’t have children in case they pass on the problem’ across all conditions. Positive stereotyped views were more frequently endorsed for people who experienced mental health problems, this included being artistic or creative and being good employees when well. For seven of the eight discrimination items, respondents indicated that people who experienced alcohol and/or other substance misuse would experience discrimination at a higher rate than respondents who were presented with the mental health problem scenario. Respondents in the physical health problem scenario consistently reported that discrimination would be less likely. Consistently across all health scenarios respondents reported that banks, insurance companies and financial institutions, other people who the individual did not know well, and employers would be the most likely to discriminate. Principal axis factor analysis of ‘stigma-related’ beliefs (items 5-19 in Table 64) derived three identical latent factors for physical and mental health problems. These three latent factors grouped together to represent: 1) negative beliefs and stereotypes of people with the condition; 2) positive beliefs and stereotypes of people with the condition; and 3) social distance and relating to others (note: social distance and relating to others will be referred to as ‘social distance’ from this point forward). Table 65 presents results from six linear regression models (physical health and mental health problems) that show the explanation of variance for the five-country sample for each factor (negative beliefs and stereotypes, positive beliefs and stereotypes and social distance).

154

Table 65. Linear regression: standardised demographic/ biographic beta values for a physical health problem (PHP) and mental health problem (MHP) by public-stigma factors (five-country sample) Factor 1

Factor 2

Factor 3

Negative beliefs and stereotypes

Positive beliefs and stereotypes

Social distance

PHP

MHP

PHP

MHP

PHP

MHP

734

710

734

710

734

710

Age

0.11

0.24

0.07

-0.02

0.01

0.13

Sex

-0.08

-0.02

-0.05

0.10

-0.10

-0.01

n Demographics

Rural

<0.01

0.05

0.03

-0.01

-0.03

Sexual orientation

-0.03

-0.01

0.05

<0.01

-0.03

0.04

Education

0.05

-0.15

-0.07

0.01

0.04

-0.02

EET

-0.04

0.02

0.01

-0.10

-0.01

0.01

Work industry (male or female)

-0.12

-0.04

-0.02

0.05

-0.11

0.04

Military service

-0.02

0.02

0.01

<0.01

-0.03

<0.01

Emergency service

0.09

0.04

-0.01

-0.02

-0.06

Masculinity latent class groups Socially connected (C1)

-0.09

-0.13

0.15

0.09

-0.04

0.01

Self-reliant risk taking (C2)

0.12

0.16

-0.02

0.03

0.13

Isolated (C3)

0.13

0.07

0.13

0.03

0.14

0.11

<0.01

0.03

-0.02

0.04

. <0.01

<0.01

Health, mental health and wellbeing, happiness and resilience Overall health K10

-0.11

0.14

0.21

-0.02

0.02

Substance misuse likelihood

-0.04

-0.05

0.01

0.05

-0.05

0.03

Wellbeing (PWI)

-0.15

-0.11

0.14

0.02

-0.16

<0.01

Happiness (OHS)

0.12

-0.01

<0.01

-0.08

0.13

-0.14

Resilience (BRCS)

-0.03

0.07

0.03

0.15

-0.01

0.17

Life event

0.03

<0.01

0.03

0.01

-0.03

Note: Statistically significant items (p ≤0.05) are highlighted in bold.

As shown in Table 65, consistently for both physical health and mental health problems, negative

beliefs and

stereotypes were

significantly explained

by age,

social

connectedness latent class and the ‘self-reliant risk taking’ latent class. People who were older and identified more with the ‘self-reliant risk taking’ latent class agreed with more stereotypical views. People who identified more with the ‘socially connected’ latent class had significantly fewer negative beliefs and stereotypes for both physical health and mental health problems. For a mental health problem, less education and lower

155

psychological distress was associated with more stereotypical beliefs and views; these associations were not found for a physical health problem. Those who worked in emergency services and those who identified more with the ‘isolated’ latent class had more negative stereotypical beliefs and views of people with physical health problems; whereas those who had higher wellbeing scores were less likely to endorse negatively stereotyped beliefs and views. When considering positive beliefs and stereotypes of people with either physical health or mental health problems, there were some noticeable commonalities. People who identified more with the ‘socially connected’ latent class and had high psychological distress endorsed significantly more positive beliefs and stereotypes about people who experience either a physical health or mental health problem. Those who identified more with the ‘isolated’ latent class and those who had higher wellbeing scores had more positive stereotyped beliefs and views of people with physical health problems. Males, people who had lower resilience scores, and those not in education or employment endorsed less positive beliefs and stereotypes of people with a mental health problem. For mental health problems, higher social distance was related to being older in age, identifying more closely with the ‘self-reliant risk taking’ latent class and having higher resilience scores. For both physical health and mental health problems, identifying with the ‘isolated’ latent class resulted in significantly higher social distance scores. Higher happiness was positively associated with higher social distance for a physical health problem, but negatively with social distance for a mental health problem. For physical health problems, those who were in a male-dominated industry and had lower wellbeing, had higher social distance scores. Principal axis factor analysis of public stigma items for alcohol and/or other substance misuse derived different results from physical health and mental health problems. Three latent factors grouped together to represent: 1) negative beliefs and stereotypes of people with the condition; 2) positive beliefs and stereotypes of people with the condition; and, 3) blame. Table 66 presents results from three linear regression models for alcohol and/or other substance misuse that show the explanation of variance from the fivecountry sample for each factor (negative beliefs and stereotypes, positive beliefs and stereotypes and blame).

156

Table 66. Linear regression: standardised demographic/ biographic beta values for alcohol and/or other substance misuse stigma factors (five-country sample) Factor 1

Factor 2

Factor 3

Negative beliefs and stereotypes

Positive beliefs and stereotypes

Blame

713

Age

0.12

0.03

-0.07

n Demographics Sex

0.09

0.01

-0.02

Rural

0.06

0.03

0.01

Sexual orientation

-0.07

0.03

-0.09

Education

0.06

-0.02

-0.06

EET

-0.02

-0.17

-0.08

Work industry (male or female)

-0.08

0.04

-0.13

Military service

-0.01

-0.03

0.06

Emergency service

0.02

0.03

-0.04

Socially connected (C1)

-0.05

0.14

-0.08

Self-reliant risk taking (C2)

0.12

0.01

0.17

Isolated (C3)

0.11

-0.01

0.09

Overall health

0.08

-0.01

0.00

K10

-0.05

0.10

-0.08

Substance misuse likelihood

-0.09

0.07

-0.07

Happiness (OHS)

-0.06

0.02

0.07

Resilience (BRCS)

0.01

0.09

0.01

Wellbeing (PWI)

-0.12

-0.08

-0.06

Life event

0.07

-0.05

0.06

Masculinity latent class groups

Health, mental health and wellbeing

Note: Statistically significant items (p ≤0.05) are highlighted in bold.

Similar to physical health and mental health problems, negative beliefs and stereotypes for alcohol and/or other substance misuse were explained by greater belonging to the ‘self-reliant risk taking’ latent class or older age groups. Like in the physical health condition, identification with the ‘isolated’ latent class resulted in greater agreement with negatively stereotyped beliefs and views. Additionally, gender was a factor whereby men were less likely to endorse views of negative stereotypes of people with alcohol and/or other substance misuse. Additionally, those who were more likely to have alcohol and/or

157

other substance misuse were less likely to endorse negative beliefs and stereotypes about this problem. For positive stereotyped beliefs about alcohol and/or other substance misuse, there were some commonalities with mental health problems. Those who were not in education or training were less likely to endorse positive beliefs and stereotypes about people with alcohol and/or other substance misuse. Whereas those who were more closely aligned with the ‘socially connected’ latent class and had higher resilience scores were more likely to endorse positive beliefs and stereotypes. Blame was a latent factor for alcohol and/or other substance misuse that did not emerge for physical health or mental health problems. More agreement with blame items in the alcohol and/or other substance misuse condition was found for those who identified as younger, heterosexual, worked in male-dominated industries and identified with either the ‘self-reliant risk taking’ or the ‘isolated’ latent classes.

// SELF-STIGMA Self-stigma scores by the condition experienced are presented by gender, age and sample in Table 67. Consistently across all sub-samples people who experienced alcohol and/or other substance misuse or a mental health problem had higher selfstigma ratings than those who experienced a physical health problem. Across all specified health problems, younger age groups (16 to 24 years and 25 to 44 years) had higher self-stigma ratings than those who were older (45 to 64 years and 65 years and over).

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Table 67. Frequency statistics of self-stigma ratings (comparative sub-samples) Gender

Five country sample

Age-bands (years)

Sample

Males

Females

16 to 24

25 to 44

45 to 64

65+

Australia

All other countries

Mean [CI 95%] n

Physical health problem

3097

38.7 [38.1-39.3] 1361

39.6 [39.0-40.2] 1736

43.8 [42.3-45.2] 350

42.3 [41.3-43.4] 570

38.5 [37.9-39.1] 1340

36.4 [35.7-37.1] 837

39.8 [39.0-40.5] 950

39.0 [38.5-39.5] 2147

Mental health problem

2525

48.6 [47.7-49.4] 878

50.9 [50.2-51.5] 1647

56.3 [55.4-57.3] 665

52.1 [51.2-53.0] 715

46.4 [45.6-47.2] 872

41.0 [39.6-42.4] 273

50.4 [49.6-51.2] 977

49.9 [49.2-50.5] 1548

Alcohol and/or other substance misuse

838

47.8 [46.8-48.9] 475

51.5 [50.1-52.8] 363

51.9 [49.5-54.3] 128

53.9 [52.4-55.4] 257

47.5 [46.3-48.8] 326

42.5 [40.7-44.2] 127

51.0 [49.6-52.4] 280

48.6 [47.5-49.7] 558

157

146

152

106

197

Mean [CI 95%]

Sample who experienced all conditions

Physical health problem

303

43.6 [41.6-45.5]

44.0 [41.9-46.2]

45.8 [40.0-51.6]

47.4 [44.6-50.2]

42.3 [40.5-44.1]

38.7 [35.0-42.5]

46.2 [43.7-48.8]

42.5 [40.8-44.2]

Mental health problem

303

48.1 [46.1-50.1]

51.2 [48.8-53.5]

58.2 [53.2-63.3]

53.8 [50.9-56.7]

47.1 [45.2-49.0]

40.8 [36.1-45.4]

52.0 [49.5-54.5]

48.3 [46.4-50.3]

Alcohol and/or other substance misuse

303

48.6 [46.6-50.5]

52.9 [50.7-55.0]

53.2 [48.1-58.2]

55.5 [52.8-58.2]

48.9 [47.0-50.8]

42.8 [39.0-46.5]

53.1 [50.7-55.4]

49.3 [47.5-51.1]

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Table 68 presents the linear regression results for the five-country sample and for the male sub-sample to predict self-stigma based on participant demographic and biographic characteristics. Both the models significantly explained the variance in self-stigma. For the five-country sample 38% of the variance in the self-stigma scores

was

explained

participant

characteristics

(AdjR2=0.38;

(19,

1,487)=50.10, p=<0.01). For the male sub-sample 33% of the variance in the selfstigma scores was explained by participant characteristics (AdjR2=0.34; F(18, 583)=17.79, p=<0.01). For the male sub-sample, self-stigma was significantly explained by age, working in emergency services, the latent classes (‘socially connected’ and ‘isolated’ groups), alcohol and/or other substance misuse and psychological distress. Males had more self-stigma if they were younger or did not currently or previously work in emergency services. For the latent classes, males that identified more strongly with the ‘isolated’ group had significantly higher self-stigma, but this finding was reversed for those who identified with the ‘socially connected’ group. Males who had more ‘probable’ alcohol and/or other substance misuse and higher psychological distress had higher self-stigma scores. These findings were similar for the five-country sample, except for those who worked in emergencies services where no significant explanation of variance was found. Additionally, not being in education or training, identifying more closely with the ‘self-reliant risk taking’ latent class group, experiencing stressful life events, having lower personal wellbeing, and lower levels of happiness were significantly associated with higher self-stigma scores.

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Table 68. Linear regression: standardised demographic/ biographic beta values for self-stigma (five country sample and male sub-sample)

Five-country sample

Male sub-sample

1,507

602

Age

-0.13

-0.11

Sex

0.02

Rural

-0.02

-0.07

Sexual orientation

<0.01

-.05

Education

0.01

0.04

EET

0.07

0.04

Work industry (male or female)

-0.01

Military service

0.02

Emergency service

-0.03

-0.07

Socially connected (C1)

-0.08

-0.11

Self-reliant risk taking (C2)

0.05

0.06

Isolated (C3)

0.13

0.14

Overall Health

<0.01

K10

0.27

0.32

Substance misuse likelihood

0.08

0.12

Wellbeing (PWI)

-0.11

-0.05

Happiness (OHS)

-0.08

-0.12

Resilience (BRCS)

-0.05

0.07

Life event

0.05

0.04

Note: Statistically significant items (p ≤0.05) are highlighted in bold.

// STIGMA AND DISCRIMINATION CHAPTER SUMMARY Perceptions and experience of stigma and discrimination Stigma has been defined as the way certain attributes are socially agreed as worthy of devaluation and social avoidance (Gilbert 2004). For those individuals who experience stigma, the effects can include feelings of fear, isolation, guilt and embarrassment, and result in avoidance of help-seeking (Clement et al 2014; Corrigan 2004; Gulliver et al 2010; Yousaf et al 2014). Hatzenbuehler et al (2013) argue that stigma in itself is a cause of health inequality. There is evidence from population surveys that stigmatising attitudes towards people with mental health problems are highly prevalent (Crisp et al 2000). In addition to this, evidence suggests that men are more likely to endorse stigmatising attitudes than women (Crisp et al 2005). The findings from this survey highlighted that men held more stigmatising attitudes towards mental health; however, in contrast, men were less likely to endorse negative beliefs and stereotypes of alcohol and/or other substance misuse.

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Stigma related to mental health problems were evidenced in the findings most commonly by those who were older and associated with the ‘self-reliant risk taking’ latent class. Parcesepe & Cabassa (2013) found that stigmatising attitudes are endorsed both by adults and children, however in these findings, older age was more highly associated with holding negative beliefs and stereotypes towards mental health than younger age groups. Younger people had less stigmatising attitudes than older people related to physical health and mental health problems, however were more likely to endorse blame items towards alcohol and/or other substance misuse. Public perceptions of mental health problems are often related to generalised ideas of psychoses or bipolar disorder, despite these being some of the least common illnesses reported (Wilkins 2010). This has a significant impact on the perceptions of the general public about mental health experiences resulting in more dangerous perceptions. When reviewing these findings, very low percentages of respondents endorsed the items related to danger, however there were still a significant number of respondents reporting that individuals with a mental health problem may ‘frighten other people’. This may reflect the view that ‘others’ think this, even if the respondent does not hold this view themselves; however, it may also demonstrate that there still may be a lack of understanding regarding the potential risk of individuals with mental health problems. These views are also reflected in the findings for social distance whereby individuals endorsed views regarding maintaining a social distance from an individual with a mental health problem. These social distance beliefs were more commonly endorsed by older respondents, those who identified with the ‘self-reliant risk taking’ class and also those who had high resilience scores. In general, physical health problems received the least stigmatising beliefs in comparison to mental health problems and alcohol and/or other substance misuse. However, interestingly those who reported working in emergency services appeared to hold more negative beliefs and stereotypes regarding people with physical health problems. This may be because they are confronted with accidents in their line of work on a daily basis, and that these accidents could be viewed as potentially avoidable. The findings suggest that alcohol and/or other substance misuse appears to be the most highly stigmatised health problem, with higher agreement across most of the negatively stereotyped views. Alongside this, respondents also felt that alcohol and/or other substance misuse was most likely to elicit negative attitudes from others. These findings are consistent with a review comparing public attitudes

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towards drug addiction and mental illness completed in the UK showing significantly greater stigma associated with substance misuse than mental illness (UKDCP 2010). Negative attitudes towards individuals with substance misuse are common and there is evidence to suggest that the impact of stigma is pervasive across all areas of an individual’s life resulting in social alienation (Livingston et al 2012; Room 2005). Livingston et al (2012) argues that stereotypes regarding alcohol and/or other substance misuse are often publically endorsed therefore increasing the stigma faced by individuals. Livingston et al (2012) goes on to suggest that such issues are treated more as a moral or criminal problem rather than a health concern. However, within these findings, when asked about prognosis, alcohol and/or other substance misuse received the highest percentage of respondents reporting the problem could be overcome. In addition to this, positive stereotyped beliefs related to alcohol and/or other substance misuse were endorsed within the findings, specifically by those who identified with the ‘socially connected’ latent class and those who reported high resilience scores. Interestingly, the results show that men were less likely to endorse negative beliefs and stereotypes of people with alcohol and/or other substance misuse. Those who were more likely to have alcohol and/or other substance misuse were also less likely to endorse negative beliefs and stereotypes. A unique aspect of stigma related to alcohol and/or other substance misuse, is when its viewed as within the individuals control and therefore blame is attached to the individual. Within these findings, blame was a latent factor associated with alcohol and/or other substance misuse only and did not emerge when considering physical or mental health problems. When reviewing the vignette regarding an individual with a specific health problem, alcohol and/or other substance misuse received the highest percentage of respondents (54.8%) identifying that the individual was responsible for the health condition. These findings are consistent with research whereby individuals are perceived to have self-control over their illness and therefore are subsequently blamed (Corrigan et al 2009). More blame was reported by younger respondents, those who were heterosexual and worked in male-dominated environments. In addition to this, those who identified with the ‘self-reliant risk taking’ and ‘isolated’ latent classes showed more agreement with the blame items. The industry of work of an individual appears to have an influence on stigma. Those who work in male-dominated environments were more likely to endorse negative beliefs and stereotypes and social distance towards those with physical health problems. In addition to this, those working in male-dominated environments were also more likely to endorse blame towards individuals with alcohol and/or other

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substance misuse. These are interesting findings and highlight the significance of the working context in views regarding health. Working in a male-dominated environment has been shown to promote high risk factors for mental health problems and can also lead to negative social issues such as isolation (Roche et al 2016). The results above may also influence communication about health problems in the workplace and help-seeking behaviour. Further work is required to establish how the workplace may influence perceptions towards health and how interventions can target the workplace to reduce negative beliefs and stereotypes towards health-related problems.

Self-stigma Self-stigma, defined as the internalisation of stigma, has been argued to be one of the most damaging aspects of stigma due to the beliefs of the individual that they are of less value to society (Green et al 2003). Due to the perceived masculine norms present within the male culture, evidence suggests that men are more likely to internalise stigma (Vogel et al 2007). Livingston & Boyd (2010) in a systematic review focused on the consequences of stigma, found a negative relationship between internalised stigma and psychosocial variables such as self-esteem and hope. These findings suggest that for males, self-stigma was experienced more by younger men and those not working within emergency services. High self-stigma was also reported by those respondents associated with the ‘isolated’ latent class. This reflects the evidence regarding the impact of self-stigma whereby individuals perceive themselves as socially unacceptable (Ben-Zeev et al 2010), which may result in more isolation from others. Lower self-stigma was reported by those within the ‘socially connected’ latent classes, suggesting that those who feel more involved within society are less likely to internalise their stigma. Males who reported probable alcohol and/or other substance misuse and higher psychological distress also had higher self-stigma. In addition to this, self-stigma is also shown to be associated with poor mental health, low personal wellbeing and low happiness. It could be argued to be expected that those experiencing low levels of mental health, wellbeing and happiness would therefore internalise stigma to a greater extent, however these findings offer useful information regarding the need to review all aspects of health combined rather than focus on individual aspects due to their interconnectedness.

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Information and Help-Seeking, Service Utilisation and Satisfaction // CHAPTER OVERVIEW This chapter reports on three main areas measured in Module E of the survey, including:  Information seeking confidence and experience for a physical health problem, mental health problem or alcohol and/or other substance misuse;  Experience of a physical health problem, mental health problem or alcohol and/or other substance misuse; and,  Help-seeking experiences for a physical health problem, mental health problem or alcohol and/or other substance misuse. // INFORMATION SEEKING INFORMATION SEEKING: Questions were adapted from previous research (Burns et al 2013; Burns et al 2007). Respondents were randomly assigned to one of three conditions, with questions relating to either: a physical health problem; a mental health problem; or alcohol and/or other substance misuse). Respondents were asked to imagine they (or a man very close to them) needed help for the randomised health condition. They were then asked to rate on a four-point Likert-scale their level of confidence (1=‘Not confident’ to 4=‘Confident’) around information seeking (eg. ‘That you could find the information you needed?’) and the support provided (eg. ‘That the care would be helpful?’). For further analysis, respondents’ overall confidence helpseeking was calculated by taking the mean score of all eight confidence items. Missing values were dealt with by the sum of the valid values being divided by the number of valid values. Respondents were also asked what age respondents think such problems start, and how long problems need to be present before seeking help. Additionally, all respondents were asked about whether they had looked for information for a physical health, mental health and alcohol and/or other substance misuse issue. Respondents were presented with dichotomous response options (1=‘No’, 2=‘Yes’). If they responded ‘yes’, they were asked to select how they obtained this information from a list of options (eg. ‘Searched Internet, apps or etools’).

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Frequency statistics for information and help-seeking confidence for a physical health problem, a mental health problem or alcohol and/or other substance misuse for the five-country sample is presented in Table 69. Table 70 presents these statistics by gender. Table 69. Frequency statistics for measures of information and help-seeking for major health problems (five-country sample) Imagine you (or a man very close to you) needs help for a…

If you (or that man very close to you) needed to access care or treatment for a major physical health problem, are you confident that…

Physical health problem

Mental health problem

Alcohol and/or other substance misuse

2,352

2,405

2,355

‘Yes confident’

You could find the information you needed?

70.0

64.1

68.6

You could access the medical care needed?

61.8

49.8

52.2

You could access the psychological care needed?

41.4

41.9

The care would be affordable?

26.3

23.8

22.0

The care would be helpful?

37.3

28.4

27.4

The care would assist in the return to school or work?

32.1

25.2

25.4

If you received professional help you would fully recover?

26.7

20.0

20.4

If you received professional help you would have some improvement?

51.7

46.1

Table 70. Frequency statistics for measures of information and help-seeking for major health problems (gender sub-samples) Imagine you (or a man very close to you) needs help for a….

Physical health problem

Alcohol and/or other substance misuse

Males

Females

Males

Females

Males

Females

954

1,399

972

1,433

915

1,141

You could find the information you needed?

70.5

69.7

61.4

65.9

69.9

67.7

You could access the medical care needed?

63.4

60.8

50.3

49.5

56.2

49.7

You could access the psychological care needed?

40.6

42.0

40.8

41.8

42.5

41.5

The care would be affordable?

29.7

24.0

25.5

22.7

22.4

21.7

The care would be helpful?

39.5

35.8

27.3

29.2

28.2

26.9

The care would assist in the return to school or work?

35.2

30.0

25.6

24.8

27.1

24.3

If you received professional help you would fully recover?

28.7

25.3

21.5

19.1

22.4

19.1

If you received professional help you would have some improvement?

52.2

51.4

44.0

47.5

48.0

44.9

If you (or that man very close to you) needed to access care or treatment for a major physical health problem, are you confident that… ‘Yes confident’

166

Mental health problem

Most respondents reported more help-seeking confidence across items for a major physical health problem than for a major mental health problem or alcohol and/or other substance misuse. Consistently, respondents rated higher levels of confidence for finding the information they need. Lower confidence was expressed by respondents across all health conditions related to not feeling confident that care would be affordable, and that if they or a close friend received professional help they would recover fully. A series of correlations were conducted to determine significant associations with total confidence help-seeking. Correlations of demographics and biographic characteristics with confidence help-seeking for physical health, mental health or alcohol and/or other substance mis/use issues for the five-country sample are presented in Table 71. Table 71. Correlations of demographic/biographic characteristics and confidence help-seeking for physical health, mental health or alcohol and/or substance mis/use issues (five-country sample)

Age

Pearson correlation n

Sex

Pearson correlation n

Rural

Pearson correlation n

Sexual orientation

Pearson correlation n

Education

Pearson correlation n

EET

Pearson correlation n

Military service

Emergency service Work industry (Male or Female)

Pearson correlation n Pearson correlation n Pearson correlation n

Physical health problem help-seeking confidence

Mental health problem help-seeking confidence

0.12

0.10

Alcohol and/or other substance misuse help-seeking confidence 0.12

2,352

2,405

2,355

-0.02

0.02

-0.03

2,352

2,405

2,355

0.04

0.05

0.01

2,352

2,405

2,355

-0.11

-0.08

2,106

2,166

2,107

0.19

0.09

0.10

2,352

2,405

2,355

0.08

0.01

0.03

2,220

2,252

2,200

0.01

-0.01

0.03

2,352

2,405

2,355

0.03

0.01

2,352

2,405

2,357

0.11

0.17

0.10

1,050

1,103

1,054

Note: Statistically significant correlations (p â&#x2030;¤0.05) are highlighted in bold.

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As shown in Table 71, those who were older, more highly educated and worked in female-dominated industries were significantly more confident in help-seeking across all conditions (physical health problems, mental health problems or alcohol and/or other substance misuse). People who identified as LGBTQIA were significantly less confident at help-seeking across all conditions. Those who lived in urban areas were significantly more confident help-seeking for mental health problems. People who were in education and training were significantly more confident in help-seeking for physical health problems. All correlations had very low to low correlation strength. Correlations of masculinity latent classes and stigma beliefs with confidence helpseeking for physical health, mental health or alcohol and/or other substance misuse issues for the five-country sample and for the male sub-sample are presented in Table 72. Table 72. Correlations of masculinity and stigma items with confidence help-seeking for physical health, mental health or alcohol and/or other substance mis/use issues (five-country and male subsample) Physical health problem help-seeking confidence

Mental health problem help-seeking confidence

Alcohol and/or other substance misuse help-seeking confidence Male Fivecountry subsample sample

Fivecountry sample

Male subsample

Fivecountry sample

Male sub-sample

n Pearson correlation Pearson correlation Pearson correlation

2,352

954

2,405

972

2,355

914

0.12

0.15

0.18

0.13

-0.01

0.04

0.03

0.09

0.01

-0.18

-0.16

-0.21

-0.15

-0.14

n Pearson correlation

1513

634

1567

663

1413

566

-0.27

-0.31

-0.35

-0.29

-0.31

Masculinity latent class Socially connected (C1) Self-reliant risk taking (C2) Isolated (C3) Stigma Self-stigma

n Negative stereotypes and beliefs Positive stereotypes and beliefs Social distance Blame

727

286

743

289

707

263

Pearson correlation

-0.12

-0.11

-0.09

-0.15

-0.07

-0.01

Pearson correlation

0.02

-0.06

-0.03

0.01

0.03

-0.02

-0.13

-0.12

-0.14

-0.20

0.01

Pearson correlation Pearson correlation

Note: Statistically significant correlations (p â&#x2030;¤0.05) are highlighted in bold.

168

0.06

As shown in Table 72, those who were identified with the ‘socially connected’ latent class were significantly more confident help-seeking across all conditions (physical health problems, mental health problems or alcohol and/or other substance misuse). People who identified more with the ‘isolated’ latent class or had higher levels of selfstigma were significantly less confident help-seeking across all conditions. These findings were consistent for the five-country sample, and for the male sub-sample. Additionally, for men only, identifying more with the ‘self-reliant risk taking’ latent class was associated with more confidence help-seeking in the mental health condition only. For the five-country sample physical health condition, more negative stereotypes and greater social distance scores were associated with less confidence help-seeking. For the five-country sample and the male sub-sample in the mental health condition, more negative stereotypes and greater social distance scores were associated with less confidence in help-seeking. All correlations had very low to low correlation strength. The strongest correlations were consistently for self-stigma across all types of help-seeking. Correlations of health and mental health items with confidence help-seeking for a physical health problem, mental health problem or alcohol and/or other substance misuse for the five-country sample and for the male sub-sample are presented in Table 73.

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Table 73. Correlations of health and mental health related items with confidence help-seeking for physical health, mental health or alcohol and/or other substance mis/use issues (five-country and male subsample) Physical health problem help-seeking confidence FiveMale country subsample sample Overall health

Pearson correlation n

Psychological distress

Pearson correlation n

Suicidal ideation

Pearson correlation n

Substance use likelihood

Pearson correlation n

Happiness

Pearson correlation n

Resilience

Pearson correlation n

Wellbeing

Pearson correlation n

Life events stress

Pearson correlation n

Mental health problem help-seeking confidence Five-country sample

Male subsample

Alcohol and/or other substance misuse help-seeking confidence Male Fivesubcountry sample sample

0.25

0.24

0.20

0.22

0.20

0.22

2353

954

2405

972

2357

915

-0.38

-0.45

-0.28

-0.34

-0.26

-0.33

2352

954

2405

972

2355

914

-0.22

-0.25

-0.23

-0.27

-0.16

-0.20

2315

940

2363

955

2311

901

-0.04

-0.09

-0.05

-0.03

-0.09

-0.12

1721

713

1739

695

1709

666

0.36

0.42

0.32

0.40

0.30

0.38

2352

954

2405

972

2355

914

0.27

0.30

0.23

0.30

0.23

0.27

2352

954

2405

972

2355

914

0.43

0.48

0.39

0.44

0.35

0.41

2352

954

2405

972

2355

914

-0.12

-0.13

-0.09

-0.13

-0.09

-0.12

2353

954

2405

972

2355

914

Note: Statistically significant correlations (p â&#x2030;¤0.05) are highlighted in bold.

As shown in Table 73, those who reported better overall health, higher wellbeing, happiness and resilience scores were significantly more confident help-seeking across all conditions (physical health problems, mental health problems or alcohol and/or other substance misuse). People who reported higher levels of psychological distress, suicidal ideation and experiencing stress from a life event were significantly less confident help-seeking across all conditions. These findings were consistent for the five-country sample, and for the male sub-sample. Additionally, for men only, a greater likelihood of having alcohol and/or other substance misuse was associated with less confidence in help-seeking for a physical health problem or substance misuse issues. Whereas for the global sample, greater likelihood of having alcohol and/or other substance misuse was associated with less confidence in help-seeking for a mental health problem or substance misuse issues. The strongest correlations were consistently for wellbeing across all types of help-seeking confidence, followed

170

by happiness and psychological distress; these all had low to moderate correlation strength. Frequency statistics showing respondents’ views on when a person should seek help if experiencing a physical health problem, mental health problem or alcohol and/or other substance misuse for the five-country sample is presented in Table 74. Table 75 presents these statistics by gender. Table 74. Frequency statistics of help-seeking items for major health problems (five-country sample) How long do you think a ….

Physical health problem

Mental health problem

Alcohol and/or other substance misuse

… needs to be present before you (or that man close to you) should seek help? n

2,350

2,405

2,356

Less than 2 weeks

59.7

29.3

19.7

2 to 4 weeks

28.1

38.9

27.5

5 to 8 weeks

7.1

18.1

22.1

9 to 12 weeks

1.9

5.4

10.0

More than 12 weeks

3.1

8.4

20.7

Table 75. Frequency statistics for help-seeking items for major health problems (gender subsamples) How long do you think a ….

Physical health problem

… needs to be present before you (or that man close to you) should seek help?

Mental health problem

Alcohol and/or other substance misuse

Males

Females

Males

Females

Males

Females

952

1,398

972

1,433

916

1,440

Less than 2 weeks

56.7

61.8

29.2

29.4

19.9

19.5

2 to 4 weeks

30.3

26.6

34.7

41.7

25.8

28.7

5 to 8 weeks

8.5

6.2

19.7

17.0

20.9

22.9

9 to 12 weeks

1.9

6.6

4.5

8.7

10.8

More than 12 weeks

2.6

3.5

9.9

7.3

24.8

18.1

The results regarding the length of time to wait before help should be sought show that for physical health problems, 59.7% of respondents identified help being required within two weeks. This figure reduced to 29.3% related to mental health problems and 19.7% for alcohol and/or other substance misuse. When considering

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gender, a higher percentage of women than men reported physical health problems as requiring help within two weeks. For mental health problems, males and females reported some help-seeking similarities and differences. Although approximately 30% of all men and women reported that they would seek help within two weeks of experiencing mental health symptoms, a greater proportion of men reported they would delay help-seeking – from five weeks to more than 12 weeks. For alcohol and/or other substance misuse, a greater percentage of both men and women reported that they would delay help-seeking longer as compared to both physical health and mental health problems. A quarter of all men in this survey reported that they would wait longer than 12 weeks if they were experiencing an alcohol and/or other substance use problem. Table 76 displays frequency statistics for measures of information and help-seeking for major health problems and Table 77 displays this for gender sub-samples. Table 76. Frequency statistics for measures of information and help-seeking for major health problems (five-country sample) Have you ever looked for information about a…

Physical health problem

Mental health problem

Alcohol and/or other substance misuse

2,350

2,406

2,353

Yes

79.4

71.2

43.3

20.6

28.8

56.7

1,867

1,714

1,018

How did you get this information? ‘Yes’ Asked a doctor

70.9

56.0

27.8

Asked a family member

31.6

21.6

14.4

Asked a friend

27.9

27.2

22.3

Bought a book or health magazine

11.6

17.6

13.2

Called a telephone helpline

9.8

15.1

15.2

Contacted a community health centre

10.8

17.2

17.9

Printed information from a pharmacy or medical centre

16.2

13.9

17.7

Searched the Internet, apps, or etools

90.4

83.4

79.5

Television or radio

5.7

6.5

9.5

Visited the library

9.2

9.6

9.4

Over 70% of respondents who were asked whether they had looked for information about a physical health problem or mental health problem indicated they had

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searched for information, whereas information on alcohol and/or other substance misuse was only sought by 43.3%. The overwhelming majority of respondents turn to the Internet, apps or etools for information about a major physical health problem (90.4%), mental health problem (83.4%), or alcohol and/or other substance misuse (79.5%). Seeking information from a doctor was also common for physical health problems (70.9%) and mental health problems (56.0%), however less so for alcohol and/or other substance misuse (27.8%). Asking a family member was one of the most common sources for information related to physical health problems, however was one of the least common sources for alcohol and/or other substance misuse. Table 77. Frequency statistics for measures of information and help-seeking for major health problems (gender sub-samples) Have you ever looked for information about aâ&#x20AC;Ś

Physical health problem Males Females

Mental health problem

Alcohol and/or other substance misuse Males Females

Males

Females 1,433

913

952

1,398

973

Yes %

78.5

80.1

63.8

76.3

38.0

46.6

No %

21.5

19.9

36.2

23.7

62.0

53.4

Asked a doctor

75.0

68.1

58.3

54.6

29.1

27.1

Asked a family member

31.3

31.8

20.3

22.4

16.7

13.3

Asked a friend

26.5

28.7

25.9

28.0

24.8

21.0

Bought a book or health magazine Called a telephone helpline

12.2

11.3

16.7

18.0

15.3

12.1

7.4

11.4

12.2

16.7

13.5

16.1

Contacted a community health centre Printed information from a pharmacy or medical centre Searched the Internet, apps, or etools Television or radio

10.3

11.1

15.8

18.0

17.9

14.2

17.6

12.7

14.5

19.3

16.8

89.0

91.3

80.0

85.3

78.1

80.2

5.5

5.8

6.8

6.3

13.8

7.3

Visited the library

8.8

9.5

8.1

10.4

10.1

9.1

How did you get this information? â&#x20AC;&#x2DC;Yesâ&#x20AC;&#x2122;

1,440

When considering the impact of gender on the results, men were more likely than women to ask a doctor in all three health problems. Men were less likely to ask a friend for information related to physical or mental health problems, however were more likely to ask a friend for information related to alcohol and/or other substance misuse.

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// EXPERIENCE OF HELP-SEEKING EXPERIENCE: Questions were adapted from previous research (Burns et al 2013; Davenport et al 2007). All respondents were asked to select whether they, or a man close to them, had experienced a major physical health problem, mental health problem or alcohol and/or other substance misuse. A follow-up question determined if the individual experienced the condition or whether a man they were close to had experienced the condition. HELP-SEEKING EXPERIENCES: For respondents who had experienced one or more conditions, the module subsequently asked about whether they sought help for the condition. If help had been sought, a question determined who provided the help by presenting a list of health professionals (eg. ‘a psychologist’) and nonprofessionals (eg. ‘family’) help-seeking experiences. For those selected, the respondent was asked to rate how helpful the support was on a five-point Likertscale (1=‘unhelpful’ to 5=‘helpful’). Table 78 displays frequency statistics for measures of experiences of a physical health problem, mental health problem or alcohol and/or other substance misuse, and Table 79 displays this by sub-samples. Table 78. Frequency statistics of health problem and help-seeking experience (five-country sample) Have you ever experienced… A major physical health problem

n 7,108

% Yes

48.3

51.7

A major mental health problem

7,108

Yes

39.1

60.9

Alcohol and/or other substance misuse

7,108

Yes

13.1

86.9

96.1

Did you receive any help for this problem? A major physical health problem

3,433

Yes No

3.9

A major mental health problem

2,781

Yes

87.7

12.3

931

Yes

41.2

58.8

Alcohol and/or other substance misuse

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Table 79. Frequency statistics of health problem and help-seeking experience (comparative sub-samples) Gender

Age-bands (years)

Sample

Males

Females

16 to 24

25 to 44

45 to 64

65+

Australia only

All other countries

Have you ever experiencedâ&#x20AC;Ś 2,838

4,270

1,576

1,639

2,546

1,347

2,276

4,832

A major physical health problem

Yes %

53.5

44.9

25.6

38.4

57.9

68.7

45.6

49.6

A major mental health problem

Yes %

34.4

42.3

47.2

47.0

37.9

22.3

46.0

35.6

Alcohol and/or other substance misuse

Yes %

18.4

9.6

9.9

17.0

14.2

10.0

13.3

13.0

Did you receive any help for this problem? n

A major physical health problem

1517

1916

404

629

1475

925

1037

2396

Yes %

96.2

96.0

93.3

94.9

96.7

97.1

97.2

95.6

975

1806

744

771

965

301

1061

1720

Yes %

87.6

87.8

76.1

91.8

92.2

91.7

89.0

87.0

523

408

156

278

362

135

302

629

Yes %

43.0

39.0

22.4

39.6

47.0

51.1

32.8

45.3

A major mental health problem n

Alcohol and/or other substance misuse

175

The results for frequency of problems linked to accessing support demonstrate that more physical health problems were reported (48.3%). Respondents were more likely to access support for physical health problems than the other two types of problems with considerably less respondents identifying seeking help related to alcohol and/or other substance misuse. Men reported higher levels of physical health problems and alcohol and/or other substance misuse than women, however they also reported a lower percentage of mental health problems. Men and women reported comparable levels of help-seeking related to all three types of health problem. Help-seeking related to alcohol and/or other substance misuse is reported as much lower than physical health and mental health problems. Young people reported the lowest level of help-seeking related to mental health problems at 76.1% in comparison to those aged 45 to 64 years (92.2%). Table 80 displays frequency statistics for help provision by type of problem. Table 80. Help provision by type of problem (five-country sample) Physical health problem

3,296 %

2,437 %

Alcohol and/or other substance misuse 384 %

Acupuncturist Alcohol and drug worker Culturally specific health worker Clergy, priest, or other religious person Counsellor Dietician/ nutritionist Family Friends General practitioner or family doctor Internet, apps, or etools Naturopath or herbalist Nurse Occupational therapist Partner (eg. girlfriend, boyfriend, spouse) Personal trainer, exercise manager, or relaxation instructor (eg. massage therapist, yoga, or meditation teacher) Pharmacist/ chemist Psychiatrist Psychologist Physiotherapist Social worker/ welfare officer Specialist doctor/ medical specialist Telephone helpline Traditional healer (eg. Qigong Master, Shaman)

11.6 2.0 1.2 3.3 9.2 12.1 24.6 19.7 77.3 19.0 8.3 22.5 9.0 18.4 11.4

4.2 3.8 1.0 6.0 46.7 5.3 29.6 32.3 61.9 21.8 4.6 8.4 5.0 21.2 6.6

2.9 51.8 1.6 5.5 35.9 4.4 22.9 31.3 29.4 11.5 3.9 5.5 2.3 14.3 5.2

19.1 6.0 9.0 20.4 4.3 69.0 2.9 2.8

9.3 42.8 52.6 3.2 10.8 9.6 8.9 2.3

5.2 19.5 25.0 <1.0 10.9 9.6 7.6 4.4

Who provided this help?

176

Mental health problem

The most common form of help provided for physical health or mental health problems was by a general practitioner. Following this, respondents identified a specialist doctor/ medical specialist for physical health problems and psychologists for mental health problems. An alcohol or drug worker was identified as the most common form of help related to alcohol and/or other substance misuse. The use of the Internet, apps and etools as a means of help is reported as low across all three problems despite the Internet being the most commonly utilised means of accessing information regarding a problem. Table 81 presents frequency statistics on the type of help provision by type of problem by gender sub-sample. It was found that men were less likely to speak to friends or family than women across all three types of problem. Men were less likely than women to utilise the Internet, apps and etools related to physical health or mental health problems, however were more likely to use this format of help for alcohol and/or other substance misuse.

177

Table 81. Help provision by type of problem (gender sub-samples) Physical health problem Who provided this help? n

Mental health problem

Alcohol and/or other substance misuse

Males

Females

Males

Females

Males

Females

1,457

6,464

4,301

6,464

4,301

6,464

Acupuncturist

9.3

13.3

3.8

4.5

1.3

5.0

Alcohol and drug worker

2.4

1.7

5.5

2.9

50.2

54.1

Culturally specific health worker

1.6

<1%

1.1

<1%

1.3

1.9

Clergy, priest, or other religious person

3.6

3.2

8.2

4.9

6.2

4.4

Counsellor

8.1

10.1

46.5

46.9

37.8

33.3

Dietician/ nutritionist

11.5

12.6

2.9

6.6

4.0

5.0

Family

20.5

27.8

24.5

32.3

21.8

24.5

Friends

15.9

22.6

26.7

35.4

28.4

35.2

General practitioner or family doctor

76.3

78.1

59.4

63.2

28.4

30.8

Internet, apps, or etools

16.0

21.4

17.1

24.4

12.4

10.1

Naturopath or herbalist

5.7

10.4

3.5

5.1

2.2

6.3

Nurse

24.2

21.1

6.8

9.3

7.6

2.5

Occupational therapist

10.2

8.0

4.6

5.2

2.7

1.9

Partner (eg. girlfriend, boyfriend, spouse)

18.0

18.7

18.6

22.5

11.6

18.2

Personal trainer, exercise manager, or relaxation instructor

9.7

12.7

4.9

7.5

5.8

4.4

Pharmacist/ chemist

18.6

19.4

9.0

9.5

5.8

4.4

Psychiatrist

6.5

5.5

41.9

43.2

17.3

22.6

178

Psychologist

8.4

9.4

48.8

54.6

23.6

27.0

Physiotherapist

18.5

21.9

3.4

3.2

<1%

Social worker/ welfare officer

4.8

4.0

10.5

10.9

10.7

11.3

Specialist doctor/ medical specialist

69.9

68.2

11.7

8.4

11.6

6.9

Telephone helpline

2.7

3.2

6.9

10.0

7.1

8.2

Traditional healer (eg. Qigong Master, Shaman)

2.7

2.8

2.2

2.3

4.4

Figure 16 provides a breakdown of helpfulness of support as rated by males who experienced a physical health problem and received support. Here, the top five ‘helpful’ supports included specialist doctors, nurses, general practitioners, partners and physiotherapists. Although the item ‘Internet, apps and etools’ received the lowest ‘helpful’ score, approximately 50% indicated they were ‘slightly helpful’ for physical health conditions. Thus, 91% of men reported that ‘Internet, apps and etools’ were slightly helpful or helpful; with none viewing these tools as ‘unhelpful’.

179

Note: Percentages only displayed for items that had over 30 respondents.

Figure 16. Percentage of male respondents who experienced physical health problem ratings of the support provided

180

Figure 17 provides a breakdown of helpfulness of support as rated by males who experienced a mental health problem and received support. Here, the top five ‘helpful’ supports included personal trainers, specialist doctors, partner, psychologist and alcohol and drug workers. Similar to physical health, the Internet, apps and etools received the lowest ‘helpful’ score. However more than half of the male respondents indicated these tools were ‘slightly helpful’ for mental health problems, and none reported them being unhelpful.

181

Note: Percentages only displayed for items that had over 30 respondents.

Figure 17. Percentage of male respondents who experienced mental health problem ratings of the support provided

182

Figure 18 provides a breakdown of helpfulness of support as rated by males who experienced alcohol and/or other substance misuse and received support. Here, the top five â&#x20AC;&#x2DC;helpfulâ&#x20AC;&#x2122; supports included friends, counsellors, alcohol and drug workers, psychiatrist and family. General practitioners received the lowest helpfulness rating scores for alcohol and/or other substance use issues.

Note: Percentages only displayed for items that had over 30 respondents.

Figure 18. Percentage of male respondents who experienced alcohol and/or other substance misuse ratings of the support provided // HELP-SEEKING SUMMARY Effective help and information seeking practices for men when it comes to their health and wellbeing is important. There can be barriers to this process. For example, Wilkins (2010) suggests that services may have inadvertently developed in such a way as to be less equipped to work with men or to be able to respond to their specific needs. Mellor et al (2012) argues that services have been set up with a focus predominantly on women and children. Staff training can also have impact, with some research reporting that there is a lack of training in responding to the needs of men and communicating with men effectively (Malcher 2005). The below results may assist to identify areas of service provision that can be improved or enhanced to suit the needs of men, and sub-groups of men, when seeking help for various health related problems.

183

Help-seeking Research has consistently reported that males are less likely to seek help than females (Yousef et al 2014; Steinfeldt et al 2009; Galdas et al 2006; Oliver et al 2005; Addis & Mahalik 2003; Courtenay 2000). When left untreated, mental health problems can often lead to further complications, such as self-medication with alcohol and/or other substances, as well as creating major barriers thriving socially, academically and vocationally (Hickie et al 2001). The results from this survey suggest that men and women do report similar levels of help-seeking behaviour; however, there are some differences in the types of help and the length of time men are willing to wait before seeking help. In addition to this, there is still a significant percentage of men and women who reported not accessing help despite identifying they have experienced one of the three types of problems. In general, help-seeking is reported as much higher for physical health problems than mental health problems or alcohol and/or other substance misuse. The lowest levels of help-seeking are seen in relation to alcohol and/or other substance misuse and the length of time individuals will wait prior to seeking help is also much longer. This is concerning given the long-term impact of these two problems and the high incidence of comorbidity between mental health problems and substance misuse. Young people particularly reported lower levels of help-seeking related to mental health problems than all other age groups. The low rate of service use by young men has been identified as an area of concern. For example, the most recent Australian Survey of Mental Health and Wellbeing indicated that over 80% of young men aged 16 to 24 years did not seek formal mental health service support despite meeting criteria for a mental health disorder in the previous 12 months (Burgess et al 2009). Types of information and help-seeking The overwhelming majority of men turn to the Internet, apps or etools for information for physical health and mental health problems as well as alcohol and/or other substance misuse. Consistently for both men and women, this was the highest rated information source. The use of the Internet, apps and etools for support, however, was reported as low across all three types of problems, despite the Internet being the most accessed form of seeking information. This indicates that although individuals seek information regarding problems on the Internet, this information does not as yet frequently lead to engagement within online interventions. New opportunities are now available to increase engagement in services, and active help-seeking behaviours through alternative means such as the use of new and emerging technologies which 184

may help to close the gap. Awareness raising of credible e-health tools is a priority. The use of the Internet, apps and etools is an emerging field of work within health services, and there is evidence to suggest that the use of online interventions can be effective in supporting health problems (Griffiths et al 2010). Men seeking information from a general practitioner was also common for physical health problems and mental health problems, however less so for alcohol and/or other substance misuse; with under a third of men seeking information from their general practitioner. General practitioners were identified as the most accessed form of help when a person indicated they had experienced a physical health or mental health problem. Alcohol or drug workers were the highest reported type of help for a person who had experienced alcohol and/or other substance misuse. A similar pattern emerged when men were asked to rate the helpfulness of the support they received; support from a general practitioner was rated highly for both physical health and mental health problems. But a low helpfulness rating was reported by men who identified as experiencing alcohol and/or other substance misuse. As general practitioners are often the port of first call for the community, more specialised general practitioner training to support men with alcohol and/or other substance misuse should be considered. Additionally, men who identified as experiencing mental health problems reported that personal training/ exercise support was helpful for their mental health. Addressing physical health needs and exercise to complement menâ&#x20AC;&#x2122;s mental health supports is another area of service provision that should be considered at greater depth. Confidence in help-seeking Age, education and working industry appears to have a small influence on confidence in help-seeking across all health conditions. Those who were older, more highly educated and working in female-dominated industries were more confident. Living in urban areas showed low positive correlations with more confidence helpseeking related to mental health than those not in major cities. This may be related to accessibility of services for those living in more rural areas, however may also be related to perspectives regarding help-seeking. Evidence focused in this area has shown that women are more likely to seek help in rural areas and that increased levels of help-seeking were associated with lower levels of perceived â&#x20AC;&#x2DC;stoicismâ&#x20AC;&#x2122; specifically in men (Judd et al 2009). There was a moderate correlation between having high resilience and happiness resulting in higher confidence in seeking help. Whilst those with high psychological

185

distress was correlated moderately with low confidence in help-seeking. These findings suggest that those who were most in need of accessing support have the lowest confidence in seeking help. This is concerning given the impact of untreated health problems; particularly for mental health, where symptoms can become more complex and result in negative coping strategies such as self-medication (Hickie et al 2001). Self-stigma, masculinity and help-seeking The results also highlight that a greater level of self-stigma associated with less confidence in help-seeking. This had low to moderate correlation strength. Stigma has been acknowledged to have an impact on seeking help for mental health distress (Vogel et al 2011). A systematic review completed by Clement et al (2014) which focused on the impact of mental health-related stigma on help-seeking found that stigma was the fourth highest ranking barrier to help-seeking with fear of disclosure as the first. Other barriers included a wish to handle the problem on oneâ&#x20AC;&#x2122;s own and a low perceived need for care which is also consistent with research by Yousaf et al (2014) related to delays in seeking support in men. Judd et al (2005) identified that levels of stigma experienced by an individual have more influence on help-seeking than levels of symptomology. This suggests that despite the level of distress an individual experiences, if there are high levels of perceived stigma, helpseeking is less likely. This evidence is consistent with the findings presented here where low confidence is associated with high self-stigma. These findings and the related research in this area suggest that interventions to increase help-seeking, must also address an individualâ&#x20AC;&#x2122;s ability to manage self-stigma. This is supported by Lee Tah et al (2014) who highlight the importance of combining public anti-stigma campaigns with interventions aimed at decreasing internalised self-stigma to support more effective help-seeking communication. The reasons for menâ&#x20AC;&#x2122;s poor engagement with mental health services are complex. Men have higher mental health stigma than women (Chandra & Minkovitz 2006; Cotton et al 2006). This is consistent with the broader help-seeking literature which lists Western norms and societal depictions of masculinity that emphasise selfreliance, stoicism and strength, as well as the desire to manage personal problems independently (Emslie et al 2006; Evans et al 2011; Moller-Leimkuhler 2002) as barriers to effective help-seeking in men. This may account for the results that show men as less likely to speak to friends or family about their health problems, which could be as a result of fear of stigma. It may also link to the high percentage of

186

respondents referring to the Internet to seek information related to health problems, a significantly higher percentage than seen in the results for asking a doctor. A recent study by Vogel et al (2011) found that men who demonstrated a higher level of masculine beliefs had less favourable views regarding seeking psychological support, however the results also indicated that self-stigma may be a better predictor of attitudes to help-seeking than masculine gender roles. Our research supports this, the current results also show that for those who identified more closely with the ‘socially connected’ latent class, higher confidence in help-seeking was seen across all health problems; although correlation strength was low compared to self-stigma across all three major health problems. In contrast, those who identified within the ‘isolated’ latent class were significantly less confident in help-seeking across all major health problems. Again this correlation strength was low compared to selfstigma. Overall, the current findings suggest that those who are less connected with others, or hold self-stigma beliefs may have less confidence help-seeking personally or may have less confidence in the health system. Stigma experiences, isolation and help-seeking are interlinked in the research. For example, individuals who experience stigma, the effects can include feelings of fear, isolation, guilt and embarrassment, and result in avoidance of help-seeking (Clement et al 2014; Corrigan 2004; Gulliver et al 2010; Yousaf et al 2014). Overall actively addressing all concerns including isolation, marginalisation and self-stigma beliefs remain important. As shown in the ‘masculinity, emotionality and social connectedness’ chapter, those who were more ‘socially connected’ were more likely to endorse items such as ‘in general, I don't like risky situations’ and ‘I tend to share my feelings’. These beliefs ultimately may explain the positive association with help-seeking confidence. For those who identified more closely as ‘isolated’, beliefs relating to more stoicism and less reliance on others were expressed. For example, people identifying with this group were more likely to endorse questions such as ’it bothers me to ask for help’. In turn, these beliefs may go some way to explaining the lower confidence in seeking help as found in this chapter. These findings provide useful information regarding the beliefs that individuals hold and how this may influence their approach to seeking help regarding health problems. Although there is evidence to suggest that masculinity does have an impact on helpseeking behaviour and the approach men take to managing their health, Gough (2006) argues that categorising ‘men’ into one group is not sufficient. Gough (2006)

187

argues that further work is required to consider the differences in age, social class and other factors that may influence how men adopt specific traits or behaviours. Addis & Mahilik (2003) support this notion and suggest that help-seeking should be understood as an interaction between the social construction of masculinity and the social psychology of giving and receiving help. Therefore, they suggest that the variability between men requires attention and how others view menâ&#x20AC;&#x2122;s help-seeking behaviour, such as close family members, should be considered. These views reflect the findings here that represent the different beliefs individuals have and how these beliefs influence help-seeking behaviour. This suggests that further work is needed to explore in more depth what influences the development and maintenance of these individual beliefs, with a view to supporting an increase in help-seeking behaviour.

188

Country Specific findings: AUSTRALIA Logistic regression analyses, following previously described techniques (see â&#x20AC;&#x2DC;mental health, wellbeing, happiness and resilienceâ&#x20AC;&#x2122; chapter) were used to understand what predicts good overall health, good mental health and high personal wellbeing in Australia. Odds ratios and 95% confidence intervals are presented in Table 82. Table 82. Odds ratios relating to overall health, mental health and personal wellbeing by demographic, employment, masculinity, emotionality and social connectedness, and health characteristics for AUSTRALIAN respondents (n=1,588) Predictor variable

Overall health (good/ very good)

OR [95% CI]

Mental health K10 (low/ very low psychological distress) OR [95% CI]

OR [95% CI]

16 to 24 25 to 44 45 to 64 65+

1.01 [0.57-1.79] 1.07 [0.65-1.76] 0.99 [0.64-1.54] 1.00

0.12 [0.06-0.25] 0.28 [0.14-0.56] 0.49 [0.26-0.92] 1.00

1.87 [0.96-3.62] 1.28 [0.73-2.25] 0.96 [0.59-1.57] 1.00

Males Females

1.45 [1.09-1.94] 1.00

1.47 [1.00-2.14] 1.00

0.69 [0.50-0.96] 1.00

Yes No

1.26 [0.82-1.95] 1.00

0.98 [0.56-1.72] 1.00

0.96 [0.60-1.55] 1.00

Heterosexual LGBTQIA

1.31 [0.93-1.83] 1.00

1.60 [1.06-2.44] 1.00

1.17 [0.79-1.73] 1.00

Demographic characteristics Age-bands (years)

PWI (>70)

Sex

Rural

Sexual orientation

189

Predictor variable

Education and employment characteristics Education Secondary or less Certificate / diploma Tertiary EET Yes NEET Work industry Male dominated Female dominated Mixed Military service Yes No Emergency service Yes No Masculinity, emotionality and social connectedness

Overall health (good/ very good)

Mental health K10 (low/ very low psychological distress)

PWI (>70)

1.24 [0.85-1.80] 0.81 [0.56-1.16] 1.00

0.65 [0.41-1.04] 0.90 [0.57-1.42] 1.00

0.80 [0.52-1.23] 0.98 [0.66-1.47] 1.00

1.18 [0.80-1.73] 1.00

0.92 [0.55-1.53] 1.00

1.98 [1.27-3.08] 1.00

0.86 [0.50-1.48] 0.96 [0.73-1.26] 1.00

1.32 [0.63-2.76] 0.75 [0.53-1.06] 1.00

1.05 [0.58-1.88] 1.30 [0.96-1.78] 1.00

0.98 [0.56-1.70] 1.00

0.89 [0.41-1.94] 1.00

0.59 [0.33-1.08] 1.00

1.11 [0.67-1.85] 1.00

1.03 [0.55-1.92] 1.00

0.92 [0.53-1.60] 1.00

0.58 [0.35-0.95] 1.13 [0.60-2.14] 0.77 [0.47-1.28]

1.58 [0.86-2.93] 0.77 [0.34-1.74] 0.36 [0.19-0.68]

2.11 [1.21-3.69] 0.75 [0.36-1.54] 0.66 [0.37-1.16]

Latent class Socially connected [C1] Self-reliant risk taking [C2] Isolated [C3]

190

Predictor variable

Overall health (good/ very good)

Mental health K10 (low/ very low psychological distress)

PWI (>70)

1.00 1.32 [0.78-2.22] 2.57 [1.53-4.33]

1.00 1.59 [0.94-2.70] 3.28 [1.96-5.50]

Low Moderate High Very high

1.00 0.44 [0.32-0.60] 0.34 [0.23-0.50] 0.16 [0.09-0.29]

1.00 0.96 [0.67-1.39] 0.51 [0.33-0.79] 0.59 [0.31-1.10]

None Possible Probable

1.00 0.97 [0.72-1.30] 0.93 [0.63-1.36]

1.00 0.81 [0.55-1.17] 0.44 [0.27-0.72]

1.00 1.05 [0.75-1.47] 1.24 [0.80-1.93]

Wellbeing, happiness and resilience PWI [wellbeing]

1.04 [1.03-1.05]

1.03 [1.02-1.05]

OHQ [happiness]

1.07 [1.01-1.14]

1.43 [1.33-1.54]

1.55 [1.45-1.65]

BRCS [resilience]

0.99 [0.94-1.05]

1.08 [1.01-1.15]

1.03 [0.97-1.10]

None Yes, not stressful Yes, stressful

1.00 1.03 [0.69-1.53] 1.11 [0.83-1.48]

1.00 1.05 [0.62-1.78] 0.58 [0.41-0.83]

1.00 1.34 [0.85-2.10] 0.59 [0.42-0.81]

Health, wellbeing, happiness and resilience Overall health Bad / very bad Moderate Good / very good K10

Substance misuse likelihood

Major life event

Note: Statistically significant items (p < 0.05) are highlighted in bold. For more conservative estimates, if the significance level could be rounded to p=0.05 items were not bolded.

191

In the Australian sub-sample, the odds of those aged between 16 to 24 years of reporting low or very low psychological distress was significantly lower than those aged 65 years and over. Men were also more likely than females to report good or very good overall health. However, when considering personal wellbeing, men had significantly lower odds of having high personal wellbeing than women. Respondents who identified as heterosexual had significantly higher odds of scoring low or very low psychological distress than those who identified as LGBTQIA. Respondents who reported that they were engaged in education, employment and training showed significantly higher odds of reporting high personal wellbeing compared to individuals not engaged in education, employment or training. Respondents who identified more closely with the ‘isolated’ latent class had much lower odds of reporting low or very low psychological distress than those who did not identify strongly with this group. For those who identified more closely with the ‘socially connected’ latent class, the odds for good personal wellbeing were high, whereas for overall health, the odds were significantly lower. There were some notable interactions between health, wellbeing and psychological distress. Those who identified as having good or very good health had significantly higher odds of reporting good mental health compared with those who reported bad or very bad overall health. In addition to this, reporting good or very good overall health resulted in significantly higher odds of having high personal wellbeing. There was a linear relationship between health and psychological distress. As the level of psychological distress increased, the odds for good overall health decreased. This pattern was also evident for wellbeing; as respondents who reported high psychological distress had significantly lower odds of reporting high personal wellbeing. Wellbeing, happiness and resilience also demonstrated a significant relationship with overall health and psychological distress. High happiness scores were associated with significantly higher odds of good overall health, mental health and wellbeing. High personal wellbeing scores resulted in higher odds of good overall health and mental health. Those who reported high resilience scores also had higher odds of good mental health than those who did not. The odds for those who met the criteria for ‘probable’ substance misuse reporting good mental health was significantly lower than those who did not have a substance misuse issue. Respondents who reported experiencing a stressful life event had lower odds of reporting good mental health and high personal wellbeing when compared to those who did not experience any life events.

192

Summary The results from the Australian sub-sample demonstrated some differences between males and females. Males were more likely to report good overall health. However, men were more likely to have lower personal wellbeing scores than women. Other large Australian studies have demonstrated that men report lower wellbeing scores (Cummings et al, 2002). As may be expected, those who identified more closely with the ‘isolated’ latent class demonstrated lower odds of reporting good mental health. As discussed in the previous chapter ‘mental health, wellbeing, happiness and resilience’, social isolation is a growing major public health concern (Holt-Lunstad et al 2015). Those who identified more strongly as ‘socially connected’ had high odds for scoring highly on the personal wellbeing index. Australian research on the personal wellbeing index has shown that people’s personal relationships are very important influences on their satisfaction with life in general and that relationship satisfaction is higher among those who are married or cohabiting than for those who are separated, divorced or have never married (Cummings et al 2002). Interestingly, the Australian sub-sample who identified more strongly as ‘socially connected’ reported lower odds of good overall health. This was not found for any other country sub-sample, nor was it found for the five-country sample.

193

Country Specific Findings: CANADA Logistic regression analyses, following previously described techniques (see â&#x20AC;&#x2DC;mental health, wellbeing, happiness and resilienceâ&#x20AC;&#x2122; chapter) were used to understand what predicts good overall health, good mental health and high personal wellbeing in Canada. Odds ratios and 95% confidence intervals are presented in Table 83. Table 83. Odds ratio relating to overall health, mental health and personal wellbeing by demographic, employment, masculinity, emotionality and social connectedness, and health characteristics of CANADIAN respondents (n=824) Predictor variable

Overall health (good/ very good)

Demographic characteristics Age-bands (years)

OR [95% CI]

Mental health K10 (low/ very low psychological distress) OR [95% CI]

PWI (>70) OR [95% CI]

16 to 24 25 to 44 45 to 64 65+

3.11 [1.45-6.68] 2.32 [1.22-4.39] 1.77 [1.11-2.81] 1.00

0.30 [0.12-0.73] 0.46 [0.22-0.98] 0.83 [0.46-1.49] 1.00

1.47 [0.58-3.70] 0.87 [0.42-1.81] 0.77 [0.45-1.31] 1.00

Males Females

1.47 [1.00-2.16] 1.00

1.28 [0.79-2.06] 1.00

0.82 [0.52-1.29] 1.00

Yes No

0.90 [0.46-1.75] 1.00

2.01 [0.84-4.84] 1.00

1.57 [0.70-3.53] 1.00

Heterosexual LGBTQIA

1.30 [0.81-2.09] 1.00

2.09 [1.20-3.65] 1.00

1.48 [0.84-2.60] 1.00

Sex

Rural

Sexual orientation

194

Predictor variable

Overall health (good/ very good)

Mental health K10 (low/ very low psychological distress)

PWI (>70)

Education and employment characteristics Education Secondary or less Certificate / Diploma Tertiary

0.70 [0.44-1.13] 0.91 [0.60-1.37] 1.00

0.60 [0.34-1.05] 0.69 [0.42-1.13] 1.00

0.52 [0.30-0.90] 0.57 [0.35-0.91] 1.00

Yes NEET

1.29 [0.85-1.95] 1.00

1.20 [0.71-2.03] 1.00

1.46 [0.89-2.42] 1.00

Male dominated Female dominated Mixed

0.95 [0.56-1.60] 1.01 [0.68-1.49] 1.00

0.87 [0.46-1.65] 1.03 [0.64-1.68] 1.00

1.22 [0.67-2.22] 1.49 [0.95-2.34] 1.00

Yes No

1.43 [0.71-2.89] 1.00

1.31 [0.53-3.28] 1.00

0.50 [0.24-1.06] 1.00

0.98 [0.48-2.00] 1.00

0.58 [0.25-1.37] 1.00

1.69 [0.72-3.97] 1.00

0.92 [0.49-1.72] 0.93 [0.41-2.13] 0.97 [0.50-1.88]

0.72 [0.33-1.56] 0.26 [0.09-0.72] 0.57 [0.25-1.26]

1.04 [0.49-2.19] 0.81 [0.31-2.12] 0.95 [0.44-2.05]

EET

Work industry

Military service

Emergency service Yes No Masculinity, emotionality and social connectedness Latent class Socially connected [C1] Self-reliant risk taking [C2] Isolated [C3]

195

Predictor variable

Overall health (good/ very good)

Health, wellbeing, happiness and resilience Overall health Bad / very bad Moderate Good / very good K10 Low Moderate High Very high Substance misuse likelihood None Possible Probable Wellbeing, happiness and resilience PWI [wellbeing]

Mental health K10 (low/ very low psychological distress)

PWI (>70)

1.00 0.92 [0.49-1.69] 2.17 [1.18-4.01]

1.00 1.77 [0.89-3.54] 3.79 [1.94-7.39]

1.00 0.30 [0.19-0.46] 0.23 [0.13-0.39] 0.17 [0.08-0.36]

1.00 0.76 [0.47-1.23] 0.56 [0.31-1.00] 0.27 [0.10-0.72]

1.00 0.74 [0.48-1.13] 0.68 [0.40-1.15]

1.00 1.09 [0.63-1.86] 0.81 [0.43-1.53]

1.00 1.37 [0.83-2.28] 0.46 [0.24-0.86]

1.03 [1.02-1.05]

1.03 [1.01-1.04]

OHQ [happiness]

1.05 [0.97-1.13]

1.34 [1.22-1.47]

1.49 [1.36-1.63]

BRCS [resilience]

1.01 [0.94-1.10]

1.02 [0.92-1.12]

1.06 [0.97-1.17]

None Yes, not stressful Yes, stressful

1.00 0.63 [0.36-1.09] 1.19 [0.79-1.79]

1.00 0.95 [0.46-1.97] 0.45 [0.29-0.71]

1.00 1.49 [0.75-2.95] 0.65 [0.41-1.04]

Major life event

Note: Statistically significant items (p < 0.05) are highlighted in bold. For more conservative estimates, if the significance level could be rounded to p=0.05 items were not bolded.

196

Young people in Canada were least likely to report good mental health compared to those aged 65 years and over. There was also a linear relationship between age and overall health. The odds of having good overall health decreased with age. Canadians aged between 16 to 24 years old were most likely to report good overall health, however all ageband differences were significant compared to those aged 65 years and older. Analysis of the regression blocks showed that across all the age-bands there was initially no significant differences in the likelihood of reporting overall good health when only accounting for demographics. However, once psychological distress, substance misuse and positive wellbeing items (personal wellbeing, happiness and resilience) were included in the analysis (i.e. controlled for) age effects for overall health became significant. Respondents who identified as heterosexual have significantly higher odds of reporting good mental health than those who identify themselves as LGBTQIA. Canadian respondents who were not tertiary educated had significantly lower odds of scoring a high personal wellbeing score in comparison to those who had completed tertiary education. Respondents who were identified within the ‘self-reliant risk taking’ latent class showed significantly lower odds of reporting good mental health. The odds for good overall health for Canadians who reported moderate, high and very high psychological distress was significantly lower compared to individuals with low distress scores. In addition to this, reporting very high psychological distress resulted in significantly lower odds of having high personal wellbeing compared to individuals with low distress scores. Canadians with high happiness ratings were significantly more likely to report good mental health and high personal wellbeing scores. High personal wellbeing scores increased the likelihood of reporting both good overall health and mental health significantly. Summary Although Canadian young people reported overall good health, they also reported experiencing higher levels of psychological distress. The block analysis clearly showed that once psychological distress was accounted for, overall health decreased with age. As discussed in the ‘mental health, wellbeing, happiness and resilience’ chapter, this demonstrates the importance of good mental health for young people’s health; and the negative impact poor mental health can have on other areas of life. This is supported by the findings that even with moderate scores of psychological distress, the odds of having good overall health were much lower. 197

In addition to this, stage or type of education had a significant impact on Canadiansâ&#x20AC;&#x2122; personal wellbeing scores, with those who had not undertaken or completed tertiary education reporting lower odds of a high personal wellbeing score in comparison to those who had. Canadian research suggests that educational progress has benefits as it can influence other wellbeing domains (Guhn et al 2010). This may be through economic returns or by subjective feelings of achievement and accomplishment. Ensuring that those who have not completed tertiary education are not marginalised educationally, economically and socially may be important to improving Canadiansâ&#x20AC;&#x2122; wellbeing.

198

Country Specific Findings: NEW ZEALAND Logistic regression analyses, following previously described techniques (see â&#x20AC;&#x2DC;mental health, wellbeing, happiness and resilienceâ&#x20AC;&#x2122; chapter) were used to understand what predicts good overall health, good mental health and high personal wellbeing in New Zealand. Odds ratios and 95% confidence intervals are presented in Table 84. Table 84. Odds ratio relating to overall health, mental health and personal wellbeing by demographic, employment, masculinity, emotionality and social connectedness, and health characteristics for NEW ZEALAND respondents (n=748) Predictor variable

Overall health (good/ very good)

OR [95% CI]

Mental health K10 (low/ very low psychological distress) OR [95% CI]

16 to 24 25 to 44 45 to 64 65+

2.25 [1.03-4.94] 1.97 [0.95-4.09] 1.92 [1.01-3.64] 1.00

0.07 [0.02-0.22] 0.28 [0.10-0.85] 0.59 [0.21-1.67] 1.00

1.45 [0.60-3.53] 1.52 [0.66-3.50] 0.81 [0.39-1.68]

Males Females

1.76 [1.15-2.69] 1.00

1.33 [0.76-2.31] 1.00

0.91 [0.56-1.47]

Yes No

0.47 [0.10-2.17] 1.00

0.35 [0.07-1.86] 1.00

1.87 [0.39-8.87]

Heterosexual LGBTQIA

1.05 [0.65-1.69] 1.00

1.72 [0.98-3.04] 1.00

1.25 [0.74-2.11]

Demographic characteristics Age-bands (years)

PWI (>70)

OR [95% CI]

Sex

Rural

Sexual orientation

199

Predictor variable

Overall health (good/ very good)

Mental health K10 (low/ very low psychological distress)

PWI (>70)

Secondary or less Certificate / diploma Tertiary

0.68 [0.42-1.12] 0.93 [0.56-1.55] 1.00

0.85 [0.47-1.54] 1.00 [0.49-2.04] 1.00

0.81 [0.47-1.39] 1.21 [0.67-2.18] 1.00

Yes NEET

0.89 [0.50-1.59] 1.00

1.66 [0.76-3.65] 1.00

1.37 [0.70-2.67] 1.00

Male dominated Female dominated Mixed

0.90 [0.40-1.98] 0.79 [0.52-1.19] 1.00

3.43 [1.09-10.75] 1.05 [0.61-1.78] 1.00

0.60 [0.25-1.43] 0.81 [0.51-1.30] 1.00

Yes No

1.12 [0.54-2.32] 1.00

0.52 [0.20-1.34] 1.00

1.32 [0.58-2.98]

0.73 [0.38-1.40] 1.00

1.41 [0.57-3.48] 1.00

1.42 [0.66-3.07] 1.00

0.94 [0.48-1.85] 2.56 [0.96-6.87] 2.18 [1.03-4.64]

1.04 [0.42-2.53] 0.37 [0.11-1.26] 0.27 [0.10-0.71]

1.77 [0.84-3.76] 0.71 [0.24-2.11] 0.29 [0.12-0.68]

Education and employment characteristics Education

EET

Work industry

Military service

Emergency service Yes No Masculinity, emotionality and social connectedness Latent class Socially connected [C1] Self-reliant risk taking [C2] Isolated [C3]

200

Predictor variable

Overall health (good/ very good)

Mental health K10 (low/ very low psychological distress)

PWI (>70)

1.00 2.56 [1.18-5.54] 6.69 [3.02-14.81]

1.00 1.35 [0.61-2.99] 2.54 [1.16-5.57]

1.00 0.71 [0.44-1.14] 0.29 [0.15-0.55] 0.21 [0.09-0.47]

1.00 0.45 [0.27-0.75] 0.50 [0.24-1.01] 0.54 [0.22-1.35]

1.00 1.44 [0.91-2.28] 0.88 [0.49-1.57]

1.00 0.83 [0.44-1.53] 0.38 [0.19-0.77]

1.00 0.82 [0.50-1.37] 0.80 [0.42-1.52]

1.04 [1.02-1.06] 1.20 [1.09-1.31] 0.96 [0.88-1.05]

1.02 [1.00-1.04] 1.33 [1.19-1.49] 1.12 [1.01-1.24]

1.56 [1.41-1.71] 1.10 [1.01-1.21]

1.00 0.97 [0.55-1.70] 1.25 [0.81-1.92]

1.00 1.08 [0.50-2.34] 0.60 [0.36-1.01]

1.00 0.71 [0.38-1.31] 0.82 [0.51-1.32]

Note: Statistically significant items (p < 0.05) are highlighted in bold. For more conservative estimates, if the significance level could be rounded to p=0.05 items were not bolded.

201

In New Zealand, men had significantly higher odds of reporting good overall health compared to women. There was also a linear relationship between age and overall health. The odds of having good overall health decreased with age. New Zealanders aged between 16 to 24 years old were most likely to report good overall health. Analysis of the regression blocks showed that across all the age-bands this effect was initially reversed, in that young people were actually significantly less likely to report good overall health when only accounting for demographics. However, once psychological distress, substance misuse and positive wellbeing items (personal wellbeing, happiness and resilience) were included in the analysis (i.e. controlled for) age effects for overall health reversed; with younger people reporting significantly better health than those aged 65 years and over. Younger people aged between 16 and 44 years were less likely to report good mental health compared to those aged 65 years and older. New Zealanders working in a male-dominated workplace reported significantly higher odds of good mental health. New Zealanders who identified more closely with the ‘isolated’ latent class showed significantly higher odds of reporting good overall health; however, were significantly less likely to report good mental health and high personal wellbeing. People with probable substance misuse were less likely to report good mental health. New Zealanders who reported moderate to very good overall health had significantly higher odds of good mental health. Whilst high to very high psychological distress scores were associated with very low likelihood of good overall health, reporting good overall health increased the likelihood of high personal wellbeing. New Zealanders with moderate psychological distress had low odds of scoring highly on the personal wellbeing index. In New Zealand, reporting high levels of happiness was associated with a greater likelihood of good overall health, good mental health and high personal wellbeing scores. People with high resilience scores were also more likely to report good mental health and high personal wellbeing. High wellbeing scores increased the odds significantly for reporting good overall health, and good mental health. Summary In New Zealand, young people had high odds of reporting good overall health, however were significantly less likely to report good mental health. The block analysis clearly showed that once factors such as psychological distress were accounted for, overall health decreased with age. As discussed in the ‘mental health, wellbeing, happiness and resilience’ chapter

202

and the Canada specific section of this Report, this demonstrates the importance of good mental health for young peopleâ&#x20AC;&#x2122;s overall health. Interestingly, working in a male-dominated work environment increased the odds of reporting good mental health. New Zealand was the only country in which this relationship was found in the results.

203

Country Specific Findings: UNITED KINGDOM Logistic regression analyses, following previously described techniques (see â&#x20AC;&#x2DC;mental health, wellbeing, happiness and resilienceâ&#x20AC;&#x2122; chapter) were used to understand what predicts good overall health, good mental health and high personal wellbeing in the UK. Odds ratios and 95% confidence intervals are presented in Table 85. Table 85. Odds ratios relating to overall health, mental health and personal wellbeing by demographic, employment, masculinity, emotionality and social connectedness, and health characteristics for UNITED KINGDOM respondents (n=865) Predictor variable

Overall health (good/ very good)

PWI (>70)

OR [95% CI]

Mental health K10 (low/ very low psychological distress) OR [95% CI]

OR [95% CI]

16 to 24 25 to 44 45 to 64 65+

0.77 [0.34-1.74] 1.02 [0.49-2.12] 0.52 [0.28-0.96] 1.00

0.26 [0.09-0.73] 0.42 [0.16-1.11] 0.57 [0.24-1.34] 1.00

3.03 [1.20-7.66] 1.10 [0.50-2.45] 1.05 [0.53-2.09] 1.00

Males Females

1.23 [0.84-1.80] 1.00

1.42 [0.87-2.31] 1.00

0.77 [0.50-1.17] 1.00

Yes No

1.47 [0.65-3.32] 1.00

0.68 [0.22-2.10] 1.00

1.11 [0.43-2.90] 1.00

Heterosexual LGBTQIA

0.73 [0.46-1.17] 1.00

1.81 [1.03-3.19] 1.00

1.50 [0.86-2.60] 1.00

Demographic characteristics Age-bands (years)

Sex

Rural

Sexual orientation

204

Predictor variable

Overall health (good/ very good)

Mental health K10 (low/ very low psychological distress)

PWI (>70)

1.78 [1.04-3.03] 1.76 [1.09-2.83] 1.00

0.60 [0.31-1.16] 0.74 [0.40-1.38] 1.00

1.09 [0.58-2.05] 1.17 [0.69-1.97] 1.00

4.44 [2.62-7.52] 1.00

1.49 [0.74-2.98] 1.00

1.30 [0.71-2.39] 1.00

0.30 [0.13-0.69] 0.86 [0.58-1.27] 1.00

2.01 [0.66-6.17] 1.60 [0.97-2.63] 1.00

1.83 [0.71-4.71] 1.33 [0.86-2.05] 1.00

1.98 [0.85-4.64] 1.00

0.62 [0.24-1.57] 1.00

0.45 [0.19-1.08] 1.00

0.42 [0.22-0.80] 1.00

1.44 [0.60-3.48] 1.00

0.56 [0.27-1.14] 1.00

0.67 [0.35-1.26] 0.86 [0.37-2.04] 1.04 [0.51-2.12]

0.65 [0.29-1.46] 0.23 [0.08-0.70] 0.36 [0.15-0.91]

1.62 [0.80-3.30] 0.65 [0.25-1.71] 0.18 [0.08-0.39]

205

Predictor variable

Overall health (good/ very good)

Mental health K10 (low/ very low psychological distress)

PWI (>70)

1.00 1.03 [0.52-2.03] 1.79 [0.92-3.49]

1.00 3.24 [1.49-7.06] 4.03 [1.90-8.58]

Low Moderate High Very high Substance misuse likelihood None Possible Probable Wellbeing, happiness and resilience PWI [wellbeing]

1.00 0.27 [0.17-0.42] 0.23 [0.12-0.41] 0.22 [0.10-0.46]

1.00 0.87 [0.54-1.41] 0.24 [0.12-0.48] 0.54 [0.22-1.33]

1.00 0.95 [0.63-1.43] 0.74 [0.43-1.27]

1.00 0.92 [0.54-1.56] 0.71 [0.36-1.37]

1.00 0.95 [0.60-1.51] 0.76 [0.41-1.42]

1.03 [1.02-1.05]

1.05 [1.03-1.07]

OHQ [happiness]

1.14 [1.05-1.24]

1.42 [1.29-1.57]

1.50 [1.37-1.65]

BRCS [resilience]

1.01 [0.94-1.09]

1.01 [0.92-1.11]

1.11 [1.02-1.22]

None Yes, not stressful Yes, stressful

1.00 1.21 [0.71-2.04] 1.06 [0.67-1.67]

1.00 0.76 [0.39-1.49] 0.33 [0.19-0.56]

1.00 0.83 [0.48-1.45] 0.83 [0.49-1.42]

Health, wellbeing, happiness and resilience Overall health Bad / very bad Moderate Good / very good K10

Major Life Event

Note: Statistically significant items (p < 0.05) are highlighted in bold. For more conservative estimates, if the significance level could be rounded to p=0.05 items were not bolded.

206

People in the UK aged 45 to 64 years had the lowest odds of reporting good overall health; this was significant when compared to the age group of people 65 years and older. As with other countries, there was a linear relationship between age and psychological distress. Those aged between 16 and 24 years had the lowest odds of reporting good mental health, which was significant when compared to people aged 65 years and older. Respondents aged 16 to 24 years had the highest odds for reporting high personal wellbeing scores. Analysis of the regression blocks showed that across all the age-bands this effect was initially reversed, in that younger age groups (between 16 and 44 years old) were actually significantly less likely to report high wellbeing scores when only accounting for demographics. However, once psychological distress, overall health, substance misuse and other positive wellbeing items (happiness and resilience) were included in the analysis (i.e. controlled for) age effects for wellbeing reversed; with younger people (16 to 24 years old) reporting significantly better wellbeing than those aged over 65 years. Respondents who identified as heterosexual had significantly higher odds of reporting good mental health than those who identified with LGBTQIA groups. When considering the impact of education, people who had not completed tertiary education were significantly more likely to report good overall health in comparison to those who were in, or had completed, tertiary education. Respondents engaged in education, employment and training had significantly higher odds of reporting good overall health in comparison to those not engaged in education, employment or training. The type of work industry also showed significant results in the UK. People from male-dominated work industries were significantly less likely to report good overall health compared to industries of a mixed classification. People that experienced a stressful life event were less likely to report good mental health than those who had not experienced any life event in the past 12 months. Those respondents who identified more strongly with the ‘isolated’ latent class had significantly lower odds of reporting either good mental health or high personal wellbeing. Those who identified more closely with ‘self-reliant risk taking’ beliefs had significantly lower odds of reporting good mental health. People from the UK who reported moderate to very good overall health were more likely to have high personal wellbeing scores. The odds of reporting good overall health decreased significantly as levels of psychological distress increased. Individuals who reported high psychological distress had significantly lower odds of having a high personal wellbeing score. 207

Reporting higher levels of happiness significantly increased the odds of reporting good overall health, good mental health and high personal wellbeing. High personal wellbeing also increased the odds of reporting overall good health and good mental health. Summary As with other countries, a linear relationship was shown between age and levels of psychological distress, with those who were 16 to 24 years old being most likely to report higher psychological distress. The results also highlight the importance mental health plays in an individualâ&#x20AC;&#x2122;s perceptions of health, an effect that was particularly pronounced for young people. For the whole sub-sample, there was a strong relationship between reporting overall good health, wellbeing, and psychological distress. Good overall health was a predictor for low psychological distress and high personal wellbeing. In addition to this, happiness was a strong indicator for good health and mental health. Interestingly, the UK was the only country where working in a male-dominated environment results in low odds of good overall health. However, those engaged in any form of education, employment or training show much higher odds of reporting good overall health. Research suggests that currently it is the worst labour market in decades in the UK and that young people are particularly vulnerable as they have poorer work prospects (Eurofound 2012; Goldman-Mellor et al 2016; Shierholz et al 2012). When this is coupled with the above findings relating to age and poorer mental health; Goldman-Mellor et al (2016) argue that alongside skills, work-related self-perceptions and mental health problems require intervention.

208

Country Specific Findings: UNITED STATES Logistic regression analyses, following previously described techniques (see â&#x20AC;&#x2DC;mental health, wellbeing, happiness and resilienceâ&#x20AC;&#x2122; chapter) were used to understand what predicts good overall health, good mental health and high personal wellbeing in the US. Odds ratios and 95% confidence intervals are presented in Table 86.

Table 86. Odds ratios relating to overall health, mental health and personal wellbeing by demographic, employment, masculinity, emotionality and social connectedness, and health characteristics for UNITED STATES respondents (n=697) Predictor variable

Overall health (good/ very good)

OR [95% CI]

Mental health K10 (low/ very low psychological distress) OR [95% CI]

OR [95% CI]

16 to 24 25 to 44 45 to 64 65+

0.96 [0.45-2.02] 1.48 [0.68-3.18] 1.06 [0.62-1.82] 1.00

0.20 [0.08-0.52] 0.41 [0.15-1.09] 0.88 [0.43-1.80] 1.00

2.85 [1.11-7.29] 1.50 [0.61-3.71] 0.77 [0.40-1.48] 1.00

Males Females

1.18 [0.76-1.82] 1.00

0.92 [0.53-1.59] 1.00

1.13 [0.67-1.93] 1.00

Yes No

0.56 [0.28-1.14] 1.00

0.67 [0.27-1.70] 1.00

1.42 [0.59-3.43] 1.00

Heterosexual LGBTQIA

1.22 [0.73-2.04] 1.00

1.52 [0.82-2.82] 1.00

1.64 [0.86-3.12] 1.00

Demographic characteristics Age-bands (years)

PWI (>70)

Sex

Rural

Sexual orientation

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Predictor variable

Overall health (good/ very good)

Mental health K10 (low/ very low psychological distress)

PWI (>70)

0.99 [0.50-1.96] 0.98 [0.50-1.91] 1.00

0.53 [0.24-1.20] 0.46 [0.20-1.03] 1.00

1.00 [0.43-2.31] 0.42 [0.18-0.98] 1.00

2.06 [1.23-3.47] 1.00

1.45 [0.73-2.87] 1.00

1.25 [0.66-2.37] 1.00

1.68 [0.79-3.58] 1.03 [0.68-1.57] 1.00

1.64 [0.63-4.26] 0.71 [0.42-1.22] 1.00

1.39 [0.58-3.31] 1.88 [1.13-3.14] 1.00

0.75 [0.40-1.43] 1.00

0.58 [0.26-1.30] 1.00

1.13 [0.53-2.42] 1.00

0.75 [0.35-1.59] 1.00

1.35 [0.49-3.71] 1.00

0.76 [0.31-1.86] 1.00

1.04 [0.50-2.15] 0.57 [0.22-1.51] 1.16 [0.52-2.59]

0.67 [0.27-1.68] 0.65 [0.18-2.29] 0.30 [0.10-0.86]

1.58 [0.64-3.94] 0.56 [0.17-1.87] 0.51 [0.19-1.34]

Latent class Socially connected [C1] Self-reliant risk taking [C2] Isolated [C3]

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Predictor variable

Overall health (good/ very good)

Mental health K10 (low/ very low psychological distress)

PWI (>70)

1.00 3.19 [1.52-6.70] 3.74 [1.80-7.79]

1.00 0.65 [0.24-1.76] 2.03 [0.79-5.21]

Low Moderate High Very high Substance misuse likelihood None Possible Probable Wellbeing, happiness and resilience PWI [wellbeing]

1.00 0.58 [0.36-0.94] 0.44 [0.23-0.83] 0.54 [0.23-1.28]

1.00 0.73 [0.42-1.26] 0.23 [0.10-0.50] 0.26 [0.08-0.91]

1.00 0.86 [0.52-1.41] 0.91 [0.48-1.73]

1.00 0.72 [0.39-1.33] 0.94 [0.42-2.10]

1.00 0.86 [0.47-1.56] 0.49 [0.22-1.09]

1.04 [1.03-1.06]

1.05 [1.03-1.07]

OHQ [happiness]

1.08 [0.98-1.18]

1.31 [1.17-1.46]

1.65 [1.46-1.86]

BRCS [resilience]

1.06 [0.97-1.16]

0.94 [0.85-1.06]

1.10 [0.98-1.23]

None Yes, not stressful Yes, stressful

1.00 0.93 [0.52-1.66] 0.89 [0.57-1.39]

1.00 1.11 [0.52-2.41] 0.90 [0.52-1.55]

1.00 0.80 [0.39-1.62] 0.64 [0.38-1.09]

Health, wellbeing, happiness and resilience Overall health Bad / very bad Moderate Good / very good K10

Major life event

Note: Statistically significant items (p < 0.05) are highlighted in bold. For more conservative estimates, if the significance level could be rounded to p=0.05 items were not bolded.

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Results from the US demonstrated a linear relationship between age and psychological distress. Those aged between 16 and 24 years had the lowest odds for reporting good mental health, which was significant when compared to people aged 65 years and over. Respondents aged 16 to 24 years had the highest odds of reporting high personal wellbeing scores. Analysis of the regression blocks showed that across all the age-bands there was initially no significant differences in the likelihood of reporting high personal wellbeing for young people when only accounting for demographics. However, once psychological distress, overall health, substance misuse and other positive wellbeing items (happiness and resilience) were included in the analysis (i.e. controlled for) age effects for wellbeing emerged; with younger people (aged 16 to 24 years) reporting significantly better wellbeing than those aged 65 years and older. Individuals who were completing or had completed education at a certificate/ diploma level had significantly lower odds of scoring high on personal wellbeing. In addition to this, respondents who were engaged in education, employment and training had significantly higher odds of having good overall health in comparison to those not engaged in education, employment or training. Individuals working within a female-dominated industry had significantly higher odds of high personal wellbeing scores in comparison to those working in a mixed environment. Those respondents who identified more closely with the â&#x20AC;&#x2DC;isolatedâ&#x20AC;&#x2122; latent class had significantly lower odds of reporting good mental health. Individuals reporting moderate to very good overall health were more likely to report good mental health. People reporting moderate or high psychological distress were less likely to report good health. Respondents reporting high or very high psychological distress had significantly lower odds of high personal wellbeing. A higher level of happiness was associated with higher odds of reporting good mental health and high personal wellbeing. High personal wellbeing also increases the odds of overall good/ very good health and low psychological distress. Summary The results for the US, as with other countries, show that 16 to 24 years old were the most likely to report higher psychological distress. Additionally, psychological distress influenced this age groups rating of overall health significantly. Again the results also highlight the crucial role that mental health plays in a young personâ&#x20AC;&#x2122;s overall view on their health. For the whole US sub-sample, there was interplay between reporting good health, mental health and wellbeing. Level of happiness was also a strong indicator for good mental health and wellbeing.

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The results regarding the impact of work industry are interesting, and as with other countries raise questions about the impact of female- or male-dominated work industries.

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// COUNTRY SPECIFIC CHAPTER SUMMARY Recent evidence has seen an increase in the psychological distress of young people (Burns et al 2013; Mission Australia 2015). As can be seen in the results above, younger people consistently have the lowest odds for reporting low psychological distress, even when the odds for reporting overall good health were high. This is a significant concern regarding the long-term impact of high levels of psychological distress in young people and the development of longer-term mental health problems. The evidence consistently shows that mental health problems most likely commence during adolescence and continue into late-life (Costello et al 2006; Government of Canada 2006). In four of the five countries, holding beliefs more closely in line with the ‘isolated’ latent class was associated with poorer mental health. As discussed in the ‘mental health, wellbeing, happiness and resilience’ chapter, marginalisation is an important issue globally. Clearly support is needed within each country for those who are socially isolated and marginalised. As highlighted by Holt-Lunstad et al (2015), social isolation is a growing major public health concern. When considering each item in the below discussion, we can see how marginalisation and social isolation are underlying themes across a number of variables and contexts that ultimately impact health, mental health and wellbeing. In the five-country sample, the male sub-sample, the Australian and the UK subsamples, LGBTQIA people and men reported worse mental health. The body of research has highlighted numerous health inequalities for lesbian, gay, bisexual and transgender populations, particularly relating to mental health (Bostwick et al 2010; King et al 2008; McCabe et al 2009; Meyer 2003). Some researchers suggest institutional and interpersonal discrimination that sexual minorities face as a potential explanation for these health disparities (Hatzenbuehler et al 2009; Mays & Cochran 2001; Meyer 2003). Probable substance use resulted in a reduced likelihood of reporting low or very low psychological distress across all countries and these findings are very evident in the male only sub-sample. Substance use as an issue has raised a number of interesting findings throughout this Report. The use of alcohol and/or other substances appear to be of concern to individuals through their identification as major health problems, however they are also consistently used as coping strategies. There is clear evidence regarding the detrimental impact of alcohol and/or other substances on health in

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general. Men reported higher levels of alcohol and/or other substance misuse than women and also a higher percentage of men reported using other substances whilst consuming alcohol. The relationship people have with alcohol as a substance is clearly conflicted, which raises an interesting question regarding motivation to consume or not consume alcohol, related to perceived impact on health. Individuals and specifically men may require more information to enable them to weigh up the cost and benefit of consuming substances, particularly when used as a coping strategy. Across all countries, there appears to be an association between some levels of education and low odds for reporting low psychological distress. For young people this may reflect research that suggests that some experiences at school, such as the pressure of exams, can increase psychological distress (Perry et al 2015; Banks & Smyth 2015). Mission Australia’s Youth Survey (2015) also highlighted coping with stress related to school and study as two of the three items young people are most concerned about. Findings from the Young and Well National Surveys also found that young men in particular are facing high levels of stress, with 50% of young men reporting stress as their main concern (Burns et al 2013). The impact of increased psychological distress and stress within this age group has also been shown to increase the risk of mental health symptoms, in particular depression (Moksnes et al 2016; Perry et al 2015). For people who are not still at school, tertiary education can influence other wellbeing domains (Guhn et al 2010). This may be through economic returns or by subjective feelings of achievement and accomplishment. As highlighted previously, ensuring that those who have not completed tertiary education are not marginalised educationally, economically and socially may be important to improving men’s wellbeing. In four of the five countries, men had higher odds than women for good overall health and low psychological distress, however had low odds of reporting a high personal wellbeing score. This may suggest that although men rate their physical and mental health more positively, their wellbeing was more likely to be reported as low. These findings are similar to those from other research whereby young men report good health overall, however in contrast show higher levels of distress and suicide risk (Burns et al 2013). These findings could be related to different perceptions of health and perceived self-stigma related to defining oneself as ‘unhealthy’. This could also be related to the broader aspects covered within the Personal Wellbeing Index including housing, relationships and achievements. Typically, men do report worse personal wellbeing (Cummings et al 2002). Dolan, Peasgood and White (2008) suggest that individual assessment is not enough due to the number of variables that can influence wellbeing

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such as income and other family members. For example, commuting for one family member may be perceived as causing negative wellbeing, however this may result in higher income and better housing which may create more positive wellbeing in another family member. Therefore, although men may report feeling healthy in general, they may have concerns regarding certain aspects of their life and as a result report low levels of personal wellbeing. The results here may also relate to the measure itself and the questions asked. The issues raised within the Personal Wellbeing Index may be perceived by men as more socially acceptable to answer, therefore leading to more accurate reporting. Although these findings relating to each country are interesting, there may be differences in the sampling that took place within each country, which impact results. This could explain some inconsistencies in the results; for example, within the results for Australia, those identified as ‘socially connected’ reported very low odds for good overall health despite increased odds for high wellbeing. In New Zealand, those who identify with the ‘isolated’ latent class had higher odds for good overall health. There is a possibility that such nuances may be due to Type 1 error, that is, incorrectly rejecting the null hypothesis. These potential biases are discussed in further detail in the following limitations chapter, particularly as smaller cell sizes result in reduced reliability. Overall, we suggest that the five-country sample results and male sub-sample results which are presented in the earlier ‘mental health, wellbeing, happiness and resilience’ chapter are utilised, as they provide a much clearer picture of the interactions between specific variables, and health, mental health and wellbeing outcomes.

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Limitations This is, from what we understand, the largest survey of men’s health to date in terms of size and scope. However, the research is not without its limitations. Although targeted recruitment across gender and age was undertaken, this survey is a non-epidemiological sample. Findings must be interpreted with this in mind. This convenience sample from a cross-section of the community means that we can see associations between two pieces of data but we cannot infer causality. The use of an online survey can result in sampling bias. For example, people without ease of access to the Internet will not be represented. The use of social media and snowballing as a recruitment tool does mean that those who do not actively use social media may not have been exposed to the study. Additionally, using research networks (through e-mailouts, news releases and also social media such as Twitter and Instagram) may bias the research towards people who work in health industries and people who have a specific interest in health-related topics, such as those with a lived experience of a health condition. Previous research has reported that people are more likely to participate in a survey if the subject matter interests them (Borsch-Supan et al 2004). In this sample, for example, approximately 39% of individuals reported low levels of psychological distress. This is low; with national data estimates indicating that this figure should be at 70% (eg. ABS 2011, 2012). This could be attributed to ‘avidity bias’; whereby individuals with a greater interest in, or experience with, a survey topic are more likely to respond (see Ethier et al 2000). In contrast, online approaches have been suggested to reduce bias in response to sensitive or stigmatising topics (Ramo et al 2000; Temple & Brown 2011). This is due to people being more willing to share sensitive information online. This has also been reported when compared to telephone and face-to-face interviews, which has been attributed to increased anonymity and social distance (Crutzen & Göritz 2010; Newman et al 2002). Both these factors need to be considered when interpreting results of online surveys and also other types of surveys. Overall, this research does not claim to report population prevalence rates. However, we suggest interactions with other variables are the findings of importance. Research had also reported that online samples have been found to be biased towards those with a higher educational attainment, those who are younger, and respondents who are female (Casler et al 2013). In line with other research studies, more women participated in this survey. However, compared with other survey research using social

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media recruitment methods (Casler et al 2013), participation of men in this sample was much higher, which indicates some targeting success. The spread of participating age groups also was reasonable; with the largest proportion of respondents in the fivecountry sample coming from the 45 to 64 year age bracket. Additionally, traditionally under-represented groups such as those living outside urban areas and people from an Indigenous culture were well represented in this survey. Another challenge that can be associated with survey studies, outside already noted limitations including the use of voluntary samples and the potential for non-response bias, is a reliance on self-report. Social desirability bias or accuracy of reported practices may be an issue. However, internal and construct validity for items were assessed and reported. We also used measures that have been tested for reliability and validity across general populations internationally. For example, the Kessler Psychological Distress Scale (or K10) is the standard tool used in Australia’s National Survey of Mental Health and Wellbeing (Burgess et al 2009) and is used widely in international studies. The exploratory nature of the research may also increase risk of ‘type I error’, that is, the incorrect rejection of a true null hypothesis. To limit ecological fallacy (i.e. make conclusions about individuals based only on analyses of group data) that comparing countries could create, our analyses generally look at aggregated country data. This allows us to look at a global picture, but as a consequence we do not present statistical comparisons between countries. Rather than comparing countries directly, we present the data for each country in key areas (overall health, mental health and wellbeing) by key demographic and biographic variables. We encourage each country to use this country specific data to generate their own unique (and novel) approaches to addressing men’s health, mental health and wellbeing needs at a local level. The strength of this research lies in large numbers of respondents and the breadth of questions asked. These top line results reported here will be supplemented by further research that drills down further into

specific areas of masculinity, social

connectedness, health, mental health and wellbeing, help-seeking and stigma. Further research will stem from this large dataset and will verify these top-line findings using additional analytic tools.

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Tables and Figures // LIST OF TABLES Table 1. Sample and eligibility by gender and by country ........................................................................ 33 Table 2. Basic demographics (age and gender) for each country ........................................................... 34 Table 3. Ethnic background of Australian respondents ............................................................................ 35 Table 4. Ethnic background of Canadian respondents............................................................................. 35 Table 5. Ethnic background of New Zealand respondents ....................................................................... 36 Table 6. Ethnic background of United Kingdom respondents .................................................................. 36 Table 7. Ethnic background of respondents from the United States ........................................................ 37 Table 8. Sample characteristics ................................................................................................................ 39 Table 9. Perceptions of major health problems faced by men aged 16 to 39 years (comparative subsamples) .................................................................................................................................................... 43 Table 10. Perceptions of major health problems faced by men aged 40 years and over (comparative sub-samples) ............................................................................................................................................. 45 Table 11. Perceptions of major mental health problems faced by men aged 16 to 39 years (comparative sub-samples) ............................................................................................................................................. 49 Table 12. Perceptions of major mental health problems faced by men aged 40 years and over (comparative sub-samples) ....................................................................................................................... 51 Table 13. Masculinity, emotionality and social connectedness scores (five-country sample) ................. 59 Table 14. Masculinity, emotional empathy and social connectedness scores (comparative sub-samples) ................................................................................................................................................................... 60 Table 15. Exploratory latent structure of CMNI-22 and its optimal three-class solution (five-country sample; n=8,201) ....................................................................................................................................... 62 Table 16. Mean loadings for the optimal CMNI-22 three-class solution for each latent class (comparative sub-samples) ............................................................................................................................................. 64 Table 17. Effect size of average mean differences (Cohen’s d; comparative sub-samples) ................... 64 Table 18. Descriptive statistics for measured variables by location and education (five-country sample) ................................................................................................................................................................... 65 Table 19. Descriptives and Cohen’s d statistics for employment and education types (five-country sample) ...................................................................................................................................................... 65 Table 20. Descriptives and Cohen’s d statistics for military service and type of employment (five-country sample) ...................................................................................................................................................... 66 Table 21. Frequency statistics for overall health ratings (five-country sample) ....................................... 73 Table 22. Frequency statistics for overall health rating (comparative sub-samples) ............................... 74 Table 23. Descriptive statistics for ‘days out of role’ (five-country sample) ............................................. 75 Table 24. Frequency statistics for ‘days out of role’ (five-country sample) .............................................. 75 Table 25. Frequency statistics for ‘days out of role’ (comparative sub-samples) .................................... 76 Table 26. Descriptive statistics for psychological distress (K10; five-country sample) ............................ 78 Table 27. Frequency statistics for psychological distress (K10), suicidal ideation (PSFS) and self-harm measures (five-country sample) ................................................................................................................ 79 Table 28. Frequency statistics for psychological distress (K10), suicidal ideation (PSFS) and self-harm (comparative sub-samples) ....................................................................................................................... 80 Table 29. BDQ ‘days out of role’ by psychological distress (K10; five-country sample) .......................... 81

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Table 30. Descriptive statistics for wellbeing (PWI), happiness (OHQ) and resilience (BRCS) variables (five-country sample) ................................................................................................................................. 84 Table 31. Frequency statistics for wellbeing (PWI), happiness (OHQ) and resilience (BRCS) variables (comparative sub-samples) ....................................................................................................................... 85 Table 32. Odds ratio relating to overall health, mental health and wellbeing by demographic, employment, masculinity, emotionality and social connectedness, and health characteristics (fivecountry sample; n=4,722).......................................................................................................................... 88 Table 33. Odds ratio relating to overall health, mental health and wellbeing by demographic, employment, masculinity, emotionality and social connectedness, and health characteristics (male subsample; n=1,940) ....................................................................................................................................... 92 Table 34. Frequency statistics of suicidal ideation (PSFS) reported by men in relation to major life event over the past 12 months and perceived experience of this event (male sub-sample) ........................... 102 Table 35. Frequency statistics of suicidal ideation (PSFS) reported by men after a major life event or no major life event (male sub-sample) ......................................................................................................... 103 Table 36. Frequency statistics for methods of coping with “stressful” major life events based on number of life events experienced (five-country sample and male sub-sample) ................................................ 104 Table 37. Frequency and experience of major life events, and response to experiencing stress (comparative sub-samples) ..................................................................................................................... 105 Table 38. Male responses based on stressful life event (male sub-sample) ......................................... 107 Table 39. Frequency statistics for measures of physical activity (five-country sample) ........................ 111 Table 40. Frequency statistics for physical activity (comparative sub-samples).................................... 112 Table 41. Pearson correlations of men’s physical activity with health and wellbeing measures and masculine conformity/ social connectedness latent classes (male sub-sample) ................................... 113 Table 42. Frequency statistics for measures of sleep (five-country sample) ......................................... 115 Table 43. Frequency statistics for measures of sleep (comparative sub-samples) ............................... 116 Table 44. Pearson correlations of men’s sleep quality and late night Internet use with health and wellbeing measures and masculine conformity/ social connectedness latent classes (male sub-sample) ................................................................................................................................................................. 119 Table 45. Frequency statistics for measures of diet choices (five-country sample) .............................. 122 Table 46. Frequency statistics for measures of dieting behaviour (comparative sub-samples) ............ 123 Table 47. Pearson correlations of men’s diet choices with health and wellbeing measures and masculine conformity/ social connectedness latent classes (male sub-sample) ................................... 126 Table 48. Frequency statistics for body mass index (BMI), self-evaluation of weight and dieting behaviour (five-country sample) .............................................................................................................. 128 Table 49. Frequency statistics for BMI, self-evaluation of weight and dieting behaviour (comparative sub-samples) ........................................................................................................................................... 129 Table 50. Frequency statistics for measures of body image distress (five-country sample) ................. 132 Table 51. Frequency statistics for measures of dieting behaviour and body image (comparative subsamples) .................................................................................................................................................. 133 Table 52. Pearson correlations of body image items with health and wellbeing measures and masculine conformity/ social connectedness latent classes (male sub-sample)..................................................... 135 Table 53. Frequency statistics for measures of weight training (five-country sample) .......................... 137 Table 54. Frequency statistics for weight lifting training (comparative sub-samples) ............................ 137 Table 55. Life time substance use (five-country sample) ....................................................................... 139 Table 56. Frequency statistics of recent alcohol, tobacco and/or other substance use and behaviours for respondents who had indicated recent use (five-country sample) ......................................................... 140

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Table 57. Likelihood of substance misuse (five-country sample) ........................................................... 141 Table 58. Frequency statistics for life-time substance use (comparative sub-samples)........................ 142 Table 59. Frequency statistics for recent alcohol and tobacco use for respondents who had indicated some life-time use (comparative sub-samples) ...................................................................................... 143 Table 60. Substance use behaviours and reasons for use of respondents who had indicated some lifetime use (comparative sub-samples) ...................................................................................................... 144 Table 61. Likelihood of alcohol and/or other substance misuse (comparative sub-samples) ............... 144 Table 62. Frequency statistics for measures of gambling (five-country sample) ................................... 147 Table 63. Frequency statistics for measures of gambling (comparative sub-samples) ......................... 148 Table 64. Stigma and discrimination ratings for each randomised condition (physical health problem, mental health problem or alcohol and/or other substance misuse; five-country sample) ...................... 152 Table 65. Linear regression: standardised demographic/ biographic beta values for a physical health problem (PHP) and mental health problem (MHP) by public-stigma factors (five-country sample) ...... 155 Table 66. Linear regression: standardised demographic/ biographic beta values for alcohol and/or other substance misuse stigma factors (five-country sample) ......................................................................... 157 Table 67. Frequency statistics of self-stigma ratings (comparative sub-samples) ................................ 159 Table 68. Linear regression: standardised demographic/ biographic beta values for self-stigma (five country sample and male sub-sample) ................................................................................................... 161 Table 69. Frequency statistics for measures of information and help-seeking for major health problems (five-country sample) ............................................................................................................................... 166 Table 70. Frequency statistics for measures of information and help-seeking for major health problems (gender sub-samples) ............................................................................................................................. 166 Table 71. Correlations of demographic/biographic characteristics and confidence help-seeking for physical health, mental health or alcohol and/or substance mis/use issues (five-country sample) ....... 167 Table 72. Correlations of masculinity and stigma items with confidence help-seeking for physical health, mental health or alcohol and/or other substance mis/use issues (five-country and male sub-sample) 168 Table 73. Correlations of health and mental health related items with confidence help-seeking for physical health, mental health or alcohol and/or other substance mis/use issues (five-country and male sub-sample) ............................................................................................................................................. 170 Table 74. Frequency statistics of help-seeking items for major health problems (five-country sample) 171 Table 75. Frequency statistics for help-seeking items for major health problems (gender sub-samples) ................................................................................................................................................................. 171 Table 76. Frequency statistics for measures of information and help-seeking for major health problems (five-country sample) ............................................................................................................................... 172 Table 77. Frequency statistics for measures of information and help-seeking for major health problems (gender sub-samples) ............................................................................................................................. 173 Table 78. Frequency statistics of health problem and help-seeking experience (five-country sample) 174 Table 79. Frequency statistics of health problem and help-seeking experience (comparative subsamples) .................................................................................................................................................. 175 Table 80. Help provision by type of problem (five-country sample) ....................................................... 176 Table 81. Help provision by type of problem (gender sub-samples) ...................................................... 178 Table 82. Odds ratios relating to overall health, mental health and personal wellbeing by demographic, employment, masculinity, emotionality and social connectedness, and health characteristics for AUSTRALIAN respondents (n=1,588) .................................................................................................... 189

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Table 83. Odds ratio relating to overall health, mental health and personal wellbeing by demographic, employment, masculinity, emotionality and social connectedness, and health characteristics of CANADIAN respondents (n=824) ........................................................................................................... 194 Table 84. Odds ratio relating to overall health, mental health and personal wellbeing by demographic, employment, masculinity, emotionality and social connectedness, and health characteristics for NEW ZEALAND respondents (n=748) ............................................................................................................. 199 Table 85. Odds ratios relating to overall health, mental health and personal wellbeing by demographic, employment, masculinity, emotionality and social connectedness, and health characteristics for UNITED KINGDOM respondents (n=865) ............................................................................................................. 204 Table 86. Odds ratios relating to overall health, mental health and personal wellbeing to demographic, employment, masculinity, emotionality an social connectedness, and health characteristics for UNITED STATES respondents (n=697) ................................................................................................................ 209

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// LIST OF FIGURES Figure 1. Participant journey through the Global Health & Wellbeing 2015 Survey ................................ 28 Figure 2. Respondent’s engagement from global impressions to survey completion.............................. 32 Figure 3. Percentage of endorsed major health problems for younger men aged 16 to 39 years and older men aged over 40 years (older men) by all respondents in the five-country sample ..................... 41 Figure 4. Percentage of endorsed major health problems for men aged 16 to 39 years by men of the same age (n=1,292) .................................................................................................................................. 44 Figure 5. Percentage of endorsed major health problems for men aged 40 years and over by men of the same age (n=2,599) .................................................................................................................................. 46 Figure 6. Percentage of endorsed major mental health problems for younger men aged 16 to 39 years and older men aged 40 years and over by all respondents in the five-country sample ........................... 47 Figure 7. Percentage of endorsed major mental health problems for men aged 16 to 39 years by men of the same age in a five-country sample (n=1,290) .................................................................................... 48 Figure 8. Percentage of endorsed major mental health problems for men aged 40 years and over by men of the same age in a five-country sample (n=2,596) ........................................................................ 52 Figure 9. Respondent’s beliefs concerning the age of onset of major physical health, mental health, and alcohol and/or other substance misuse (%) in a five-country sample (physical health n=9,632; mental health n=9,625; alcohol and/or other substance misuse n=9,624) .......................................................... 54 Figure 10. Heterogeneity of the optimal CMNI-22 three-class solution with associations using the fivecountry sample (n=8,205) ......................................................................................................................... 63 Figure 11. Significant beta scores for social connectedness measures (IBM, SSSS and Duke satisfaction with Social Support Subscale) and emotionality by class, age and gender for the fivecountry sample .......................................................................................................................................... 69 Figure 12. Significant beta scores for social connectedness measures (IBM, SSSS and Duke satisfaction with Social Support Subscale) and emotionality by class and age for the male sub-sample70 Figure 13. Percentage of major life events experienced by the five-country sample within the past 12 months (n=9,034 to n=9,037) .................................................................................................................. 100 Figure 14. Percentage of respondents who experienced a major life event over the past 12 months (including percentage who found the experience stressful) ................................................................... 101 Figure 15. Changes in BMI categories across age-bands for male respondents .................................. 130 Figure 16. Percentage of male respondents who experienced physical health problem ratings of the support provided ...................................................................................................................................... 180 Figure 17. Percentage of male respondents who experienced mental health problem ratings of the support provided ...................................................................................................................................... 182 Figure 18. Percentage of male respondents who experienced alcohol and/or other substance misuse ratings of the support provided................................................................................................................ 183

239

The Authors

Jane Burns

Tracey Davenport

Alyssa Milton

Ian Hickie

Associate Professor Jane Burns is the founder and CEO of the Young and Well Cooperative Research Centre, an organisation that unites young people with researchers, practitioners and innovators to explore the role of technology in improving mental health and wellbeing for young people aged 12 to 25. Jane holds a Principal Research Fellowship at Orygen, The National Centre of Excellence in Youth Mental Health and an Honorary Fellowship at the Brain & Mind Centre. She has led the youth agenda for beyondblue, was a Commonwealth Fund Harkness Fellow at the University of California, San Francisco, and was Director of International Partnerships at Inspire Foundation. Jane held a VicHealth fellowship from 2006-2013, an NHMRC fellowship from 1997-2000 and an NHMRC scholarship from 1994-1996. She holds a PhD in Medicine from the Faculty of Medicine (Public Health and Epidemiology) University of Adelaide. Jane was recently announced a winner in the category of Social Enterprise and Not-forprofit for 2015’s Australian Financial Review and Westpac Group 100 Women of Influence, and was a Victorian Finalist in the 2012 Telstra Business Women's Awards. Jane is a Graduate of the Australian Institute of Company Directors.

From 1997 to 2004, Tracey worked alongside Professor Ian Hickie as the Research Director of the Academic Department of Psychiatry and later the Brain & Mind Centre. In this role, Tracey was responsible for all research methodologies pertaining to associated mental health projects in the Australian community and more specifically primary care psychiatry. Tracey has also been involved in several large– scale public health and government initiatives aimed at improving patient knowledge and understanding of depression and doctor recognition and treatment. Further, in 2003, Tracey earned the honour of Research Fellow with the Centers for Disease Control and Prevention (USA) where she conducted an important project that utilised International epidemiological and clinical research data to test the validity of the diagnosis of chronic fatigue syndrome and related chronic fatigue states. During her research career, Tracey found one of her strengths to be statistics and its application to the health sector. As a result, Tracey set up a private consultative company Academic Research and Statistical Consulting (ARSC) with Dr Georgina Luscombe in 2005. ARSC has consequently allowed Tracey to continue her work with the Brain & Mind Centre as well as apply her statistical expertise to a number of different projects associated with several other government, non–government and private organisations.

Dr Alyssa Milton is a Research Fellow at The University of Sydney’s Brain and Mind Centre. Alyssa has worked for over a decade in the mental health sector in both research and for non-government mental health rehabilitation organisations. As a researcher Alyssa was a Principal Research Associate / Senior Research Clinician for University College London (Division of Psychiatry) where she advised on various interventions delivered across NHS Mental Health Crisis Teams and Early Intervention Services. She was an expert advisor on the NICE on the UK Guidelines for Schizophrenia, 2014. Prior to this, Alyssa held an executive team management role for a nongovernment mental health service. In this role Alyssa headed implementing State and Commonwealth funded mental health programs in the South West Sydney region. In this role she was one of five Australian mental health professionals awarded a Rotary international exchange fellowship to Canada for youth mental health (2010).

In 2003, Professor Ian Hickie was appointed as the inaugural executive director of The University of Sydney’s flagship Brain & Mind Research Institute (now Brain and Mind Centre). Since then he has overseen its development as a major hub in translational neuroscience and clinical psychiatry. Prior to this, in October 2000 he was appointed as the inaugural Chief Executive Officer of beyondblue and from 2003 to 2006 served as its Clinical Advisor. In 2006, Professor Hickie received the Australian Honours Award of Member (AM) in the General Division; for services to medicine in the development of key national mental health initiatives and general practice services in both the public and non– government sectors. From 2006 he was a founding member of headspace. In 2007, Professor Hickie was elected as a Fellow of the Academy of the Social Sciences in Australia. From 2007 to 2012, Professor Hickie was one of the first round of NHMRC Australian Research Fellows, recognising excellence in Australian medical research. From 2008 to 2010, he was appointed to the Federal Health Minister’s National Advisory Council on Mental Health and then in 2010 to 2011, the Federal Minister’s Mental Health Expert Advisory Group. From 2012, Professor Hickie was appointed as a Commissioner in the new National Mental Health Commission, to oversee enhanced accountability for mental health reform in Australia. He was also appointed as Chair of the Scientific Leadership Council for the Young and Well CRC.

Alyssa is also a psychologist specialising in health psychology, and completed her PhD in 2016. She is also a research consultant for various local and international universities, government and non-government agencies, often focusing on mental health employment, selfmanagement, peer support and stigma reduction programs.

With…

Vicky Baldwin

Louise Ellis

Vicky Baldwin is an Education and Research Consultant currently working within The University of Sydney’s Brain and Mind Centre. Vicky’s background is in mental health nursing working in both the community and residential services. Vicky moved into an education role and has substantial experience in the development and delivery of local, regional and national education curriculum including the use of online technology. Prior to moving to Sydney, Vicky was the Head of Education for the Institute of Mental Health, Nottingham in the UK, responsible for managing the education provision delivered through the Institute.

Dr Louise Ellis is a Postdoctoral Research Fellow in the Faculty of Health Sciences at the University of Sydney. She has a PhD in Psychology from the University of Western Sydney and is dedicated to making a positive difference in the lives of young people. Her prior positions include Research Officer at the Inspire Foundation, Principal Clinician at the MULTILT clinic (Macquarie University) and Research Fellow at the Australian Council for Educational Research. Louise is a registered Psychologist and full member of the Australian Psychological Society.

Vicky was also joint lead in the development of the UK National Knowledge and Understanding Framework for Personality Disorder commissioned by the UK Department of Health and Ministry of Justice and acted as Programme Director for all three levels of the framework. Vicky was also the Implementation lead for the evaluation and research program for the Leeds Personality Disorder Network including managing a regional evaluation of the implementation of the National Personality Disorder Offender Strategy. Vicky is also an expert advisor to Emergence + CIC, a national service user lead organisation focused on reducing the stigma towards personality disorder and providing creative opportunities for service users outside of mental health services.

Dr Ellis’ primary research interests include mental health, self-concept, peer support, and learning difficulties/disabilities. Recently, she has become particularly interested in the development and evaluation of web-based interventions designed to improve mental health. She is an academic member of Prometheus (http://www.prometheus.net.au/), a research focused community in the Faculty of Health Sciences. The research goals of Prometheus are aimed at improving behavioural health across the lifespan through the use of cutting edge technology, such as the Internet and multimedia, as well as drawing from established contemporary scientific and clinical practices in psychology and mental health.

Appendix 1: Recruitment // FACEBOOK Paid advertising A paid Facebook advertising campaign was launched in June 2015 with three general images (Figure I) and one video (see YouTube campaign https://www.youtube.com/watch?v=yorU3rcgTrY).

Figure I. Initial Facebook advertisements (image example)

Quotas During early September, quota targets started being reached for a proportion of age and gender groups. Table I presents a snapshot of quotas as of the 12th October 2015, Table II presents this information at the end of survey recruitment. This information was used to enhance Facebook advertising as targeted advertising was to engage specific age and gender demographic groupings. Advertising was reduced for groups that had reached quota targets (in this snapshot: Australian females 16 to 64 years old; New Zealand females 16 to 24 years old; females from the UK 16 to 64 years old). Targeted advertising was increased for other age/ gender/ country groups that had not met quota objectives. Particular focus was placed on Canada and the US. Figure II provides an example of our new advertisements designed to target younger males in the northern hemisphere.

Table I. Age quotas snapshot (12 October 2015) Group AU(F)

Total (n) 2,225

16 to 24 yrs 544

25 to 44 yrs 622

45 to 64 yrs 420

65 yrs + 100

Age not specified 538

AU(M)

998

149

267

222

CA(F)

800

106

158

CA(M)

389

NZ(F)

1,181

373

NZ(M)

530

UK(F)

Key Blue

Exceeding target

268

Pink

Continue targeting

355

Yellow

Focused targeting

136

179

218

352

120

166

1,443

292

267

253

547

UK(M)

751

109

148

156

271

US(F)

832

232

107

108

300

US(M)

559

208

Note. AU= Australia; CA= Canda; NZ=New Zealand; UK=United Kingdom; US=United States; F=Female; M=Male

243

Figure II. Additional Facebook advertisement example

Table II. Age quotas at the end of the survey recruitment (10 Dec 2015) Group

Total (n)

16 to 24 yrs

25 to 44 yrs

45 to 64 yrs

65 yrs +

Age not specified

AU(F)

Blue 2,823

579

666

579

238

759

1,761

209

342

435

289

482

1,950

153

179

527

234

854

1,293

127

335

254

504

1,531

448

227

285

476

1,024

155

151

255

135

327

2,001

329

300

390

152

828

1,255

181

176

251

152

493

1,648

396

136

288

246

580

AU(M)

Pink

CA(F) CA(M)

Key

Yellow

Exceeded participation targets Sufficient participation Low participation

NZ(F) NZ(M) UK(F) UK(M) US(F) US(M) 160 135 276 196 451 1,220 Note. AU= Australia; CA= Canda; NZ=New Zealand; UK=United Kingdom; US=United States; F=Female; M=Male

Organic Facebook use Global interaction with Facebook users was carried out organically with the Global Health & Wellbeing 2015 Survey Facebook page. Survey updates, global news and information on health and wellbeing, as well as images from the Global Health & Wellbeing Survey 2015 Instagram campaign and countdown to the survey finish date were all shared on Facebook to encourage people to like and share with their social connections. As of December 20 (1 days after survey closure), the Facebook page has received 5,428 page likes (Figure III).

245

Figure III. Graph of Facebook likes as of December 20 2015

// TWITTER

Twitter is an important method for public engagement and survey dissemination. In particular, Twitter assists the reach to additional respondents through support from the Global Consortia and friends of the Global Health & Wellbeing 2015 Survey organisational and personal social media channels. As of 20 December, the Global Health & Wellbeing 2015 Survey had reached 437 followers since the commencement of the campaign in July (Figure IV).

Figure IV. Twitter page

247

// INSTAGRAM On Instagram the Global Health & Wellbeing 2015 Survey used two campaign approaches. The first was an international approach (Figure V) where the Global Consortia and survey supporters from around the world photographed various places, events and people with one of three branded Global Health & Wellbeing 2015 Survey postcards. Images were posted to Instagram and when appropriate pushed to Facebook and Twitter. The theme of these posts often focused on life, happiness, health and wellbeing.

Figure V. Instagram International campaign The second campaign was a countdown to the last day of the survey which was released simultaneously on Facebook and Twitter feeds. Information about the survey as well as various health and wellbeing topics were provided, with hash tagging to increase visibility.

Figure VI. Countdown Instagram campaign

// YOUTUBE A paid YouTube advertisement for the Global Health & Wellbeing 2015 Survey was launched in July 2015 (https://www.youtube.com/watch?v=yorU3rcgTrY). By 20 December 2015, the advertisement had reached 565,497 YouTube users across the target countries (Figure VII).

Figure VII. YouTube image // NEWSPAPER ADVERTISING Online newspaper advertising was piloted in the two countries who were recruiting the most (Australia) and the least (US) respondents (Figure VIII). After trialling this method for one-month, click conversion (i.e. the percentage of people who completed the survey after seeing the advertisement) was low (AUS: 0.05%; US: 0.06%) across both countries compared to Facebook advertising and hence, the pilot program was not expanded to the other remaining countries as it was determined to be a poor allocation of resources.

Figure VIII. Newspaper advertising link

// ADDITIONAL ONLINE SOCIAL MEDIA AND ADVERTISING Other social media outlets were utilised for recruitment advertising. These included: 

Thunderclap (a crowd speaking platform that assisted with message dissemination by synchronising a coordinated mass-sharing of the Global Health & Wellbeing 2015 Survey message on social media channels; Figure IX).



LinkedIn (promotes sharing through professional networks).



Google+ (promotes sharing through social networks).



Radio news interviews and radio news grabs.

Figure IX. Thunderclap page

Appendix 2: Additional data tables Table iii. PAF analysis pattern matrix for physical health problem stigma-related beliefs condition (fivecountry sample) To what extent do you agree with the following statements regarding people like Chris? "People like Chris...â&#x20AC;?

Factor 1

0.76

0.02

-0.21

1. Negative beliefs and stereotypes Have themselves to blame Often perform poorly as parents Are dangerous to others Should pull themselves together Shouldn't have children in case they pass on the problem Are a burden to their relatives Are hard to talk to

0.75

0.03

0.06

0.74

-0.03

-0.02

0.69

0.08

-0.13

0.65

0.02

0.00

0.53

-0.03

0.19

0.51

-0.07

0.23

0.02

0.83

-0.04

-0.01

0.83

-0.04

-0.08

0.61

0.08

0.13

0.56

0.11

-0.05

0.02

0.68

-0.24

0.10

0.64

0.26

-0.02

0.48

0.22

0.01

0.48

2. Positive beliefs and stereotypes Often make good employees when they are well Are often very productive people when they are well Often try even harder to contribute to their families or work when they are well Are often artistic or creative people when they are well 3. Social Distance Are kept at a distance by others Are not understood by other people Frighten other people Find it difficult to get married or to live with a partner Factor correlations 1. Negative beliefs and stereotypes 2. Positive beliefs and stereotypes 3. Social distance and relating to others Note: Salient factor loadings (Îť >0.30) are shown in bold text.

252

1.00

-0.36

0.36

-0.36

1.00

0.13

0.36

0.13

1.00

Table IV. PAF analysis pattern matrix for mental health problem stigma-related beliefs condition (fivecountry sample) To what extent do you agree with the following statements regarding people like Chris?" People like Chris...

Factor 1

0.76

0.14

-0.25

0.72

0.04

-0.21

0.62

0.01

0.04

0.56

-0.07

0.12

0.50

-0.05

0.28

0.48

-0.02

0.30

0.39

-0.04

0.29

0.02

0.80

0.00

-0.02

0.76

0.00

-0.07

0.57

0.09

0.16

0.55

0.07

-0.11

0.06

0.55

1. Negative beliefs and stereotypes Should pull themselves together Have themselves to blame Shouldn't have children in case they pass on the problem Are dangerous to others Often perform poorly as parents Are a burden to their relatives Are hard to talk to 2. Positive beliefs and stereotypes Are often very productive people when they are well Often make good employees when they are well Often try even harder to contribute to their families or work when they are well Are often artistic or creative people when they are well 3. Social distance and relating to others Are kept at a distance by others Frighten other people Are not understood by other people Find it difficult to get married or to live with a partner Factor correlations 1. Negative beliefs and stereotypes 2. Positive beliefs and stereotypes 3. Social Distance and relating to others

0.09

0.00

0.54

-0.26

0.17

0.50

0.23

0.03

0.45

1 1.00

2 -0.45

3 0.35

-0.45

1.00

0.08

0.35

0.08

1.00

Note: Salient factor loadings (Îť >0.30) are shown in bold text.

253

Table V. PAF analysis pattern matrix for alcohol or other drug misuse problem stigma-related beliefs condition (five-country sample) To what extent do you agree with the following statements regarding people like Chris? "People like Chris...

Factor 1

0.63

0.04

-0.05

1. Negative beliefs and stereotypes Frighten other people Are kept at a distance by others Find it difficult to get married or to live with a partner Often perform poorly as parents Are hard to talk to Are a burden to their relatives Are dangerous to others

0.55

0.10

-0.21

0.52

0.04

0.01

0.49

-0.09

0.24

0.44

-0.04

0.18

0.44

-0.04

0.27

0.39

-0.05

0.32

-0.07

0.81

0.11

-0.02

0.79

0.08

0.04

0.60

-0.05

0.13

0.51

0.01

-0.01

0.06

0.73

-0.05

0.12

0.72

0.19

-0.07

0.44

0.42

0.15

-0.42

Factor correlations 1. Negative beliefs and stereotypes 2. Positive beliefs and stereotypes 3. Blame Note: Salient factor loadings (Îť >0.30) are shown in bold text.

254

1.00

-0.11

0.46

-0.11

1.00

-0.47

0.46

-0.47

1.00

Table VI. Multiple regression models for stigma-related beliefs (Factor 1, Factor 2 and Factor 3) for physical health problems, mental health problems and alcohol or other drug misuse. F df p Adj. R2 Physical health problems Factor 1: Negative beliefs and stereotypes

5.40

[19, 714]

<0.01

0.10

Factor 2: Positive beliefs and stereotypes

2.52

[19, 714]

<0.01

0.04

Factor 3: Social Distance and relating to others

2.85

[19, 714]

<0.01

0.05

7.16

[19, 690]

<0.01

0.14

Mental health problems Factor 1: Negative beliefs and stereotypes Factor 2: Positive beliefs and stereotypes

3.67

[19, 690]

<0.01

0.07

Factor 3: Social Distance and relating to others

2.71

[19, 690]

<0.01

0.04

Factor 1: Negative beliefs and stereotypes

3.02

[19, 693]

<0.01

0.05

Factor 2: Positive beliefs and stereotypes

2.86

[19, 693]

<0.01

0.05

Factor 3: Blame

4.69

[19, 693]

<0.01

0.09

Alcohol or other drug misuse

255

Appendix 3: International consortia and champions of The Global Health & Wellbeing 2015 Survey With special thanks to Professor Sagar Parikh (Canada & United States), Professor Richard Porter (New Zealand), Professor Jan Scott (United Kingdom), Professor Kathleen Merikangas (United States) and Dr Michael Rovito (United States).

For their incredible contributions to the international consortia, we also thank…

AUSTRALIA Organisations Batyr EMALE Global Anti-Stigma Alliance Headspace Mental Health Coordinating Council Individuals/Groups Bettina Fredrich, Global Anti-Stigma Alliance Andrew King, Mensline Australia Greg Millan, EMALE Charlotte Pointeaux, University of Sydney Tim Sharpe, The Happiness Institute Kathryn M Tomlinson, Department of Transport and Main Roads QLD Government

CANADA Organisations Canadian Association of Retired Persons Canadian Men’s Health Foundation Crescent School – Toronto, Canada University of Toronto, Public Health School University of Toronto, Institute of Health Policy Young Agrarians Individuals/Groups Peter Coleridge, Canadian Mental Health Association Akwatu Khenti, Centre for Addiction and Mental Health David Kuhl, University of British Columbia John Olife, University of British Columbia Tim Paquette, Canadian Physiotherapy Association Brian Russell, Dad Central Cara Tannenbaum, CIHR Institute of Gender and Health

256

NEW ZEALAND Organisations Capital Coast District Health Board Capri Hospital Gay New Zealand Get the Tools Lifehack Mental Health Foundation of New Zealand Individuals/Groups Dave Balwin, Healthy Bastards Janine Bycroft, Health Navigator Bronwyn Dunnachie, The Werry Centre for Child & Adolescent Mental Health Dean Heiford, Rotary Club of Blenheim South Steve Kenny, Get the Tools Claire Newtown, National Council of Women of New Zealand Virginia Tangatatai, Emerge Aotearoa Hiran Thabrew, The Werry Centre for Child & Adolescent Mental Health

UNITED KINGDOM Organisations Anxiety UK B-Eat Big White Wall Bristol Metla Health Division of Psychiatry, University College London Global Action on Men’s Health Healthwatch International society of affective disorders Kings College London Newcastle University Now Then Magazine, Sheffield Off the record, Bristol PANDAS Royal College of Psychiatrists Samaritans, UK School of Psychology, Sussex University The Care Forum Individuals/Groups Liz Andrews, Time to Change Peter Baker/ Global Action on Men’s Health Tom Bromwich, Pluss Sarah Carr, University College London Samantha Carter, Mind The CORE and CIRCLE study team, Division of Psychiatry, University College London Ellen Devine, Healthwatch Bristol Richard Frost, NHS HTAS team, Royal College of Psychiatrists Hannah Istead, Coventry University Marissa Lambert, Nottinghamshire Recovery Trust Karen Machin, Time to change Carrie McKenzie, Healthwatch Sheffield Rachel Needham, Zest Sheffield Lynsey Ogden, Zest Sheffield Claire Palmer, Shellfield-Hallam University Magda Pasiut, Healthwatch, Brighton Jonathan Piotrowski, Bristol University

Sarah Pritchard, NHS Paula Reid, Rethink Steve Robertson, Leeds Beckett University. Kirby Sainsbury, University of Newcastle Josef Sen, Wellness Centre, Sheffield-Hallam University Glenn Townsend, NHS Keith Winestein, Mind Paul White, NHS

UNITED STATES Organisations American Public Health Association The Good Men Project Men’s Health Caucus, APA Men’s Health Network Men’s Health Initiative Prevention Institute Society for Public Health Educators Individuals Nicole Brueggeman, Washington University in St Louis Alicia Czachowski, Alice! Health Promotion Kathy Rovito, Men’s Health Initiative Ashley Tierney, University College Florida Darryl Davidson, City of Milwaukee Health Department Tony Dellovo, The National Depression Screening Day Initiative Dr Dan Duquette, University of Wisconsin Ana Fadich, Men's Health Network Michelle Kiefer, Antelope Valley Partners for Health Barbara Laymon, NACCHO (National Association of County & City Health Officials) Brandon Leonard, Men's Health Network James E. Leone, Bridgewater State University Ramon Llamas, Switch/Health Catherine Mcmains, Texas Health Resources Jenna Peterson, Planned Parenthood Stephen Petty, Intergris Health Tyler Phillips, University College Florida Ilya Plotkin, Public Health Foundation Ken Schwartz, Altarum Institute Steven Shelton, Texas AHEC East Andrea Siguenza, University College Florida Elliot Skylar, Nova Southeastern University Robert Trachtenberg, National AHEC Organization Miguel Valdez Soto, Mayo Clinic Sivarama "Prasad" Vinjamury, Southern California University of Health Sciences Shafiq Wasif, Altarum Institute Kimberly Williams, City of Houston Health Department

258

Appendix 4: The Global Health and Wellbeing 2015 Survey

GLOBAL HEALTH AND WELLBEING SURVEY // MODULE A.

This section asks some questions about you. MAdemo1Q1a. ☐

☐

Yes

MAdemo1Q1b.

Are you 16 years of age or older? [SR] (TERMINATE SURVEY)

What is your age? [SR]

☐☐ years

[EACH ITEM IN MAdemo1Q2 is country specific] MAdemo1Q2a . AUSTRALIA Do you live in: [SR] ☐ A rural or remote

☐ A regional centre

locality

☐ An urban area/ capital city

MAdemo1Q2b. CANADA Do you live in: [SR] ☐ A remote, rural area or

☐ A town or village

isolated dwelling

☐ A city or large urban area/ centre

MAdemo1Q2c. NEW ZEALAND Do you live in: [SR] ☐ A remote, rural area or

☐ A rural area with an

☐ A city or urban area/

isolated dwelling

urban influence

community

MAdemo1Q2d. UK Do you live in: [SR]

☐ A hamlet or isolated

☐ A town or village

☐ A city or large urban

dwelling

area/ centre

MAdemo1Q2e. USA Do you live in: [SR] ☐ A remote area or

☐ A town

☐ A city or large urban

isolated dwelling

area/ center

MAdemo1Q3. What is your 'home' area code, post code or zip code? [SR] ☐☐☐☐☐☐☐☐☐ MAdemo1Q4. What is your highest level of education? [SR] No formal education ☐

☐

Completed or partially completed primary education (1 to 7 years at school)

☐

Completed or partially completed junior high school (8 to 11 years at school)

☐

Completed or partially completed senior high school (12 to 13 years at school)

☐ ☐ ☐

Completed or partially completed a certificate or diploma (includes apprenticeship or trade qualification) Completed or partially completed an undergraduate College or University Degree Completed or partially completed a Postgraduate Diploma, Masters, PhD or other Postgraduate Professional Qualification

MAdemo1Q5a. Which of these best describes your main activity? [SR] ☐

Full-time work (including self-employed)

☐

Part-time work (including self-employed)

☐

Employed but not at work due to illness or vacation etc

☐

Not working and currently receiving sickness allowance or disability support pension

260

☐

Unemployed or looking for work

☐

Volunteer work

☐

Retired

☐

Home duties

☐

Student attending school or university

☐

Other (please specify:

)

MAdemo1Q5b. Do you work or have you ever worked in the military or for emergency services? (You can choose more than 1 answer) [MR]  No, I’ve never worked in the military or emergency services  Not currently, but I’ve previously completed duty/ have Veteran status  Not currently, but have previously worked for emergency services  Yes, active military (including Reserves)  Yes, active emergency services

MAdemo1Q5c. And, which of the following best describes your industry of work? [SR]  Accommodation and food services  Administrative and support services  Agriculture, forestry, fishing and hunting  Arts, entertainment and recreation services  Construction  Educational and training services  Finance and insurance  Health care and social assistance/ social work activities  Information media and telecommunications  Manufacturing  Mining, quarrying, oil and gas extraction  Management of companies and enterprises  Professional, scientific and technical services  Public administration and Defence compulsory social security  Real estate, rental and leasing  Retail trade  Transportation, postal and warehousing  Utilities (electricity, gas, water and waste services)  Wholesale trade  Other services

[SUBMIT RESPONSES AND CONTINUE]

// MODULE B. This section asks about your views regarding major physical and mental health problems often faced by men.

Thinking of men’s health in [INSERT COUNTRY OF RESIDENCE], please answer the following questions.

MBknowQ1a. Right now, what do you think are the MAJOR HEALTH PROBLEMS for younger men aged 16 to 39 years (choose up to 4 answers)? [MR ROTATE] ☐ Accidental injuries (eg. road traffic

☐ Lung and chest infections (eg. pneumonia)

accidents, falls) ☐ Brain, behavioural [behavioral] and mental

☐ Muscle or joint diseases (eg. arthritis)

health disorders (eg. depression, alcohol or other drug misuse, dementia, gambling addiction, bipolar disorder/manicdepressive illness, schizophrenia, anxiety, neurasthenia) ☐ Cancer (eg. liver, lung, prostate, skin)

☐ Non-accidental injuries (eg. self-inflicted, suicide, violence, war)

☐ Diabetes (including high blood sugar)

☐ Stomach, bowel and liver disease (eg. stomach ulcer, cirrhosis of liver)

☐ Heart disease

☐ Stroke (including Transient Ischaemic Attack or ‘ministroke’)

☐ Infectious diseases (eg. HIV/AIDS,

☐ Vision or hearing impairment or loss

diarrhoea, tuberculosis) ☐ Lung and chest diseases (eg. asthma,

☐ Other (please specify:

)

emphysema)

MBknowQ1b. And, what do you think are the MAJOR HEALTH PROBLEMS for older men aged over 40 years (choose up to 4 answers)? [MR ROTATE] ☐ Accidental injuries (eg. road traffic

☐ Lung and chest infections (eg. pneumonia)

accidents, falls) ☐ Brain, behavioural [behavioral] and mental health disorders (eg. depression, alcohol or other drug misuse, dementia, gambling

262

☐ Muscle or joint diseases (eg. arthritis)

addiction, bipolar disorder/manicdepressive illness, schizophrenia, anxiety, neurasthenia) ☐ Cancer (eg. liver, lung, prostate, skin)

☐ Non-accidental injuries (eg. self-inflicted, suicide, violence, war)

☐ Diabetes (i.e. high blood sugar)

☐ Stomach, bowel and liver disease (eg. stomach ulcer, cirrhosis of liver)

☐ Heart disease

☐ Stroke (including Transient Ischaemic Attack or ‘ministroke’)

☐ Infectious diseases (eg. HIV/ AIDS,

☐ Vision or hearing impairment or loss

diarrhoea, tuberculosis) ☐ Lung and chest diseases (eg. asthma,

☐ Other (please specify:

)

emphysema)

MBknowQ2a. Right now, what do you think are the MAJOR MENTAL HEALTH PROBLEMS for young men aged 16 to 39 years (choose up to 4 answers)? [MR ROTATE] ☐ Behavioural [behavioural] or emotional

☐ Gambling addiction

disorders (eg. ADD/ ADHD, conduct disorder) ☐ Alcohol abuse or addiction

☐ Manic-depressive illness or bipolar disorder

☐ Anxiety, neurosis or panic disorder (eg.

☐ Mental retardation or intellectual disorders

neurasthenia, post-traumatic stress disorder) ☐ Autism spectrum disorders (eg. Asperger’s

☐ Personality disorders

syndrome, autism) ☐ Dementia (eg. Alzheimer’s disease,

☐ Schizophrenia or other psychoses

Dementia with Lewy Bodies) ☐ Depressive illness ☐ Drug misuse or addiction ☐ Eating disorders (eg. anorexia nervosa, bulimia nervosa, severe obesity)

☐ Other (please specify:

)

MBknowQ2b. And, what do you think are the MAJOR MENTAL HEALTH PROBLEMS for older men aged over 40 years (choose up to 4 answers)? [MR ROTATE] ☐ Behavioural [behavioural] or emotional

☐ Gambling addiction

disorders (eg. ADD/ ADHD, conduct disorder) ☐ Alcohol abuse or addiction

☐ Manic-depressive illness or bipolar disorder

☐ Anxiety, neurosis or panic disorder (eg.

☐ Mental retardation or intellectual disorders

neurasthenia, post-traumatic stress disorder) ☐ Autism spectrum disorders (eg. Asperger’s

☐ Personality disorders

syndrome, autism) ☐ Dementia (eg. Alzheimer’s disease,

☐ Schizophrenia or other psychoses

Dementia with Lewy Bodies) ☐ Depressive illness

☐ Other (please specify:

☐ Drug misuse or addiction ☐ Eating disorders (eg. anorexia nervosa, bulimia nervosa, severe obesity)

MBknowQ3. For most men, at what age do you think… [SR] 1) MAJOR HEALTH PROBLEMS usually start? 2) MAJOR MENTAL HEALTH PROBLEMS usually start? 3) ALCOHOL OR OTHER DRUG MISUSE usually starts?  Under 12 years old

 45-49 years

 12-15 years

 50-54 years

 16-19 years

 55-59 years

 20-24 years

 60-64 years

 25-29 years

 65-69 years

 30-34 years

 70-74 years

 35-39 years

 75 years and over

 40-44 years

[SUBMIT RESPONSES AND CONTINUE]

264

)

// MODULE C. The first part of this section asks some questions about major periods of change you may experience during your lifetime. MCghwbQ1a. Over the past 12 months have you: [SR] No

Yes

Become a parent for the first time?

☐

Finished high school/secondary school?

☐

Started university/college?

☐

Started a new job?

☐

Suddenly or unexpectedly become unemployed?

☐

Retired?

☐

Experienced a relationship breakdown with someone important to

☐

Yes

Become aggressive (eg. road rage)?

☐

Become bossy, inflexible or angry with others?

☐

Eat more or less?

☐

Engage in spiritual activities?

☐

Get professional help?

☐

Increase use of tobacco, alcohol or other drugs?

☐

Isolate yourself from others, including people close to you?

☐

Overdo activities such as exercising or shopping?

☐

Sleep too much or too little?

☐

Spend more time with friends and loved ones?

☐

Stay at home from work or stay at work extended hours?

☐

Take more risks?

☐

Talk to someone about how you were feeling?

☐

Talk to someone for advice?

☐

Do nothing?

☐

you? [If each response to MCghwbQ1a is ‘No’, skip to MCghwbQ2] MCghwbQ1b. And did you find this experience(s) stressful? [SR]  No (SKIP TO MCghwbQ2)  Yes MCghwbQ1c. If yes, did you: [SR]

☐

Other

☐

MCghwbQ2. Now, here are a number of statements about happiness and coping. Please indicate how much you agree or disagree with each. [SR] Strongly disagree ☐

Disagree

Neutral

Agree

☐

Strongly agree ☐

I can fit in everything I want to

☐

I feel fully mentally alert

☐

I feel that life is very rewarding

☐

I actively look for ways to replace the losses I

☐

I am well satisfied about everything in my life

encounter in life I believe I can grow in positive ways by dealing with difficult situations I look for creative ways to alter difficult situations Regardless of what happens to me, I believe I can control my reaction to it MCghwbQ3. And, using a scale of 1 to 10 (where 1 is completely dissatisfied and 10 is completely satisfied), how satisfied are you with... [SR] Your standard of living? Your health? What you're currently achieving in life? Your personal relationships? How safe you feel? Feeling part of your community? Your future security?

☐ ☐ ☐ ☐ ☐ ☐ ☐

MCghwbTEXT1. The second part of this section asks some questions about your general health and wellbeing. MCghwbQ4. How would you rate your overall health in the past 4 weeks? [SR] ☐ Very bad ☐ Bad ☐ Moderate ☐ Good ☐ Very good

266

MCghwbQ5. In the past 30 days, how often did you feel… [SR] None of

A little of

Some of

Most of

All of the

the time

time

a. tired out for no good reason

☐

b. nervous

☐

c. so nervous that nothing could calm you down

☐

d. hopeless

☐

e. restless or fidgety

☐

f. so restless that you could not sit still

☐

g. depressed

☐

h. that everything was an effort

☐

i. so sad that nothing could cheer you up

☐

j. worthless

☐

MCghwbQ6. During the past month, how many days in total were you unable to carry out your usual daily activities fully (eg. going to work or school)? [SR Range 0-30] ☐☐ days MCghwbQ7a. The next set of questions is about self-harm and suicidal ideation. Remember, all of your answers are confidential. Are you happy to answer these questions? [SR]  No (SKIP TO MODULE D)  Yes (CONTINUE) MCghwbQ7b. In the past 12 months, have you ever: [SR] No

Yes

a. Harmed or hurt yourself on purpose to experience pain or suffering?

☐

b. Felt that life is hardly worth living?

☐

d. Thought about taking your own life?

☐

e. Made plans to take your own life?

☐

Thought that you really would be better off dead?

Attempted to take your own life?

[If a participant responds “Yes” to any item within MCghwbQ7b, the online survey automatically generates the following pop-up box: If you would like to speak to someone about any of the issues that were mentioned in this survey you can contact: 

Australia [include only in Australian Survey] o

Lifeline 

Telephone: 13 11 14



Website: www.lifeline.org.au



Crisis Support Chat: www.lifeline.org.au/Get-Help/Online-Services/crisis-chat



Kids Helpline 

Telephone: 1800 55 1800



Website: www.kidshelp.com.au



Email: counsellor@kidshelp.com.au



Web Counselling: www.kidshelp.com.au/teens/get-help/web-counselling/

beyondblue 

Telephone: 1300 22 4636



Website: www.beyondblue.org.au

Canada: [include only in Canadian Survey] o

Kids Help Phone 

Telephone:1800 668 6868



Website: www.kidshelpphone.ca



Live Chat counselling: www.kidshelpphone.ca/Teens/AskUsOnline/Chatcounselling.aspx

Crisis Intervention Centre 



Suicide Action Montreal 

Telephone: 1 866 277 3553



Website: www.suicideactionmontreal.org

New Zealand: [include only in NZ Survey] o

Lifeline 

Telephone: 09 5222 999 (in Auckland) or 0800 543 354 (outside Auckland)



Website: www.lifeline.org.nz

Suicide Crisis Helpline 



Telephone: 1800 757 7766

Telephone: 0508 828 865

Youthline 

Telephone: 0800 376 633



Website: www.youthline.co.nz



SMS: 234



Email: talk@youthline.co.nz

UK: [include only in UK Survey] o

o 268

Samaritans: 

Telephone: 08457 90 90 90 (UK); 116 123 (ROI)



Website: http://www.samaritans.org/



Email: jo@samaritans.org



SMS: 07725 909090

HopeLine 

Telephone: 0800 068 4141



Website: www.papyrus.org.uk



Email: pat@papyrus-uk.org



SMS: 07786 209697

Childline





Telephone: 0800 1111



Website: www.childline.org.uk

SupportLine 

Telephone: 01708 765200



Website: www.supportline.org.uk



Email: info@supportline.org.uk

US: [include only in US Survey] o

Depression Hotline 

Telephone: 1630 482 9696

National Suicide Prevention LifeLine 

Telephone: 1800 273 8255



Website: www.suicidepreventionlifeline.org

Teen Help Line 

Telephone: 1800 400 0900]

[SUBMIT RESPONSES AND CONTINUE]

// MODULE D. This section asks about your values, ideas and beliefs. MDmescQ1. How much do you personally agree or disagree with each of the following statements. [SR] Strongly

Disagree

Neutral

Agree

disagree

Strongly agree

☐

b. I make sure people do as I say

☐

d. It would be awful if someone thought I was gay

☐

e. I love it when men are in charge of women

☐

g. I would feel good if I had many sexual partners

☐

h. It is important to me that people think I’m

☐

a. My school/ work is the most important part of my life

In general, I don’t like risky situations

I like to talk about my feelings

heterosexual i.

I believe that violence is never justified

☐

I tend to share my feelings

☐

I should be in charge

☐

I would hate to be important

☐

m. Sometimes violent action is necessary

☐

n. I don’t like giving all my attention to school/

☐

o. More often than not, losing doesn’t bother me

☐

p. If I could, I would frequently change sexual

☐

q. I never do things to be an important person

☐

I never ask for help

☐

I enjoy taking risks

☐

Men and women should respect each other as

☐

u. Winning is the most important thing

☐

work

partners

equals

It bothers me when I have to ask for help

270

MDmescQ2. Please read the following statements carefully and rate how frequently you feel or act in the manner described. [SR] Never

Rarely

Sometimes

Often

Always

a. I’m not really interested in how other people feel

☐

b. I become irritated when someone cries

☐

e. I enjoy making other people feel better

☐

I find it silly for people to cry out of happiness

☐

g. I find that i’m ‘in tune’ with other people’s moods

☐

h. I get a strong urge to help when I see someone

☐

I can tell when others are sad even when they don’t say anything

d. I don’t feel sympathy for people who cause their own serious illnesses

who is upset i.

I have tender, concerned feelings for people less fortunate than me

I remain unaffected when someone close to me is happy

It upsets me to see someone being treated disrespectfully

Other people’s misfortunes do not disturb me a great deal

m. When a friend starts to talk about their problems, I try to steer the conversation towards something else n. When I see someone being taken advantage of, I feel kind of protective towards them o. When I see someone being treated unfairly, I don’t feel very much pity for them p. When someone else is feeling excited, I tend to get excited too

MDmescQ3a. And, now some questions about your relationships with people close to you and your social networks. [SR] No

Yes

Yes, sort of

☐

Are you a member of any social club or sporting group?

☐

Are you currently in a relationship? (i.e. have a girlfriend/

☐

Is there anyone you feel you can turn to, if in trouble or crisis?

☐

When you feel happy do you have someone you can share this

☐

If you get angry or upset do you have people you can tell just how you feel? Recently have you had any fights or arguments with people close to you?

boyfriend/ partner/ husband/ wife) Do you have someone you can trust with your private thoughts and feelings? If you’re having a tough time, do you have someone you can really depend on? Is there anyone who really knows you very well? (eg. understands how you think and feel) Is there anyone you feel close to, that understands your concerns/ difficulties?

with? MDmescQ3b. [SR] Hardly ever

Some of the

Most of the

time

☐

Do you know what is going on with your family and friends?

☐

When you are talking with your family and friends, do you feel

☐

Does it seem that your family and friends (people who are important to you) understand you? Do you feel useful to your family and friends (people important to you)?

you are being listened to? Do you feel you have a definite role or place in your family and among your friends? Can you talk about your deepest problems with at least some of your family and friends?

272

MDmescQ3c. [SR] Never

Rarely

Sometimes

Often

☐

How often do family and /or friends criticise [criticize] you?

☐

How often do family and/or friends express interest in how you

☐

How often do family and/or friends create tensions or arguments with you?

are doing? How often do family and/or friends make too many demands on you? How often do family and/or friends make you feel cared for?

MDmescQ4a. Other than members of your family, how many persons do you feel you can depend on or feel very close to? [SR]  None  1 to 2 people  More than 2 people MDmescQ4b. And, thinking specifically about your family and friends, about how many times in the past week (excluding time spent at school or work), did you: [SR] 0 times

More than 7 times

Spend time with someone who doesn’t live with you (eg. went to see them or they came to visit you, or you went out

☐

together)? Talk to someone (friends, relatives or others) on the telephone? Go to meetings of clubs, religious meetings, or other groups of which you’re a member? Use the Internet to spend time with someone, talk with someone or attend club/ group meetings?

MDmescQ5. The following question lists some attitudes and behaviours [behaviors] which people reveal in their close relationships. Please judge your partner's (or someone else very close to you) attitudes and behaviour [behaviour] towards you in recent times. [SR] Not true at

Somewhat

Moderately

Very true

all

true

a. Confides closely in me

☐

b. Is a good companion

☐

c. Is affectionate to me

☐

d. Is fun to be with

☐

e. Is gentle and kind to me

☐

f. Is physically gentle and considerate

☐

g. Is very considerate of me

☐

h. Is very loving to me

☐

i. Makes me feel needed

☐

j. Shows their appreciation of everything I do

☐

k. Speaks to me in a warm and friendly voice

☐

l. Understands my problems and worries

☐

MDmescSKIP. You can finish this survey here but we would appreciate if you would complete the next section. Would you like to continue?  Yes, I will continue  No, go to MODULE H

[SUBMIT RESPONSES AND CONTINUE]

274

// MODULE E. This section asks about information and help-seeking for various health-related issues including physical health, mental health, and alcohol or other drug misuse.

[Participants randomly allocated to either; MEhsQ1/ MEhsQ2/ MEhsQ3 OR MEhsQ4/ MEhsQ5/ MEhsQ6 OR MEhsQ7/ MEhsQ8/ MEhsQ9

FEMALE SURVEY MEhsQ1. Imagine a man very close to you, needs help for a MAJOR PHYSICAL HEALTH PROBLEM. If that man very close to you needed to access care or treatment for a major physical health problem, are you confident… [SR ROTATE]

MEhsQ1. Imagine you (or a man very close to you) needs help for a MAJOR PHYSICAL HEALTH PROBLEM. If you (or that man very close to you) needed to access care or treatment for a major physical health problem, are you confident… [SR ROTATE] No, not

Slightly

Yes,

confident

unconfident

confident

a. You could find the information you needed?

☐

b. You could access the medical care needed?

☐

c. You could access the psychological care

☐

d. The care would be affordable?

☐

e. The care would be helpful?

☐

f. The care would assist in the return to school or

☐

needed?

work? g. If you received professional help you would fully recover? h. If you received professional help you would have some improvement?

FEMALE SURVEY MEhsQ2. How long do you think a MAJOR PHYSICAL HEALTH PROBLEM needs to be present before that man very close to you should seek help? [SR]

MEhsQ2. How long do you think a MAJOR PHYSICAL HEALTH PROBLEM needs to be present before you (or that man very close to you) should seek help? [SR]

☐ Less than 2 weeks ☐ 2 to 4 weeks ☐ 5 to 8 weeks ☐ 9 to 12 weeks ☐ More than 12 weeks

MEhsQ3a. Have you ever looked for information about a MAJOR PHYSICAL HEALTH PROBLEM? [SR] ☐ No (SKIP TO MEhsQ10)

☐ Yes (CONTINUE)

MEhsQ3b. How did you get this information (you can choose more than 1 answer)? [MR ROTATE] ☐

Asked a doctor

☐

Asked a family member

☐

Asked a friend

☐

Bought a book or health magazine

☐

Called a telephone helpline

☐

Contacted a community health centre

☐

Printed information from pharmacies or medical centre

☐

Searched Internet, apps or etools

☐

Television or radio

☐

Visited the library

☐

Other (please specify:

)

FEMALE SURVEY MEhsQ4. Imagine a man very close to you needs help for a MAJOR MENTAL HEALTH PROBLEM. If that man very close to you needed to access care or treatment for a major mental health problem, are you confident… [SR ROTATE]

MEhsQ4. Imagine you (or a man very close to you) needs help for a MAJOR MENTAL HEALTH PROBLEM. If you (or that man very close to you) needed to access care or treatment for a major mental health problem, are you confident… [SR ROTATE]

276

No, not

Slightly

Yes,

confident

unconfident

confident

a. You could find the information you needed?

☐

b. You could access the medical care needed?

☐

c. You could access the psychological care needed?

☐

d. The care would be affordable?

☐

e. The care would be helpful?

☐

f. The care would assist in the return to school or

☐

work? g. If you received professional help you would fully recover? h. If you received professional help you would have some improvement?

FEMALE SURVEY MEhsQ5. How long do you think a MAJOR MENTAL HEALTH PROBLEM needs to be present before that man very close to you should seek help? [SR]

MEhsQ5. How long do you think a MAJOR MENTAL HEALTH PROBLEM needs to be present before you (or that man very close to you) should seek help? [SR] ☐ Less than 2 weeks ☐ 2 to 4 weeks ☐ 5 to 8 weeks ☐ 9 to 12 weeks ☐ More than 12 weeks

MEhsQ6a. Have you ever looked for information about a MAJOR MENTAL HEALTH PROBLEM? [SR] ☐ No (SKIP TO MEhsQ10)

☐ Yes (CONTINUE)

MEhsQ6b. How did you get this information (you can choose more than 1 answer)? [MR ROTATE] ☐

Asked a doctor

☐

Asked a family member

☐

Asked a friend

☐

Bought a book or health magazine

☐

Called a telephone helpline

☐

Contacted a community health centre

☐

Printed information from pharmacies or medical centre

☐

Searched Internet, apps or etools

☐

Television or radio

☐

Visited the library

☐

Other (please specify:

)

FEMALE SURVEY MEhsQ7. Imagine a man very close to you needs help for ALCOHOL OR OTHER DRUG MISUSE. If that man very close to you needed to access care or treatment for alcohol or other drug misuse, are you confident… [SR ROTATE]

MEhsQ7. Imagine you (or a man very close to you) needs help for ALCOHOL OR OTHER DRUG MISUSE. If you (or that man very close to you) needed to access care or treatment for alcohol or other drug misuse, are you confident… [SR ROTATE] No, not

Slightly

Yes,

confident

unconfident

confident

a. You could find the information you needed?

☐

b. You could access the medical care needed?

☐

c. You could access the psychological care needed?

☐

d. The care would be affordable?

☐

e. The care would be helpful?

☐

f. The care would assist in the return to school or

☐

work? g. If you received professional help you would fully recover? h. If you received professional help you would have some improvement?

278

FEMALE SURVEY MEhsQ8. How long do you think ALCOHOL OR OTHER DRUG MISUSE needs to be present before that man very close to you should seek help? [SR]

MEhsQ8. How long do you think ALCOHOL OR OTHER DRUG MISUSE needs to be present before you (or that man very close to you) should seek help? [SR] ☐ Less than 2 weeks ☐ 2 to 4 weeks ☐ 5 to 8 weeks ☐ 9 to 12 weeks ☐ More than 12 weeks

MEhsQ9a. Have you ever looked for information about ALCOHOL OR OTHER DRUG MISUSE? [SR] ☐ No (SKIP to MEhsQ10)

☐ Yes (CONTINUE)

MEhsQ9b. How did you get this information (you can choose more than 1 answer)? [MR ROTATE] ☐

Asked a doctor

☐

Asked a family member

☐

Asked a friend

☐

Bought a book or health magazine

☐

Called a telephone helpline

☐

Contacted a community health centre

☐

Printed information from pharmacies or medical centre

☐

Searched Internet, apps or etools

☐

Television or radio

☐

Visited the library

☐

Other (please specify:

)

MEhsTEXT. The second part of this section asks about your personal experience with health care.

MEhsQ10. Have you ever experienced…(you can choose more than 1 answer)? [MR] ☐

A major physical health problem

[If Yes, a PHYSICAL HEALTH PROBLEM in MEhsQ10  ask MEhsQ11b]

☐

A major mental health problem

[If Yes, a MAJOR MENTAL HEALTH PROBLEM in MEhsQ10  MEhsQ12b]

☐

Alcohol or other drug misuse

[If Yes, ALCOHOL OR OTHER DRUG MISUSE in MEhsQ10  ask MEhsQ13b]

☐

None of the above

SKIP TO MEhsSKIP

MEhsQ11b. Did you receive any help for this PHYSICAL HEALTH PROBLEM? [SR] ☐ No (SKIP TO MEhsQ12b (if

☐ Yes (CONTINUE)

yes MEhsQ10 MH) OR MEhsQ13b (if yes MEhsQ10 D&A) OR MEhsSKIP if neither condition arises)

MEhsQ11c. Who provided this help (you can choose more than 1 answer)? [MR ROTATE] ☐

Acupuncturist

☐

Alcohol and drug worker

☐

Culturally specific health worker

☐

Clergy, priest or other religious person

☐

Counsellor [counselor]

☐

Dietitian [dietician] /Nutritionist

☐

Family

☐

Friends

☐

General practitioner or family doctor

☐

Internet, apps or etools

☐

Naturopath or herbalist

☐

Nurse

☐

Occupational therapist 280

☐

Partner (eg. girlfriend, boyfriend, spouse)

☐

Personal trainer, exercise manager or relaxation instructor (eg. massage therapist, yoga or meditation teacher)

☐

Pharmacist / Chemist

☐

Psychiatrist

☐

Psychologist

☐

Physiotherapist

☐

Social worker/ Welfare officer

☐

Specialist doctor/ Medical specialist

☐

Telephone helpline

☐

Traditional healer (eg. Qigong master, shaman)

☐

Other (please specify:

)

MEhsQ11d. And, how helpful was this support? [MR ROTATE - only items selected in MEhsQ11dc presented] Unhelpful

Slightly

Neither helpful or

Slightly

unhelpful

helpful

Helpful

a. Acupuncturist

☐

b. Alcohol and drug worker

☐

c. Culturally specific health worker

☐

d. Clergy, priest or other religious

☐

e. Counsellor [counselor]

☐

f. Dietitian [dietician] / Nutritionist

☐

g. Family

☐

h. Friends

☐

i. General practitioner or family doctor

☐

j. Internet, apps or etools

☐

k. Naturopath or herbalist

☐

l. Nurse

☐

m. Occupational therapist

☐

n. Partner (eg. girlfriend, boyfriend,

☐

person

spouse)

Unhelpful

Slightly

Neither helpful or

Slightly

unhelpful

helpful

☐

p. Pharmacist/ Chemist

☐

q. Psychiatrist

☐

r. Psychologist

☐

s. Physiotherapist

☐

t. Social worker/ Welfare officer

☐

u. Specialist doctor/ Medical specialist

☐

v. Telephone helpline

☐

w. Traditional healer (eg. Qigong

☐

o. Personal trainer, exercise manager

Helpful

or relaxation instructor (eg. massage therapist, yoga or meditation teacher)

master, shaman) x. Other

MEhsQ12b. Did you receive any help for this MENTAL HEALTH PROBLEM? [SR] ☐ No [SKIP TO MEhsQ13b (if

☐ Yes (CONTINUE)

yes MEhsQ10 D&A) OR MEhsSKIP if neither condition arises)

MEhsQ12c. Who provided this help (you can choose more than 1 answer)? [MR ROTATE] ☐

Acupuncturist

☐

Alcohol and drug worker

☐

Culturally specific health worker

☐

Clergy, priest or other religious person

☐

Counsellor [counselor]

☐

Dietitian [dietician] /Nutritionist

☐

Family

☐

Friends

☐

General practitioner or family doctor

☐

Internet, apps or etools

☐

Naturopath or herbalist

☐

Nurse 282

☐

Occupational therapist

☐

Partner (eg. girlfriend, boyfriend, spouse)

☐

Personal trainer, exercise manager or relaxation instructor (eg. massage therapist, yoga or meditation teacher)

☐

Pharmacist / Chemist

☐

Psychiatrist

☐

Psychologist

☐

Physiotherapist

☐

Social worker/ Welfare officer

☐

Specialist doctor/ Medical specialist

☐

Telephone helpline

☐

Traditional healer (eg. Qigong master, shaman)

☐

Other (please specify:

)

MEhsQ12d. And, how helpful was this support? [MR ROTATE only items selected in MEhsQ12c presented] Unhelpful

Slightly

Neither helpful

Slightly

unhelpful

or unhelpful

helpful

Helpful

a. Acupuncturist

☐

b. Alcohol and drug worker

☐

c. Culturally specific health worker

☐

d. Clergy, priest or other religious

☐

e. Counsellor [counselor]

☐

f. Dietitian [dietician] / Nutritionist

☐

g. Family

☐

h. Friends

☐

i. General practitioner or family doctor

☐

j. Internet, apps or etools

☐

k. Naturopath or herbalist

☐

l. Nurse

☐

m. Occupational therapist

☐

n. Partner (eg. girlfriend, boyfriend,

☐

person

spouse)

Unhelpful

Slightly

Neither helpful

Slightly

unhelpful

or unhelpful

helpful

☐

p. Pharmacist/ Chemist

☐

q. Psychiatrist

☐

r. Psychologist

☐

s. Physiotherapist

☐

t. Social worker/ Welfare officer

☐

u. Specialist doctor/ Medical specialist

☐

v. Telephone helpline

☐

w. Traditional healer (eg. Qigong

☐

o. Personal trainer, exercise manager

Helpful

or relaxation instructor (eg. massage therapist, yoga or meditation teacher)

master, shaman) x. Other

MEhsQ13b. Did you receive any help for this ALCOHOL OR OTHER DRUG MISUSE? [SR] ☐ No (SKIP TO MEhsSKIP)

☐ Yes (CONTINUE)

MEhsQ13c. Who provided this help (you can choose more than 1 answer)? [MR ROTATE] ☐

Acupuncturist

☐

Alcohol and drug worker

☐

Culturally specific health worker

☐

Clergy, priest or other religious person

☐

Counsellor [counselor]

☐

Dietitian [dietician] /Nutritionist

☐

Family

☐

Friends

☐

General practitioner or family doctor

☐

Internet, apps or etools

☐

Naturopath or herbalist

☐

Nurse

☐

Occupational therapist

☐

Partner (eg. girlfriend, boyfriend, spouse)

284

☐

Personal trainer, exercise manager or relaxation instructor (eg. massage therapist, yoga or meditation teacher)

☐

Pharmacist / Chemist

☐

Psychiatrist

☐

Psychologist

☐

Physiotherapist

☐

Social worker/ Welfare officer

☐

Specialist doctor/ Medical specialist

☐

Telephone helpline

☐

Traditional healer (eg. Qigong master, shaman)

☐

Other (please specify:

)

MEhsQ13d. And, how helpful was this support? [MR ROTATE - only items selected MEhsQ13c presented] Unhelpful

Slightly

Neither helpful

Slightly

unhelpful

or unhelpful

helpful

Helpful

a. Acupuncturist

☐

b. Alcohol and drug worker

☐

c. Culturally specific health worker

☐

d. Clergy, priest or other religious

☐

e. Counsellor [counselor]

☐

f. Dietitian [dietician] / Nutritionist

☐

g. Family

☐

h. Friends

☐

i. General practitioner or family doctor

☐

j. Internet, apps or etools

☐

k. Naturopath or herbalist

☐

l. Nurse

☐

m. Occupational therapist

☐

n. Partner (eg. girlfriend, boyfriend,

☐

person

spouse) o. Personal trainer, exercise manager or relaxation instructor (eg. massage therapist, yoga or meditation teacher)

Unhelpful

Slightly

Neither helpful

Slightly

unhelpful

or unhelpful

helpful

Helpful

p. Pharmacist/ Chemist

☐

q. Psychiatrist

☐

r. Psychologist

☐

s. Physiotherapist

☐

t. Social worker/ Welfare officer

☐

u. Specialist doctor/ Medical specialist

☐

v. Telephone helpline

☐

w. Traditional healer (eg. Qigong

☐

master, shaman) x. Other

MEhsSKIP. You can finish this survey here but we would appreciate if you would complete the next section. Would you like to continue? ☐ Yes, I will continue

☐ No, go to final section of survey

[If “no”, go to Module H]

286

// MODULE F. This section includes a short story about a person called Chris, and asks you some questions about their circumstances.

[FOR MFsdQ1, MFsdQ2, & MFsdQ3 PARTICIPANTS ARE RANDOMISED TO ONE OF THREE HEALTH CONDITIONS – physical health (MFsdTEXT1a), mental health (MFsdTEXT1b), alcohol or other drug misuse (MFsdTEXT1c)]

MFsdTEXT1a. "Chris is a person with a PHYSICAL HEALTH CONDITION who recently attended a community meeting. The community meeting was a discussion about the PHYSICAL HEALTH CONDITION Chris experiences, and the role it plays in education, training and the workforce."

MFsdTEXT1b. "Chris is a person with a MENTAL HEALTH CONDITION who recently attended a community meeting. The community meeting was a discussion about the MENTAL HEALTH CONDITION Chris experiences, and the role it plays in education, training and the workforce."

MFsdTEXT1c. "Chris recently experienced an ALCOHOL OR OTHER DRUG USE PROBLEM and decided to attend a community meeting. The community meeting was a discussion about the ALCOHOL OR OTHER DRUG USE PROBLEM Chris experienced, and the role it plays in education, training and the workforce." MFsdQ1. Please rate how much you agree with the following statements about Chris. [SR ROTATE] Strongly

Neutral

Strongly

disagree a. Chris is responsible for this health

agree

☐

condition b. Chris is able to overcome problems related to it c. Chris should be able to receive help from the community d. Chris should be given assistance related to education, training or work.

MFsdQ2. To what extent do you agree with the following statements regarding people like Chris? [SR ROTATE] “People like Chris …

Strongly

Disagree

Neutral

Agree

disagree

Strongly agree

a. Are a burden to their relatives

☐

b. Are dangerous to others

☐

c. Are hard to talk to

☐

d. Are kept at a distance by others

☐

e. Are not understood by other people

☐

f. Are often artistic or creative people when they are

☐

i. Frighten other people

☐

j. Have themselves to blame

☐

k. Often make good employees when they are well

☐

l. Often perform poorly as parents

☐

m. Often try even harder to contribute to their families

☐

n. Should pull themselves together

☐

o. Shouldn’t have children in case they pass on the

☐

well g. Are often very productive people when they are well h. Find it difficult to get married or to live with a partner

or work when they are well

problem

MFsdQ3. And, do you think people like Chris would be discriminated against by: [SR ROTATE] Definitely

Probably

unlikely

Probably

Definitely

likely

☐

c. A public or private hospital

☐

d. Other people they don’t know well

☐

e. A doctor or other health professional

☐

f. An employer

☐

g. Family

☐

a. A bank, insurance company or other

Neutral

financial institution b. A government or other public welfare agency

288

h. Friends i. Other (please specify:

)

Definitely

Probably

unlikely

☐

Neutral

Probably

Definitely

likely

☐

MFsdQ4. Do you think Chris is:  Male  Female  Gender Neutral MFsdQ5a. Based on your personal experience with a PHYSICAL HEALTH PROBLEM, how much do you agree with the following statements: [NOTE: ONLY asked if identified yes to personally experiencing a PHYSICAL HEALTH PROBLEM in MEhsQ10] Strongly

Disagree

Neutral

Agree

disagree

Strongly agree

a. I feel ashamed

☐

b. I feel embarrassed

☐

c. I feel inferior to other people

☐

d. I feel disappointed in myself

☐

e. I think I should be able to cope with things

☐

f. I think I should be able to ‘pull myself together’

☐

g. I think I should be stronger

☐

h. I think I only had myself to blame

☐

i. I feel embarrassed about seeking professional

☐

k. I see myself as weak if I took medications

☐

l. I want people to know that I wasn’t coping

☐

m. I feel I couldn’t contribute much socially

☐

n. I feel inadequate around other people

☐

o. I feel like I was good company

☐

p. I feel like a burden to other people

☐

help for my condition j. I feel embarrassed if others knew I was seeking professional help for my condition

MFsdQ5b. Based on your personal experience with a MENTAL HEALTH PROBLEM, how much do you agree with the following statements: [NOTE: ONLY asked if identified yes to personally experiencing a MENTAL HEALTH PROBLEM in MEhsQ10] Strongly

Disagree

Neutral

Agree

disagree

Strongly agree

a. I feel ashamed

☐

b. I feel embarrassed

☐

c. I feel inferior to other people

☐

d. I feel disappointed in myself

☐

e. I think I should be able to cope with things

☐

f. I think I should be able to ‘pull myself together’

☐

g. I think I should be stronger

☐

h. I think I only had myself to blame

☐

i. I feel embarrassed about seeking professional

☐

k. I see myself as weak if I took medications

☐

l. I want people to know that I wasn’t coping

☐

m. I feel I couldn’t contribute much socially

☐

n. I feel inadequate around other people

☐

o. I feel like I was good company

☐

p. I feel like a burden to other people

☐

help for my condition j. I feel embarrassed if others knew I was seeking professional help for my condition

290

MFsdQ5c. Based on your personal experience with ALCOHOL OR OTHER DRUG MISUSE, how much do you agree with the following statements: [NOTE: ONLY asked if identified yes to personally experiencing an ALCOHOL OR OTHER DRUG MISUSE PROBLEM in MEhsQ10] Strongly

Disagree

Neutral

Agree

disagree

Strongly agree

a. I feel ashamed

☐

b. I feel embarrassed

☐

c. I feel inferior to other people

☐

d. I feel disappointed in myself

☐

e. I think I should be able to cope with things

☐

f. I think I should be able to ‘pull myself together’

☐

g. I think I should be stronger

☐

h. I think I only had myself to blame

☐

i. I feel embarrassed about seeking professional

☐

k. I see myself as weak if I took medications

☐

l. I want people to know that I wasn’t coping

☐

m. I feel I couldn’t contribute much socially

☐

n. I feel inadequate around other people

☐

o. I feel like I was good company

☐

p. I feel like a burden to other people

☐

help for my condition j. I feel embarrassed if others knew I was seeking professional help for my condition

MFsdSKIP. You can finish this survey here but we would appreciate if you would complete the next section. Would you like to continue?  Yes, I will continue  No, go to final section of survey

[SUBMIT RESPONSES AND CONTINUE]

// MODULE G. This section asks about health-related activities including physical activity, eating and sleeping. It also asks about alcohol, tobacco and other drug use as well as gambling. MGhbTEXT. i. PHYSICAL HEALTH AND ACTIVITY SCHEDULE Please answer each question even if you do not consider yourself to be an active person. Think about the activities you do at work, as part of your house and yard work, to get from place to place, and in your spare time for recreation, exercise or sport.

Vigorous physical activities make you breathe much harder than normal and may include heavy lifting, digging, aerobics, or fast bicycling. Think only about those physical activities that you did for at least 10 minutes at a time.

MGhbQ1a. Over the past week, on how many days did you do vigorous physical activities? [SR]  0 days (SKIP TO MGhbQ2a)  1 day  2 days  3 days  4 days  5 days  6 days  7 days MGhbQ1b. How much time did you usually spend doing vigorous physical activities on one of those days? [SR] Response: _____ minutes OR _____ hours Moderate physical activities make you breathe somewhat harder than normal and may include carrying light loads, bicycling at a regular pace, or doubles tennis. Do not include walking. Again, think about only those physical activities that you did for at least 10 minutes at a time. MGhbQ2a. Over the past week, on how many days did you do moderate physical activities? [SR]  0 days (SKIP TO MGhbQ3a)  1 day  2 days  3 days  4 days  5 days  6 days 292

 7 days MGhbQ2b. How much time did you usually spend doing moderate physical activities on one of those days? [SR] Response: _____ minutes OR _____ hours Walking includes at work and at home, walking to travel from place to place, and any other walking that you might do solely for recreation, sport, exercise, or leisure.

MGhbQ3a. Over the past week, on how many days did you walk for at least 10 minutes at a time? [SR]  0 days [SKIP TO MGhbQ4]  1 day  2 days  3 days  4 days  5 days  6 days  7 days MGhbQ3b. How much time did you usually spend walking on one of those days? [SR] Response: _____ minutes OR _____ hours

Sitting includes time spent at work, at home, and during leisure time. This may include time spent sitting at a desk, visiting friends, reading or sitting or lying down to watch television.

MGhbQ4. Over the past week, how much time did you usually spend sitting in a week day? [SR] Response: _____ minutes OR _____ hours MGhbQ5a. Over the past week, how many times did you weight train? [SR]  None (SKIP TO MGhbQ6a)  1 time during the week  2 times during the week  3 to 4 times during the week  5 to 6 times during the week  Every day

MGhbQ5b. What is the length of a typical weight training session? [SR] Response: _____ minutes OR _____ hours MGhbQ5c. What is the intensity of a typical weight training session? [SR]

 Light  Moderate  Heavy MGhbQ6a. During your life, how many times have you taken steroid pills or shots without a doctor's prescription?  0 times (SKIP to MGhbTEXT1)  1 to 2 times  3 to 9 times  10 to 19 times  20 to 39 times  40 or more times MGhbQ6b. And, in the past 12 months have you taken steroid pills or shots without a doctor's prescription?  No  Yes MGhbTEXT1. ii. EATING BEHAVIOURS AND BODY IMAGE MGhbQ7. Over the past week, how often have you consumed: [MR ROTATE] Once a

A few

week

times a

Most days

Once a

Several

day

times a

week

Not at all

day

a. Energy dense snacks (eg. confectionary, cakes, sweet



biscuits, potato crisps) b. Fast food (eg. fish and chips, hot chips, pizza, hot dogs, meat pies) c. Fruit and vegetables (either canned, fresh, frozen or dried) d. Meat (including red meat, pork or chicken) e. Fish (including canned, fresh, frozen or dried)

MGhbQ8. Are you currently dieting? [SR]  No

 Yes, to lose weight

MGhbTEXT2. The next set of questions asks about body image. 294

 Yes, to gain weight

MGhbQ9a. What is your weight? (Please choose your preferred scale.) [SR]  Kilograms (CONTINUE TO MGhbQ9ai)  Pounds (SKIP TO MGhbQ9aii) MGhbQ9ai. Weight in kilograms:_______________________ MGhbQ9aii. Weight in pounds:________________________ MGhbQ9b. What is your height? (Please choose your preferred scale.) [SR]  Metres (CONTINUE TO MGhbQ9bi)  Feet and inches (SKIP TO MGhbQ9bii) MGhbQ9bi. Height in metres: MGhbQ9bii. Please enter feet in the slider below then inches in the slider below that: Feet:_____________________ MGhbQ9biii. Inches:________________ MALE SURVEY ONLY MGhbQ9c. What is your waist measurement? (Your waist measurement is often the 'size' of your jeans.)  Centimetres (CONTINUE TO MGhbQ9ci)  Inches (SKIP TO MGhbQ9cii) MGhbQ9ci. Centimetres:______________ MGhbQ9cii. Inches:__________________ MGhbQ10. Would you consider yourself to be: [SR]  Very underweight  Slightly underweight  About the right weight  Slightly overweight  Very overweight MGhbQ11. In the past 3 MONTHS, how much has your weight or your shape influenced how you think about yourself as a person? [SR] 1

Not at all 

6 A great deal



MGhbQ12a. Do you get very distressed or preoccupied by any specific aspect of your physical appearance? [SR]  No (SKIP TO MGhbTEXT3)  Yes (CONTINUE TO MGhbQ12b) MGhbQ12b. Which aspect or aspects of your physical appearance do you worry about?  Facial features (including eyes, nose, teeth)  Height (being too tall or too short)  Body (shape or type)  Hips, thighs or bottom  Chest  General overall appearance  Arms or legs  Skin imperfections (including scars, acne, eczema)  Hair (including body hair)  Weight (being overweight or underweight)  Muscles (including size, tone, mass)  Stomach or waist  Nothing MGhbTEXT3. iii. SLEEPING MGhbQ13. On average over the past week, how many hours did you sleep each night? [SR] ☐ <6 hours

☐ 6 hours

☐7 hours

☐ 8 hours

☐9 hours

☐10 or more hours

MGhbQ14. On average over the past week, rate the quality of your sleep: [SR] ☐ Very bad

☐ Bad

☐Average ☐

Good

☐Very good

MGhbQ15. On average over the past week, how refreshed did you feel when you woke up? [SR] ☐ Very tired

☐ Tired

☐Average ☐

Awake

☐Very awake

HGhbQ33a. And, do you use the Internet after 11pm at night?  No (SKIP TO MGhbTEXT4)  Yes (CONTINUE TO MGhbQ33b) MGhbQ33b. How often do you use the Internet after 11pm at night?  Less than once a week  1 to 3 nights a week  4 to 5 nights a week  6 to 7 nights a week MGhbTEXT4. iv. ALCOHOL OR OTHER DRUG USE [IF NO TO ALL MGhbQ16 AND MGhbQ17a SKIP TO MGhbTEXT6]

296

MGhbQ16. In your life, have you ever tried any of the following substances? [SR ROTATE] No a. Tobacco products (cigarettes, e-cigarettes, pipes, cigars or chewing tobacco)

Yes If yes, continue



and complete MGhbQ22c If yes, continue

b. Alcoholic beverages (beer, wine or spirits)



and complete MGhbQ23a

MGhbQ17a. In your life, have you ever tried any of the following substances for non-medical uses? [SR ROTATE] [if yes to any item in MGhbQ17a answer MGhbQ18] No

Yes If yes, continue

a. Cannabis (marijuana, pot, grass or hash)



and complete MGhbQ24a If yes, continue

b. Cocaine (coke, crack etc)



and complete MGhbQ25a

Methamphetamine/ amphetamine type stimulants (speed, ice, diet pills, ecstasy etc)

If yes, continue 



and complete MGhbQ26a If yes, continue

d. Inhalants (nitrous, glue, petrol, paint thinner etc)



and complete MGhbQ27a If yes, continue

e. Sedatives or sleeping pills (Valium, Serepax, Rohypnol etc)



and complete MGhbQ28a If yes, continue

Hallucinogens (LSD, acid, mushrooms, PCP, Special K etc)



and complete MGhbQ29a If yes, continue

g. Opioids (heroin, morphine, methadone, codeine etc) for non-medical uses



and complete MGhbQ30a If yes, continue

h. Other substances (please specify by completing question MGhbQ17b)



and complete MGhbQ31a

[ONLY ANSWER MGhbQ17b IF THE ANSWER IS ‘YES’ TO MGhbQ17a “h. Other substances”] MGhbQ17b. Please specify the 'Other substances' you mention above:_____________________________ [SKIP MGhbQ18 IF FOR MGhbQ16 AND MGhbQ17a “ALCOHOL” IS THE ONLY SUBSTANCE SELECTED]

MGhbQ18. Have you used any of these substances at the same time as drinking alcohol? [SR]  No  Yes MGhbQ19. Have you recently thought that you should cut down on alcohol, tobacco or other addictive drugs? [SR] [IF YES, AT END OF MODULE PROVIDE SUPPORT NUMBERS]  No  Yes MGhbQ20. Have you recently had a friend, relative or doctor suggest that you should cut down on alcohol, tobacco or other addictive drugs? [SR] [IF YES, AT END OF MODULE PROVIDE SUPPORT NUMBERS]  No  Yes MGhbQ21. Why do you use this/ these substances? [SR ROTATE] No

Yes

a. Because it helps me enjoy a party



b. Because it is a habit



d. Because I cannot stop myself



e. To cheer myself up when I’m in a bad mood



g. To forget about my worries



h. Other reason



Because it makes social gatherings more fun

To forget about my problems

Tobacco products MGhbQ22c. In the past 3 months, how often have you used tobacco products? [SR] 

Daily (GO TO MGhbQ22ci)



At least weekly (GO TO MGhbQ22cii)



Less often than weekly (GO TO MGhbQ22ciii)



Not at all, but I have smoked in the past 12 months (GO TO MGhbQ22cv)



Not at all and I have not smoked in the past 12 months (SKIP TO MGhbQ22a)

MGhbQ22ci. How many a day  Less than 5  5 – 10  11 – 15  16 – 20  More than 20 298

[GO TO MGhbQ22civ] MGhbQ22cii. How many a week?  Less than 5  5 – 10  11 – 15  16 – 20  More than 20 [GO TO MGhbQ22civ] MGhbQ22ciii. How many a month?  Less than 5  5 – 10  11 – 15  16 – 20  More than 20 [GO TO MGhbQ22civ] MGhbQ22civ. Time to first cigarette after waking?  1 - 15 minutes  15 - 29 minutes  30 - 44 minutes  45 - 59 minutes  1 - 2 hours  3 - 6 hours  More than 6 hours [GO TO MGhbQ22d] MGhbQ22cv. How many in the past 12 months? __________ [GO TO MGhbQ22a (below)] MGhbQ22d. How many days in the last week have you used tobacco products?  0 days  1 day  2 days  3 days  4 days  5 days  6 days  7 days [SKIP MGhbQ22a IF ANSWER TO MGhbQ22c WAS ‘Daily’]

MGhbQ22a. Have you ever used tobacco products on a daily basis?  No  No, I have only ever smoked a few times  Yes, I smoke daily now  Yes, I used to smoke on a daily basis, but not now MGhbQ22b. How old were you when you first started using tobacco products daily? [SR] [Answer option between 0 & 99] Alcohol MGhbQ23a. How old were you when you had your first full serve of alcohol? [SR] ______ (age) MGhbQ23b. In the past 12 months, how often did you have an alcoholic drink of any kind? [SR]  Every day  5 to 6 days a week  3 to 4 days a week  1 to 2 days a week  2 to 3 days a month  About 1 day a month  Less often  I have not had an alcoholic drink in the past 12 months [SKIP TO NEXT SELECTED SUBSTANCE ELSE MGhbTEXT6] MGhbQ23c. On a day that you have an alcoholic drink, how many standard drinks do you usually have? [SR]  13 or more drinks  11 to 12 drinks  7 to 10 drinks  5 to 6 drinks  3 to 4 drinks  1 to 2 drinks MGhbQ23d. At the present time do you consider yourself to be… [SR]  A non-drinker  An ex-drinker  An occasional drinker  A light drinker  A social drinker  A heavy drinker  A binge drinker MGhbQ23e. How many days in the last week have you had alcohol? [SR]  0 days 300

 1 day  2 days  3 days  4 days  5 days  6 days  7 days Cannabis MGhbQ24a. How old were you when you first used cannabis? [SR] ______ (age) MGhbQ24b. In the past 12 months, how often did you use cannabis? [SR]  Every day  Once a week or more  About once a month  Every few months  Once or twice a year  Never [SKIP TO NEXT SELECTED SUBSTANCE ELSE MGhbTEXT6] MGhbQ24c. How many days in the last week have you used marijuana or cannabis? [SR]  0 days  1 day  2 days  3 days  4 days  5 days  6 days  7 days Cocaine MGhbQ25a. How old were you when you first used cocaine? [SR] ______ (age) MGhbQ25b. In the past 12 months, how often did you use cocaine? [SR]  Every day  Once a week or more  About once a month  Every few months  Once or twice a year  Never (SKIP TO NEXT SELECTED SUBSTANCE ELSE MGhbTEXT6) MGhbQ25c. How many days in the last week have you used cocaine? [SR]  0 days

 1 day  2 days  3 days  4 days  5 days  6 days  7 days Methamphetamine/ amphetamine MGhbQ26a. How old were you when you first used methamphetamine/ amphetamine type stimulants? [SR] ______ (age) MGhbQ26b. In the past 12 months, how often did you use methamphetamine/ amphetamine type stimulants? [SR]  Every day  Once a week or more  About once a month  Every few months  Once or twice a year  Never (SKIP TO NEXT SELECTED SUBSTANCE ELSE MGhbTEXT6) MGhbQ26c. How many days in the last week have you used methamphetamine/ amphetamine type stimulants? [SR]  0 days  1 day  2 days  3 days  4 days  5 days  6 days  7 days Inhalants MGhbQ27a. How old were you when you first used inhalants? [SR] ______ (age) MGhbQ27b. In the past 12 months, how often did you use inhalants? [SR]  Every day  Once a week or more  About once a month  Every few months  Once or twice a year  Never (SKIP TO NEXT SELECTED SUBSTANCE ELSE MGhbTEXT6) 302

MGhbQ27c. How many days in the last week have you used inhalants? [SR]  0 days  1 day  2 days  3 days  4 days  5 days  6 days  7 days

Sedatives or sleeping pills MGhbQ28a. How old were you when you first used sedatives or sleeping pills? [SR] ______ (age) MGhbQ28b. In the past 12 months, how often did you use sedatives or sleeping pills? [SR]  Every day  Once a week or more  About once a month  Every few months  Once or twice a year  Never (SKIP TO NEXT SELECTED SUBSTANCE ELSE MGhbTEXT6) MGhbQ28c. How many days in the last week have you used sedatives or sleeping pills? [SR]  0 days  1 day  2 days  3 days  4 days  5 days  6 days  7 days Hallucinogens MGhbQ29a. How old were you when you first used hallucinogens? [SR] ______ (age) MGhbQ29b. In the past 12 months, how often did you use hallucinogens? [SR]  Every day  Once a week or more  About once a month  Every few months  Once or twice a year

 Never (SKIP TO NEXT SELECTED SUBSTANCE ELSE MGhbTEXT6) MGhbQ29c. How many days in the last week have you used hallucinogens? [SR]  0 days  1 day  2 days  3 days  4 days  5 days  6 days  7 days Opioids MGhbQ30a. How old were you when you first used opioids? [SR] ______ (age) MGhbQ30b. In the past 12 months, how often did you use opioids? [SR]  Every day  Once a week or more  About once a month  Every few months  Once or twice a year  Never (SKIP TO NEXT SELECTED SUBSTANCE ELSE MGhbTEXT6)

MGhbQ30c. How many days in the last week have you used opioids? [SR]  0 days  1 day  2 days  3 days  4 days  5 days  6 days  7 days Other MGhbQ31a. How old were you when you first used these other drugs you previously mentioned in the above question that have not been listed in the categories above? [SR] ______ (age) MGhbQ31b. In the past 12 months, how often did you use these drugs? [SR]  Every day  Once a week or more  About once a month  Every few months 304

 Once or twice a year  Never (MGhbTEXT6) MGhbQ31c. How many days in the last week have you used these other drugs? [SR]  0 days  1 day  2 days  3 days  4 days  5 days  6 days  7 days [If yes to MGhbQ19 and/or MGhbQ20 show: MGhbTEXT5 (country specific) If you would like to talk to someone about any alcohol or other drug use problems, you can ring: 

Australia [include only in Australian Survey] o



Lifeline 

Telephone: 13 11 14



Website: www.lifeline.org.au



Crisis Support Chat: www.lifeline.org.au/Get-Help/Online-Services/crisis-chat

Kids Helpline 

Telephone: 1800 55 1800



Website: www.kidshelp.com.au



Email: counsellor@kidshelp.com.au



Web Counselling: www.kidshelp.com.au/teens/get-help/web-counselling/

beyondblue 

Telephone: 1300 22 4636



Website: www.beyondblue.org.au

Canada: [include only in Canadian Survey] o

Canadian Centre of Substance abuse 

Website: http://www.ccsa.ca/Eng/Pages/Addictions-Treatment-HelplinesCanada.aspxSuicide Action Montreal

Kids Help Phone 

Telephone:1800 668 6868



Website: www.kidshelpphone.ca



Live Chat counselling: www.kidshelpphone.ca/Teens/AskUsOnline/Chatcounselling.aspx



New Zealand: [include only in NZ Survey] o

Alcohol Drug Helpline: 

Telephone: 0800 787 797 (10am – 10pm)





Lifeline 

Telephone: 09 5222 999 (in Auckland) or 0800 543 354 (outside Auckland)



Website: www.lifeline.org.nz

Youthline 

Telephone: 0800 376 633



Website: www.youthline.co.nz



SMS: 234



Email: talk@youthline.co.nz

UK: [include only in UK Survey] o



Website: http://alcoholdrughelp.org.nz/helpline/

FRANK 

Telephone: 0300 123 6600



Website: http://www.talktofrank.com/



Email: frank@talktofrank.com



Live Chat:



SMS: 82111

US: [include only in US Survey] o

SAHMSA 

Telephone 1-800-662-HELP (4357)



Website: http://www.samhsa.gov/

Teen Help Line 

Telephone: 1800 400 0900]

MGhbTEXT6. v. GAMBLING MGhbQ32a. Have you gambled in the past 12 months?  No (SKIP TO MODULE H)  Yes MGhbQ32b. How often?  Every day  Once a week or more  About once a month  Every few months  Once or twice a year MGhbQ32c. Have you recently thought that you should cut down on gambling?  No  Yes MGhbQ32d. Have you recently had a friend, relative or doctor suggest that you should cut down on gambling? 306

 No  Yes [If yes to MGhbQ32c and/or MGhbQ32d show: MGhbTEXT7 (country specific)

[SUBMIT RESPONSES AND CONTINUE]

// MODULE H. Before you finish, we just need to briefly ask you some additional general information so we know a little more about who has answered our questionnaire. You’re almost done. [EACH ITEM IN MHdemo2Q1 IS SEPERATED BY COUNTRY SPECIFIC QUESTIONNAIRE] AUSTRALIA MHdemo2Q1a1. Is English the only language you speak? [SR] ☐ No (CONTINUE TO MHdemo2Q1a2) ☐ Yes (SKIP TO MHdemo2Q1a3) MHdemo2Q1a2. Which other languages do you speak? [SR]  Arabic  Cantonese  Croatian  Greek  Italian  Japanese  Mandarin  Macedonian  Serbian  Spanish  Tagalog/Filipino  Turkish  Vietnamese MHdemo2Q1a3. Are you of Aboriginal or Torres Strait Islander origin? [SR] ☐ No ☐ Yes, Aboriginal ☐ Yes, Torres Strait Islander ☐ Yes, Both Aboriginal and Torres Strait Islander

308

CANADA MHdemo2Q1b1. Are you an Aboriginal person, that is, First Nations (North American Indian), Métis or Inuk (Inuit)? ☐ No, not an Aboriginal person (CONTINUE TO MHdemo2Q1b2) ☐ Yes, First Nations(North American Indian) (SKIP TO MHdemo2Q2) ☐ Yes, Métis ☐ Yes, Inuk (Inuit) MHdemo2Q1b2. To which ethnic group(s) do you identify with most? (Peoples’ ethnicity describes their feeling of belonging and attachment to a distinct group of a larger population that shares their ancestry, language or religion) Please tick as many as you need to show which ethnic group(s) you feel you belong to. [MR OPTIONAL] ☐ White ☐ Chinese ☐ South Asian (eg. East Indian, Sri Lankan, Pakistani etc.) ☐ Black ☐ Filipino ☐ Latin American ☐ Southeast Asian (eg. Vietnamese, Cambodian, Malaysian, Laotian etc.) ☐ Arab ☐ West Asian (eg. Iranian, Afghan etc.) ☐ Japanese ☐ Korean ☐ Other group (please specify:

)

NEW ZEALAND MHdemo2Q1c. To which ethnic group(s) do you identify with most? (Peoples’ ethnicity describes their feeling of belonging and attachment to a distinct group of a larger population that shares their ancestry, language or religion) Please tick as many as you need to show which ethnic group(s) you feel you belong to. [MR OPTIONAL] ☐ New Zealand European ☐ Mäori ☐ Samoan ☐ Cook Islands Maori ☐ Tongan ☐ Niuean ☐ Tokelauan ☐ Fijian

☐ Other Pacific Peoples ☐ British ☐ Other European ☐ Southeast Asian ☐ Chinese ☐ Indian ☐ Other Asian ☐ Middle Eastern ☐ Latin American ☐ African ☐ Mixed background (please specify: ☐ Other (please specify:

) )

UNITED KINGDOM MHdemo2Q1di. To which ethnic group(s) do you identify with most? (Peoples’ ethnicity describes their feeling of belonging and attachment to a distinct group of a larger population that shares their ancestry, language or religion) Please tick as many as you need to show which ethnic group(s) you feel you belong to. [MR OPTIONAL] A: White (GO TO MHdemo2Q1dii) B: Mixed (GO TO MHdemo2Q1diii) C: Asian or Asian British (GO TO MHdemo2Q1div) D: Black or Black British (GO TO MHdemo2Q1dv) E: Other ethnic group (GO TO MHdemo2Q1dvi) MHdemo2Q1dii. A: White ☐ British ☐ Irish ☐ Any other White background (please specify:________________________________) MHdemo2Q1diii. B: Mixed ☐ White and Black Caribbean ☐ White and Black African ☐ White and Asian ☐ Any other mixed background (please specify: ________________________________) MHdemo2Q1div. C: Asian or Asian British ☐ Indian ☐ Pakistani ☐ Bangladeshi

310

☐ Chinese ☐ Any other Asian background (please specify: ________________________________) MHdemo2Q1dv. D: Black or Black British ☐ Caribbean ☐ African ☐ Any other Black background (please specify: ________________________________) MHdemo2Q1dvi. E: Other ethnic group ☐ Arab ☐ Any other (please specify: ________________________________)

UNITED STATES MHdemo2Q1e. To which ethnic group(s) do you identify with most? (Peoples’ ethnicity describes their feeling of belonging and attachment to a distinct group of a larger population that shares their ancestry, language or religion) Please tick as many as you need to show which ethnic group(s) you feel you belong to. [MR OPTIONAL] ☐ African-American ☐ Caribbean ☐ Caucasian ☐ Chinese ☐ Filipino ☐ Indian (South Asian) ☐ Japanese ☐ Korean ☐ Latino or Hispanic ☐ Native American or Aleut ☐ Native Hawaiian ☐ Middle Eastern ☐ Mixed background (please specify:

)

☐ Pacific Islanders ☐ Vietnamese ☐ Other (please specify:

MHdemo2Q2. Who do you live with? [SR] ☐ Live alone ☐ Live alone with child(ren) ☐ Live with partner and no child(ren)

)

☐ Live with partner and child(ren) ☐ Live with parents ☐ Live with other relatives ☐ Live with friends ☐ Live in shared accommodation ☐ Other (please specify: MHdemo2Q3.

)

How do you describe your sexual orientation? [SR] ☐ Heterosexual

☐ Gay/ lesbian

☐ Bisexual

☐ Other (please specify: )

MHdemo2Q4.

Which of the following best describes you? [SR] ☐ Never married ☐ Widowed ☐ Divorced ☐ Separated but not divorced ☐ Married/ de facto

MHdemo2Q5.

What beliefs would you describe as your religion? [SR]  Agnostic  Anglican  Atheist  Baptist  Buddhism  Catholic  Christian Orthodox  Christian other  Evangelical/Born Again  Hinduism  Humanist  Islam  Judaism  Latter-day Saints  Lutheran  Methodist  Native American  Pagan  Pentecostal  Presbyterian  Ratana

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 Sikh  Spiritualism and New Age Religions  Traditional (Aboriginal) Spirituality  Unitarian/Universalist  United Church  Wiccan  No religious affiliation  I’d prefer not to answer  Other:…………………………. MHdemo2Q6.

Are you [SR]:

☐ A patient, a consumer of health services, a health service user or a person with an illness ☐ A carer or family member of a person with an illness ☐ A health professional (eg. a doctor, nurse, psychologist, pharmacist) ☐ Other (please specify:

This final section asks about your knowledge of men’s health and men’s health organisations. MHdemo2Q7. How did you find out about this survey [MR] (you can choose more than 1 answer)? ☐A friend sent it to me (SKIP TO MHdemo2Q8a) ☐A family member sent it to me (SKIP TO MHdemo2Q8a) ☐A colleague sent it to me (SKIP TO MHdemo2Q8a) ☐An organisation [organization] sent it to me (CONTINUE TO MHdemo2Q7a) ☐Through a Facebook link (SKIP TO MHdemo2Q8a) ☐Through a twitter link (SKIP TO MHdemo2Q8a) ☐Via YouTube (SKIP TO MHdemo2Q8a) ☐Through an email link (SKIP TO MHdemo2Q8a) ☐Through other social media channels (CONTINUE TO MHdemo2Q7b) ☐Word of mouth (SKIP TO MHdemo2Q8a) MHdemo2Q7a. Which organisation [organization] sent you this survey?________________________________________ [GO TO MHdemo2Q8a] MHdemo2Q7b. Which social media channel(s) brought you to this survey?__________________________ MHdemo2Q8a. Have you seen, read or heard anything in the media about men’s health? [SR] ☐ No (SKIP TO MHdemoQ9a) ☐ Yes (CONTINUE TO MHdemo2Q8b)

)

MHdemo2Q8b. What have you seen, read or heard in the media about men's health?_________________ MHdemoQ9a. Have you heard of any organisations [organizations] related to men’s health? [SR] ☐ No (SKIP TO MHdemo2Q10a) ☐ Yes (CONTINUE TO MHdemo2Q9b) MHdemo2Q9b. Which organisations [organizations] related to men's health have you heard of?_____________________ [If answer ‘Movember’ SKIP TO MHdemo2Q10c] MHdemo2Q10a. Have you heard of Movember? [SR] ☐ No (CONTINUE TO MHdemo2TEXT2) ☐ Not sure (CONTINUE TO MHdemo2TEXT2) ☐ Yes – I have heard of Movember prior to completing this survey (SKIP TO MHdemo2Q10C) MHdemo2TEXT2. Movember is an initiative for men’s health. Funds are raised through the Foundation’s annual awareness campaign. By signing up with a clean-shaven face, and committing to growing a moustache over the 30 days of November, you can spark conversation and raise vital funds for the Movember Foundation’s men’s health programs in saving and improving the lives of men affected by prostate cancer, testicular cancer and mental health problems. MHdemo2Q10b. Have you heard of Movember before completing this survey? [SR] ☐ No ☐ Yes

MHdemo2Q10c. Have you ever participated in a Movember campaign? [SR] ☐ No (SKIP TO MODULE I) ☐ Yes (CONTINUE TO MHdemo2Q10d)

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MHdemo2Q10d. Do you remember when you participated in a Movember campaign? [SR]  2003  2004  2005  2006  2007  2008  2009  2010  2011  2012  2013  2014

[SUBMIT RESPONSES AND CONTINUE]

// MODULE I. Thank you very much for your time and comments. That is the end of the survey. MIrecoQ1. However, we would really like to invite you to answer more questions about men’s general health and wellbeing in the future. Would you be willing to be contacted again for future surveys? ☐ No (GO TO MIrecoQ2) ☐ Yes (CONTINUE TO MIrecoQ1a) MIrecoQ1a. What is your email address? Note, this information will not be used for any other purpose than to recontact you for this research. ________________ MIrecoQ2. Pass it on! Would you be willing to share this survey with your friends, colleagues, parents, brothers, sisters, children or grandparents to help make an impact on men's health globally?  No (GO TO MODULE J)  Yes, please email me a link I can share (CONTINUE TO MIrecoQ1a)  Yes, I will share via the social media links below (GO TO MODULE J) MIrecoQ2b. What is your email address?________________________________________________________

[SUBMIT RESPONSES AND CONTINUE]

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// MODULE J. If you have any complaints about this survey, they may be directed to The Manager, Human Ethics Administration at The University of Sydney (Australia). +61 2 8627 8176 (telephone) +61 2 8627 8177 (facsimile) ro.humanethics@sydney.edu.au (email) If you would like to speak to someone about any of the issues that were mentioned in this survey, you can contact: Lifeline Telephone: 13 11 14 Website: www.lifeline.org.au Crisis Support Chat: www.lifeline.org.au/Get-Help/Online-Services/crisis-chat Kids Helpline Telephone: 1800 55 1800 FREE Website: www.kidshelp.com.au Email: counsellor@kidshelp.com.au Web Counselling: www.kidshelp.com.au/teens/get-help/web-counselling/ beyondblue Telephone: 1300 22 4636 Website: www.beyondblue.org.au Gambling Help Online Telephone: 1800 858 858 FREE Website: http://www.gamblinghelponline.org.au/ For taking the time to complete this survey and helping us better understand global health and wellbeing, hereâ&#x20AC;&#x2122;s a game for you â&#x20AC;&#x201C; check out SpaceBall Pong and compete with other survey participants all around the world. Good luck! SpaceBall Pong

// Country specific differences US Version MAdemo1Q5c – defense MBknowQ1a – behavioral MBknowQ1b – behavioral MBknowQ2a – behavioral MBknowQ2b – behavioral MDmescQ3c – criticize MDmescQ5 – behaviors MEhsQ11c – Counselor MEhsQ11c – Dietician MEhsQ11d – Counselor MEhsQ11d – Dietician MEhsQ12c – Counselor MEhsQ12c – Dietician MEhsQ12d – Counselor MEhsQ12d – Dietician MEhsQ13c – Counselor MEhsQ13c – Dietician MEhsQ13d – Counselor MEhsQ13d – Dietician MGhbQ7 – confectionary, cakes, cookies, potato crisps MGhbQ7 – fish and chips, French fries, pizza, hot dogs, hamburgers MHdemo2Q7 – organization MHdemo2Q7a – organization MHdemo2Q9a – organizations MHdemo2Q9b – organizations Canadian version MDmescQ3c – criticize MGhbQ7 – ‘confectionary, cakes, cookies, potato crisps’ MGhbQ7 – ‘fish and chips, French fries, pizza, hot dogs, hamburgers’ MHdemo2Q7 – organization MHdemo2Q7a – organization MHdemo2Q9a – organizations MHdemo2Q9b – organizations Note: words in brackets denote US spelling

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