program. Additionally, it is common and acceptable that most evaluations result in mainly correlational, not causal, findings, as most program evaluation activities will not include an experimental research design. Thus, when communicating about the results of the data, avoid using general statements or words like “proof” or “cause.” Instead, provide factual statements about the perceived program successes and challenges, which can allow for the development of lessons learned and next steps. Furthermore, you may use previous case studies or examples to help make inferences to the results of the study. Key themes that emerge from qualitative data collection allow for an in-depth understanding of study findings. Including powerful quotes and anecdotes retrieved from qualitative data collection are likely to engage a wider range of audiences more than numerical results alone. Thus, it is essential to use a range of creative methods to share qualitative data, including visual outputs, such as pictures, clipart and other images that are unique and specific to the evaluation. This technique often results in a more personal and relatable presentation. Compelling and influential methods of communicating qualitative data results provide a storytelling opportunity behind quantitative data results, which is a reason behind why mixed-methods data collection is key to multifaceted results. Common target audiences include the general population (i.e., public, community members), the media and other important stakeholders (i.e., government officials, funders, academics, policymakers). In preparation for communicating the results through written, visual or verbal methods, it is important to tailor the messaging to your audience. For example, it is important to use accessible language without technical jargon to the general population, while using more professional language may be beneficial to institutional audiences — such as, policymakers or government officials. Effectively communicating data results assists audiences in recognizing the importance of not only the results, but also the program itself and potentially could influence them to support the program. Therefore, it is critical to consider the target audience and appropriately interpret the results in a truthful and effective manner.
Part E. Health Metrics
Exhibit 4. NRPA’s Seven Dimensions of Well-Being
This section introduces the seven dimensions of well-being (see Exhibit 4) included in NRPA’sCommunity Wellness Hub toolkit (learn more at nrpa.org/CommunityWellnessHubs). This displays our vision of the interconnectedness and interdependence of various well-being domains that are essential to consider when creating and evaluating a Community Wellness Hub. Additionally, this section provides a description and examples of quantitative measures for each of the seven domains. Table 9 presents the description of each health domain, examples of outcomes within each domain and existing validated measures that target the domain. These quantitative health metrics can be challenging to collect at a community level because they tend to need more resources for data collection (i.e., funding, staff). Using secondary public data sources and/or qualitative data are alternatives that provide additional details about the health of the community. Even then, however, data need to be interpreted with caution. Many national data sets are collected using nationally representative samples, which means that results often cannot be directly interpreted at the community level. Some sources, such as the County Health Rankings,60 try to overcome this challenge by modeling data to smaller geographic levels. While helpful, it is important to recognize that data are, in fact, modeled, and may not be an accurate representation of community-level health behaviors and outcomes.
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