Anastasia Mallidou PhD, Greta Cummings RN PhD, Carole Estabrooks RN PhD, Phyllis Giovannetti RN PhD *Original
publication: Mallidou, A. A., Cummings, G. G., Estabrooks, C. A., & Giovannetti, P. B. (2011). Nurse specialty subcultures and patient outcomes in acute care hospitals: A multiple-group structural equation modeling. International Journal of Nursing Studies, 48(1), 81-93. Doi: 10.1016/j.ijnurstu.2010.06.002
Background
The 4-group stacked model - All background variables covary
SEM - Matrix Σ and Matrix S: Covariances (below the diagonal) & Variances (the diagonal)
Organizational culture can be illustrated by the story of seven blind men and the elephant. Hospital organizational culture matters to effective services, their delivery, system performance, and outcomes. Little empirical evidence exists, though, to support culture effects on provider and patient outcomes or how this occurs.
Σ=
Purpose
a) To develop and test a theoretical framework on hospital culture and subcultures such as nurse specialty subcultures (NSSCs), and b) To explore causal relationships among a number of NSSCs and selected patient (i.e., quality of care, adverse patient events) and nurse (i.e., job satisfaction) outcomes. Research questions: • “Are there any NSSCs within acute care hospitals in Alberta?” • “Do several NSSCs differentially influence nurse and patient outcomes?” • “What are the mechanisms (how) between NSSCs and outcomes?”
S=
Exp: Experience FT/PT: Full-time / Part-time
Salary: Satisfactory salary CE: continuing education QAP: Quality assurance program
Formal practices Aut: Autonomy Con: Control over practice RN-MD: Nursephysician relations
Endogenous Variables (DVs) JS: Job satisfaction
QOC: Quality of care
JS
QOC
Exp
FT/PT
APEs: Adverse patient events
QAP
Prec
Con
Med
-0.085*
Surg
-0.082*
0.232**
-0.193**
0.146*
ICU
Job Satisfaction
ER
-0.091*
Med
Prec: Preceptorship
0.895 -0.285 -0.023 0.032 -0.026 -0.061 -0.079 -0.136 -0.216 -0.135 1.358 0.019 -0.058 -0.052
0.724 0.139 0.069 0.054 0.380 0.655 0.181 -2.328 0.002 0.001 -0.024
0.827 0.228 0.197 0.574 0.901 0.217 -2.390 0.005 -0.018 0.015
0.589 0.167 0.455 0.732 0.176 -1.831 0.005 0.004 -0.004
0.948 0.434 0.718 0.253 -1.839 -0.025 0.062 0.008
2.843 2.763 8.104 0.979 1.277 -7.344 -14.647 -0.013 -0.012 0.046 0.191 -0.041 -0.146
1.762 -3.244 123.918 -0.096 -0.030 0.074 -0.672 0.081 0.429
0.218 -0.075 -0.049
0.180 -0.036
0.130
χ2 = 24.18 p = 0.115 df = 17
29.201 0.132 0.233 0.007 0.027 0.722 -0.213 0.045 0.139 0.825 0.177 0.027 0.065 0.216 0.588 0.174 0.000 0.043 0.191 0.148 0.964 -0.352 0.003 0.368 0.537 0.451 0.396 2.826 -1.068 0.044 0.662 0.893 0.729 0.643 2.701 8.082 -0.020 -0.018 0.174 0.205 0.178 0.226 0.936 1.249 1.773 -1.664 -0.566 -2.110 -2.092 -1.592 -1.678 -6.752 -3.636 -2.558 18.016 -0.001 0.015 -0.009 0.010 0.003 -0.024 -0.017 -0.055 -0.112 0.049 0.213 0.026 -0.028 0.008 -0.023 0.005 0.068 0.056 0.231 0.084 -0.769 -0.076 0.186 0.176 0.004 -0.026 0.014 -0.004 0.008 -0.045 -0.153 0.087 0.475 -0.048 -0.038
0.131
References
Findings
Exogenous Variables (IVs) Informal practices
0.425 -0.105 0.088 -0.005 0.061 0.087 0.115 0.087 0.338 0.818 0.210 -2.181 -0.013 0.061 -0.035
0.235 0.032 0.037 0.027 -0.006 0.002 0.016 -0.033 -0.547 0.016 -0.026 0.003
Nurse specialty subcultures not only exist within hospitals, but they differentially influence nurse job satisfaction, quality of care and adverse patient events. Understanding the meaning of subcultures in clinical settings would affect nurses’ and administrators’ efforts to implement clinical change on complexity of care that influences outcomes.
Martin’s (2002) theoretical framework on nested subcultures within organizations A large nurse survey dataset in acute care hospitals in Alberta, Canada Four NSSCs identified: Medical, Surgical, ICU, and Emergency Subcultures operationalized based on: Formal practices, Informal practices, and Content themes Analysis: Structural Equation Modeling (SEM) with LISREL.
Demographics
0.926 -0.345 30.631 -0.020 0.126 0.020 -0.037 -0.052 -0.190 -0.055 0.169 -0.076 0.254 -0.171 -0.368 -0.267 -1.084 -0.154 -0.006 1.493 -1.985 0.003 -0.014 -0.051 0.033 -0.041 0.193
Conclusion & Implications
Methods
Content themes EE: Emotional exhaustion
433 100 070 004 046 098 113 094 354 816 216 166 006 051 033
0.082*
Surg
0.209**
ICU
0.214**
ER
0.183*
Med
0.145*
0.357**
Quality of Care 0.086*
Surg
-0.110**
ICU
-0.202**
ER
-0.296** -0.136*
0.085* -0.104*
-0.140* 0.081*
-0.178*
-0.178*
EE
R2 (%)
-0.536**
43.6
-0.486**
45.1
0.215**
-0.384**
35.7
0.120*
-0.557**
52.4
24.0
0.303**
29.2 39.5
0.179* -0.115*
Adverse Patient Events
-0.164**
23.6
0.106*
0.281**
0.432** -0.140** -0.104*
RN-MD
0.231**
9.2 6.0 10.8 17.4
Giovannetti, P., Estabrooks, C.A., Hesketh, K.L., 2002. Alberta Nurse Survey Final Report (Report No. 02-01-TR). University of Alberta, Faculty of Nursing, Edmonton, AB. Hayduk, L.A., 1987. Structural equation modeling with LISREL. Essential and advances. Boston, MA: Johns Hopkins University Press. Martin, J. (Ed.). (2002). Organizational culture: Mapping the terrain. Thousand Oaks, CA: Sage Publications.
Acknowledgements
• Hellenic (Greek) State Scholarships Foundation (I.K.Y.) • Alberta Heritage Foundation for Medical Research (AHFMR) • Canadian Health Services Research Foundation (CHSRF) • Canadian Institutes of Health Research (CIHR) • Faculty of Nursing, University of Alberta • Dr. Karen Golden-Biddle & Dr. Les Hayduk
Further Information Anastasia Mallidou: mallidou@uvic.ca