Linking interprofessional work to outcomes for employees: A meta-analysis.
Sabine KaiserJoshua PatrasMonica MartinussenPublished in: Research in nursing & health (2018)
The aim of this meta-analysis of studies of workers in the health and social care sector was to examine the relationship between interprofessional work and employee outcomes of job stress, autonomy, burnout, engagement, job satisfaction, turnover intention, and perceived service quality, and to examine the influence of different moderators on those relationships. A systematic literature search of the PsycInfo, Embase, Medline, and the Cumulative Index to Nursing and Allied Health Literature databases was conducted to identify relevant articles. A total of 45 articles with results for 53 independent samples was included in the meta-analysis. A random effects model was used to estimate the mean effect sizes (correlations). Most employees were nurses working in hospitals. Interprofessional work was weakly negatively associated with job stress, burnout, and turnover intention (range mean r = -.13 to -.22); and was moderately positively associated with autonomy, engagement, job satisfaction, and perceived service quality (range mean r =.33 to .46). When feasible, interprofessional work was categorized as teamwork (most intensive), collaboration, or cooperation. Teamwork, the most intense of three forms of interprofessional work, promoted lower burnout and turnover intention. The results of this meta-analysis suggest that interprofessional work is linked to better well-being for employees in health and social care.
Keyphrases
- healthcare
- mental health
- systematic review
- patient safety
- social support
- quality improvement
- nursing students
- depressive symptoms
- public health
- case control
- health information
- social media
- bone mineral density
- palliative care
- meta analyses
- physical activity
- metabolic syndrome
- affordable care act
- pain management
- type diabetes
- body composition
- insulin resistance
- skeletal muscle
- health insurance
- stress induced
- climate change
- heat stress
- machine learning
- randomized controlled trial