Front-Line Registered Nurse Job Satisfaction and Predictors: A Meta-Analysis from 1980 - 2009

Front-line registered nurses (RNs) make up the workforce that directly affect the care of patients in a variety of different healthcare settings. RN job satisfaction is important because it is tied to retention, organizational commitment, workforce safety, patient safety, and cost savings. The strongest predictors have been difficult to determine because workplaces differ, numerous tools to measure satisfaction exist, the workforce is diversified by generations and work positions, and ongoing policy changes directly impact the work of the front-line RN. The strength and stability of the workforce depends on an accurate understanding of the predictors of job satisfaction for the front-line RN. The purpose of this study was to comprehensively, quantitatively examine predictors of front-line RN job satisfaction from 1980-2009 to provide overarching conclusions based on empirical evidence. Of interest was: the (1) estimation of large, moderate, and small predictor summary effect sizes; (2) assessment of predictor differences among decades (i.e., 1980s, 1990s, and 2000s); (3) identification of causes for predictor differences among studies (i.e., moderators); and (4) investigation of predictor differences between generations (i.e., Baby Boomers, Generation X, and Millennials).A non-a priori meta-analysis approach was guided by inclusion and exclusion criteria to review published and unpublished studies from 1980(-)2009. The search process identified 48 published and 14 unpublished studies used for analysis. Within the studies that met inclusion criteria, 27 job satisfaction predictors met inclusion for analysis. Studies were coded for Study Characteristics (e.g., Year of Publication, Country of Study) that were needed for moderator analysis. Predictors were coded for data that were necessary to calculate predictor summary effect sizes (i.e., r, n). Coding quality was maximized with a coding reliability scheme that included the primary investigator (PI) and secondary coder. A random-effects model was used to guide the ...