Analyzing the Drivers of Job Satisfaction and Nurse Retention Among Kazakh Nurses in Public Clinical Centers: Key Factors and Insights

2026

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DOI 10.1155/nuf/4032456
Data confidence 70 medium
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Abstract

Background Global nurse shortages are well documented, yet empirical evidence from Central Asia remains scarce. In Kazakhstan, where the health system is undergoing reform, little is known about the factors shaping nurses’ job satisfaction and retention. Objectives This study aimed to (1) assess overall levels of job satisfaction and retention intention among nurses in Kazakhstan, (2) examine the association between practice environment characteristics and nurses’ job satisfaction, and (3) identify demographic and professional factors influencing job satisfaction. Methods A cross‐sectional survey was conducted with 359 nurses using the Casey–Fink Nurse Retention Survey and the Practice Environment Scale of the Nursing Work Index (PES‐NWI). Retention intention was assessed using Casey–Fink items that ask nurses about their plans to remain in their current position and within the profession. Data were analyzed with descriptive statistics, Pearson correlations, and multiple regression models to identify predictors of job satisfaction. Results Recognition and rewards, mentorship, scheduling flexibility, and professional role were significant predictors of job satisfaction ( p < 0.05). Recognition and mentorship demonstrated a strong correlation ( r = 0.84). Limited scheduling flexibility negatively affected satisfaction, while the overall regression model explained a meaningful proportion of variance in outcomes. Conclusion These findings underscore the importance of mentorship, recognition, and flexible scheduling as key drivers of job satisfaction, with potential implications for improving retention intentions. These findings highlight the need for targeted workforce strategies that strengthen recognition systems, mentorship programs, and flexible scheduling policies. Future longitudinal research is recommended to examine causal relationships and evaluate the effectiveness of interventions.