Bibliometric analyses of turnover intention among nurses: implication for research and practice in China

The impact of moral resilience on nurse turnover intentions: the mediating role of job burnout in a cross-sectional study

BACKGROUND

High nursing staff turnover is a critical issue that negatively impacts the quality of care and patient safety. Turnover intentions, a key predictor of actual turnover, reflect an individual's likelihood of leaving their current position. Understanding the underlying mechanisms driving nurse turnover intentions is crucial for developing targeted interventions to stabilize the nursing workforce globally.

OBJECTIVES

This study aims to explore the relationship between moral resilience, job burnout, and turnover intentions among nurses, focusing on the mediating role of job burnout in this relationship.

DESIGN

This study employed a quantitative, cross-sectional design.

METHODS

A convenience sample of 322 registered nurses was recruited from two tertiary hospitals in China between August and October 2023. Data were collected using the Chinese version of the Rushton Moral Resilience Scale (Chi-RMRS), the Maslach Burnout Inventory (MBI), and the Turnover Intention Scale. Data analyses were conducted using SPSS 26.0 and Amos 21.0. The study followed the STROBE guidelines for observational research.

ETHICAL CONSIDERATION

Before the commencement of data collection, the Institutional Review Board of Hunan Traditional Chinese Medical College (YXLL202401004) granted ethical approval.

RESULTS

The proposed model exhibited an excellent fit to the data, with fit indices as follows: χ2/df = 1.819, CFI = 0.977, TLI = 0.961, RMSEA = 0.072 (90% confidence interval [CI]: 0.033 to 0.107). The structural equation model revealed that moral resilience was inversely associated with job burnout and turnover intentions. Furthermore, job burnout fully mediated the relationship between moral resilience and turnover intentions (β = -0.473, p = 0.007). Further analysis indicated that the depersonalization component of job burnout was the sole mediator in the relationship between moral resilience and turnover intention (β = -3.934, 95% CI [-5.837, -1.932]).

CONCLUSION

The findings indicate that moral resilience among nurses is negatively associated with turnover intentions, with this effect fully mediated by the depersonalization dimension of job burnout. Enhancing moral resilience in nurses may be valuable strategy for healthcare administrators to mitigate job burnout and subsequently reduce turnover intentions.

Predicting Nurse Turnover for Highly Imbalanced Data Using the Synthetic Minority Over-Sampling Technique and Machine Learning Algorithms

Predicting nurse turnover is a growing challenge within the healthcare sector, profoundly impacting healthcare quality and the nursing profession. This study employs the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance issues in the 2018 National Sample Survey of Registered Nurses dataset and predict nurse turnover using machine learning algorithms. Four machine learning algorithms, namely logistic regression, random forests, decision tree, and extreme gradient boosting, were applied to the SMOTE-enhanced dataset. The data were split into 80% training and 20% validation sets. Eighteen carefully selected variables from the database served as predictive features, and the machine learning model identified age, working hours, electric health record/electronic medical record, individual income, and job type as important features concerning nurse turnover. The study includes a performance comparison based on accuracy, precision, recall (sensitivity), F1-score, and AUC. In summary, the results demonstrate that SMOTE-enhanced random forests exhibit the most robust predictive power in the classical approach (with all 18 predictive variables) and an optimized approach (utilizing eight key predictive variables). Extreme gradient boosting, decision tree, and logistic regression follow in performance. Notably, age emerges as the most influential factor in nurse turnover, with working hours, electric health record/electronic medical record usability, individual income, and region also playing significant roles. This research offers valuable insights for healthcare researchers and stakeholders, aiding in selecting suitable machine learning algorithms for nurse turnover prediction.