Development and validation of a nomogram for predicting high-burnout risk in nurses

Abstract

Abstract Background Nurses are one of the occupational populations that are most susceptible to burnout and high-burnout nurses experienced significantly impacts their physical and mental health, and even compromises patient safety and the quality of care. Therefore, it is crucial to identify and prevent burnout among nurses at an early stage. Developing a predictive model for high-burnout is essential for this purpose. Methods A cross-sectional study was conducted among 2,750 Chinese nurses using an online survey. Data were collected by the 15-item Chinese Maslach Burnout Inventory-General Survey (CMBI-GS) and self-administered questionnaires that included demographic, behavioral, health-related, and occupational variables. Multivariate logistic regression analysis and nomogram were used to identify the factor associated with high-burnout risk. Stata 16.0 software was used for data analysis. Results A total of 2,750 nurses from 23 provinces of mainland China were included, with 1,925 participants (70%) in a development set and 825 participants (30%) in a validation set. Workplace violence, shift work, working time per week, depression, stress, self-reported health, and drinking were significant contributors to high-burnout risk and a nomogram was developed using these factors. The receiver operating characteristic (ROC) curve analysis demonstrated that the area under the curve (AUC) of the model was 0.808 in the development set and 0.790 in the validation set. For calibration analysis, the Hosmer-Lemeshow tests produced P values of 0.697 and 0.640 in the two sets, respectively. The nomogram demonstrated a high net benefit in the clinical decision curve in both sets. Conclusion This study has developed and validated a predictive nomogram for identifying high-burnout in nurses. The nomogram will assist nursing managers in identifying at-high-risk nurses, understanding related factors and implementing early interventions. Additionally, our study provides a tool for nurses to monitor their risk of high-burnout and overall mental health.