Abstract
AIM: To develop a predictive model for high-burnout of nurses. DESIGN: A cross-sectional study. METHODS: This study was conducted using an online survey. Data were collected by the Chinese Maslach Burnout Inventory-General Survey (CMBI-GS) and self-administered questionnaires that included demographic, behavioural, health-related, and occupational variables. Participants were randomly divided into a development set and a validation set. In the development set, multivariate logistic regression analysis was conducted to identify factors associated with high-burnout risk, and a nomogram was constructed based on significant contributing factors. The discrimination, calibration, and clinical practicability of the nomogram were evaluated in both the development and validation sets using receiver operating characteristic (ROC) curve analysis, Hosmer-Lemeshow test, and decision curve analysis, respectively. Data analysis was performed using Stata 16.0 software. RESULTS: A total of 2750 nurses from 23 provinces of mainland China responded, with 1925 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 ROC curve analysis demonstrated that the area under the curve of the model was 0.808 in the development set and 0.790 in the validation set. 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. RELEVANCE TO CLINICAL PRACTICE: The nomogram conducted by our study will assist nursing managers in identifying at-high-risk nurses and understanding related factors, helping them implement interventions early and purposefully. REPORTING METHOD: The study adhered to the relevant EQUATOR reporting guidelines: TRIPOD Checklist for Prediction Model Development and Validation. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.