ArticleJournal of clinical nursing2025
Development and validation of a nomogram for predicting high-burnout risk in nurses.
Article in Journal of clinical nursing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Identifying Key Predictors and Developing a Machine Learning Model for Nurse Burnout in China.Journal of nursing management · 2026Article
- Latent Profile Analysis of Professional Quality of Life and Its Relationship With Posttraumatic Growth Among Nurses: A Cross-Sectional Study.Journal of nursing management · 2026Article
- The rising tide of workplace violence in the healthcare sector: a global scoping review of burden, determinants, and prevention strategies (2021-2025).Frontiers in public health · 2026Article
- Development and Validation of a Nomogram-Based Risk Stratification Model for Moderate-to-High Frequency Workplace Violence Among Chinese Pediatric Nurses: A Cross-Sectional Study.Journal of nursing management · 2026Article
- Development and validation of a nomogram for predicting high-burnout risk in nurses.Journal of clinical nursing · 2025Article
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Authors and funding
8 authors.
Funding
Abstract
aimTo develop a predictive model for high-burnout of nurses.
designA cross-sectional study.
methodsThis 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.
resultsA 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.
conclusionThis 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
methodThe 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.
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