Abstract
BACKGROUND: Compassion fatigue is a significant issue in nursing, affecting both registered nurses and nursing students, potentially leading to burnout and reduced quality of care. During internships, compassion fatigue can shape nursing students' career trajectories and intent to stay in the profession. Identifying those at high risk is crucial for timely interventions, yet existing tools often fail to account for within-group variability, limiting their ability to accurately predict compassion fatigue risk. OBJECTIVES: This study aimed to develop and validate a predictive model for detecting the risk of compassion fatigue among nursing students during their placement. DESIGN: A cross-sectional study was used to capture the prevalence and associations of compassion fatigue among nursing interns, as it allows for timely assessment of key influencing factors without requiring long-term follow-up. METHODS: A convenience sampling strategy was used to recruit 2256 nursing students from all ten public junior colleges in Hunan province in China between December 2021 and June 2022. Participants completed questionnaires assessing compassion fatigue, professional identity, self-efficacy, social support, psychological resilience, coping styles, and demographic characteristics. Predictors were selected based on prior literature and theoretical frameworks related to compassion fatigue in nursing. Latent profile analysis was used to classify compassion fatigue levels, and potential predictors were identified through univariate analysis and least absolute shrinkage and selection operator (LASSO) regression. Eight machine learning algorithms were applied to predict compassion fatigue, with performance assessed through cross-validation, calibration, and discrimination metrics. The best-performing model was further validated to ensure robustness. RESULTS: A three-profile model best fits the data, identifying low (55.73%), moderate (32.17%), and severe (12.10%) profiles for compassion fatigue. Generally, an area under the curve (AUC) above 0.700 is acceptable, and above 0.800 indicates good predictive performance. The AUC values for the eight machine learning models ranged from 0.644 to 0.826 for the training set and 0.651 to 0.757 for the test set, indicating moderate to good discriminatory ability. The eXtreme Gradient Boosting (XGBoost) performed best, with AUC values of 0.840, 0.768, and 0.731 in the training, validation, and test sets, respectively. Shapley Additive Explanation (SHAP) analysis interpreted the model by quantifying the contribution of each variable to the prediction, revealing that psychological resilience, professional identity, and social support were the key contributors to the risk of compassion fatigue. A user-friendly, web-based prediction tool for calculating the risk of compassion fatigue was developed. CONCLUSIONS: The XGBoosting classifier demonstrates excellent performance, and implementing the online tool can help nursing administrators manage compassion fatigue effectively. It holds practical value for nursing education and practice by supporting early detection and intervention. Future research should validate its use across settings, and longitudinal studies could assess its long-term impact.