Compassion Fatigue in Emergency Department Nurses: A Cross-Sectional Evaluation of Predictive Models

Compassion fatigue (CF) is a potential risk for Emergency Department (ED) nurses when workload increases, they experience repeated exposure to trauma or have a long-term emotional burden. A cross-sectional study was conducted to explore the connection between workload, trauma exposure, coping, and compassion satisfaction, and to assess the performance of logistic regression, Random Forest, and Support Vector Machine for internal binary classification. The sample included 150 full-time registered ED nurses from three tertiary hospitals, who completed a structured self-administered questionnaire. The analyses encompassed descriptive statistics, group comparisons, multiple linear regression, train-test split (80:20), and stratified five-fold cross-validation. The relationships between workload and trauma exposure were positive (r = 0.75 and r = 0.62, respectively), while the relationships between coping mechanisms and compassion satisfaction were negative (r = -0.52 and r = -0.60, respectively) with CF. The four-predictor regression model explained most of the variance in CF (adjusted R² = 0.852), with coping and compassion satisfaction being the strongest negative predictors (β = -0.25; β = -0.35), and trauma exposure and workload being the strongest positive predictors (β = 0.32; β = 0.42). Random Forest outperformed Support Vector Machine (accuracy 83%, ROC-AUC 0.83) and logistic regression (accuracy 80%, ROC-AUC 0.80) in terms of internal classification (accuracy 85%, precision 82%, recall 80%, F1-score 81%, ROC-AUC 0.84). These findings suggest that the following interventions can be used to reduce compassion fatigue among ED nurses: workload management, trauma-informed support, coping, and compassion satisfaction.