ArticleBMC geriatrics2026
High prevalence and predictive modeling of compassion fatigue in geriatric nursing practice.
Article in BMC geriatrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundCompassion fatigue (CF) significantly affects nurses’ workplace psychology and care quality, yet its status among geriatric nurses is underexplored and challenging to manage.
objectiveThis study examines the prevalence of CF in geriatric nurses and develops a predictive model.
methodsGeriatric nurses from 90 hospitals across China were surveyed using four scales to assess CF prevalence. Univariate and multivariate logistic regression and Pearson correlation analyses identified key factors. Multiple machine learning algorithms were used to construct predictive models, validated internally and externally.
resultsCF prevalence was 92.8%, primarily at moderate to severe levels. Key risk factors included middle and night shift, average daily work hours, income satisfaction, neuroticism, extraversion, and support utilization. Thirteen variables were selected via Lasso and Boruta for model building. The Extreme Gradient Boosting model outperformed Support Vector Machines and K-Nearest Neighbors. The optimal Naive Bayes model achieved internal validation accuracy of 0.75, precision 0.87, F1 score 0.75, specificity 0.90, sensitivity 0.74, and AUC 0.96. SHAP analysis highlighted neuroticism, agreeableness, and extraversion as key contributors. A web application was developed for management support.
conclusionThis multicenter study identified high CF prevalence in geriatric nurses and created a concise, interpretable predictive model, providing a tool for nursing management.
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