ArticleBMC nursing2025
Establishment and validation of a prediction model for compassion fatigue in nursing students.
Article in BMC nursing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Protocol: Psychosocial Interventions Addressing Mental Health Outcomes Among Hospital-Based Healthcare Workers in United States Hospitals: A Systematic Review and Meta-Analysis.Campbell systematic reviews · 2026Review
- Development and Internal Validation of an Explainable Machine Learning Model for Compassion Fatigue Risk Stratification Among Clinical Nurses in China.Risk management and healthcare policy · 2026Article
- The effect of self-efficacy on compassion fatigue among nursing interns: The mediation of psychological resilience and social support.Medicine · 2025Article
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2 authors.
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Abstract
backgroundCompassion fatigue is a common issue nursing students face during clinical internships. Prolonged exposure to patients' suffering and trauma can lead to emotional exhaustion and psychological stress. Compared to formal healthcare workers, nursing students have less professional experience and weaker emotional regulation abilities, making them more vulnerable to secondary trauma and other negative emotions, which exacerbates compassion fatigue. Early identification and intervention in compassion fatigue are crucial for improving the mental health of nursing students and the quality of care they provide.
objectiveThis study aims to develop a predictive model for compassion fatigue in nursing students using various statistical and machine learning methods, identify key influencing factors, and provide scientific evidence for nursing educators and administrators.
methodsA cross-sectional survey collected valid questionnaire data from 512 nursing students. LASSO regression was used to select critical variables, and models such as logistic regression, random forest, and XGBoost were applied for prediction. Model performance was evaluated, and SHAP values were used to interpret the importance of model features.
resultsThe logistic regression model performed best on the test set with an AUC value 0.77. Key predictive factors included psychological resilience, peer support, secondary trauma, and empathy satisfaction.
conclusionThis study successfully developed a predictive model for compassion fatigue in nursing students, with the logistic regression model showing high accuracy. The critical factors identified provide theoretical support for early interventions, aiding in more targeted nursing management and enhancing the mental well-being of nursing students.
trial registrationNot applicable. This study is an observational study aimed at investigating compassion fatigue among students, without involving any interventions or treatment methods. Therefore, this study does not meet the definition of a clinical trial and does not require registration of a clinical trial number.
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