Establishment and validation of a prediction model for compassion fatigue in nursing students

Compassion 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.

This 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.

A 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.

The 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.

This 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.

Not 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.