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.

Introduction: This study explores how students develop, negotiate and represent their mental health alongside their professional identities. The aim is to understand how nursing students construct and negotiate their mental health identities in academic and clinical environments. Method: Using Interpretative Phenomenological Analysis (IPA), this qualitative research employed nine nursing students enrolled in the Bachelor of Science in Nursing (BSN) programme at King Khalid University in Saudi Arabia. Semi-structured interviews were conducted in Arabic, the participants’ native language, and professionally translated to English for analysis. Interviews were analyzed using a six-stage IPA protocol and an integrated coding framework operationalizing concept from Social Identity Theory, Symbolic Interactionism, and Identity Negotiation Theory. Results: The data led to five themes: (1) Navigating the Stigma Boundary: The ‘tough nurse’ imperative, strategic silence or selective disclosure as identity management. (2) Developing the ‘Mental Health’ Label: Repertoires and levels of surveillance/monitoring. (3) The Clinical–Classroom Divide: Contexts affecting identity performance and role transitions. (4) Help-Seeking as Identity Betrayal: The cost to identity of seeking formal help and preference for informal support. (5) Reconciling Personal and Professional Identities: Future self-prototype construction, divergent personal–professional developments, and post-trauma story-telling. Conclusion: Nursing students’ mental health identities are socially constructed and strategically negotiated across contexts. This multi-theoretical model advances understanding beyond deficit-oriented frameworks, showing that help-seeking is limited by identity barriers.

Background: Cyberchondria, characterized by excessive or repeated online searching for health-related information accompanied by heightened health concerns or distress, is an increasingly recognized behavioral phenomenon. Healthcare students represent an important population for investigation due to their frequent exposure to medical information and digital health resources. However, the extent of cyberchondria-related behaviors and their relationship with demographic and academic characteristics remain insufficiently explored in this population. Objectives: This study aimed to describe the level and distribution of cyberchondria-related behaviors and to explore whether selected demographic and academic characteristics were associated with CSS-12 scores among undergraduate female healthcare students from different academic programs at a Saudi university. Methods: A cross-sectional descriptive correlational design was employed. A proportionate stratified non-probability convenience sample of 362 female students was recruited from six health-related colleges. Data were collected using the validated Cyberchondria Severity Scale (CSS-12), which measures four dimensions: excessiveness, distress, reassurance, and compulsion. Descriptive statistics, Pearson correlation, and multiple linear regression were applied using SPSS version 29. Results: The overall CSS-12 mean score indicated a moderate level of cyberchondria-related behaviors (M = 32.46, SD = 5.80). At the dimensional level, Excessiveness recorded the highest mean score (M = 10.29, SD = 2.10), followed by Distress (M = 7.46, SD = 2.35), while Reassurance (M = 7.73, SD = 2.20) and Compulsion (M = 6.98, SD = 2.05) showed comparatively lower scores. Based on the sample-derived score distribution, 23.8% of participants were classified in the upper-score group, while 30.4% and 45.9% were classified in the middle- and lower-score groups, respectively. Correlation analysis revealed significant positive relationships among all CSS-12 dimensions (p < 0.001), with correlation coefficients ranging from weak to strong (r = 0.31–0.56). Multiple linear regression analysis showed that age, GPA, academic year, and academic program were not significantly associated with CSS-12 scores (p > 0.05), and the model explained a very small proportion of variance (R² = 0.006), indicating limited explanatory power. Conclusions: Cyberchondria-related behaviors were distributed across low, moderate, and high score categories among female healthcare students, with excessiveness emerging as the most prominent dimension. The findings demonstrate interrelationships among CSS-12 dimensions but no significant associations with demographic or academic variables. Overall, the results are descriptive and correlational in nature and indicate that the demographic and academic characteristics examined in this study did not meaningfully account for variation in cyberchondria-related behaviors. Psychological and behavioral factors not assessed in the present study warrant investigation in future research.