ArticleBMJ mental health2025
Childhood trauma and recent stressors in predicting subclinical psychotic symptoms among Chinese university students in southwest China: a machine learning analysis within a gender-specific framework.
Article in BMJ mental health, 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.
- Nonlinear Dose-Response Association Between Stressful Life Events and Probable Problematic Internet Use Among Chinese College Students: Cross-Sectional Study.Journal of medical Internet research · 2026Article
- Development and validation of the psychological stability scale for special operations personnel in China.Frontiers in psychology · 2026Article
- Parental expectation and psychological distress of Chinese youth: the chain mediating effects of core self-worth and perceived stress.BMC public health · 2025Article
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19 authors.
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Abstract
backgroundSubclinical psychotic symptoms (SPS) are common among college students and can lead to future mental health issues. However, it is still not clear which specific childhood trauma, stressors and health factors lead to SPSs, partly due to confounding factors and multicollinearity.
objectiveTo use machine learning to find the main predictors of SPS among university students, with special attention to gender differences.
methodsA total of 21 208 university students were surveyed regarding SPS and a wide range of stress-related factors, including academic pressure, interpersonal difficulties and abuse. Nine machine learning models were used to predict SPS. We examined the relationship between SPS and individual stressors using χ
findingsThe Extreme Gradient Boosting (XGBoost) model had the best prediction results, with an Area Under the Curve (AUC) of 0.89, and validated with external data. It also showed good calibration, and DCA indicated clear clinical benefit. Interpersonal difficulties, academic pressure and emotional abuse emerged as the strongest predictors of SPS. Gender-stratified analyses revealed that academic pressure and emotional abuse affected males more, while health issues like chest pain and menstrual pain were stronger predictors for females.
conclusionsMachine learning models effectively identified key stressors associated with SPS in university students. These findings highlight the importance of gender-sensitive approaches for the early detection and prevention of psychotic symptoms. CLINICAL IMPLICATIONS: SPSs in college students can be predicted by interpersonal difficulties, academic stress and childhood emotional abuse. This information can help mental health professionals develop better ways to prevent and address SPSs.
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