ArticleFrontiers in psychiatry2026
Psychometric validation and predictive efficacy of a comprehensive depression risk model for undergraduates.
Article in Frontiers in psychiatry, 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
Background: Undergraduate depression is prevalent, yet traditional screening is unidimensional and inefficient. We developed a biopsychosocial risk classification model for the cross-sectional identification of current depressive symptoms. Methods: A cross-sectional study enrolled 898 undergraduates from a medical university in Western China (March-June 2024). The participants were randomized to training ( Results: The point prevalence of depressive symptoms (PHQ-9 ≥5) was 45.21% (406/898), which was significantly higher in women (OR = 2.98). Multivariate analysis identified severe somatic symptoms (OR = 37.94), moderate somatic symptoms, and social isolation as key independent risk factors. Excluding PHQ-9 items to avoid circularity, the random forest model achieved an AUC of 0.872 (95% CI: 0.841-0.903), outperforming scale-only (ΔAUC = 0.110, Conclusion: A comprehensive model combining physiological, psychological, and social factors yields excellent cross-sectional discriminative capability and stability for identifying undergraduates currently at risk for depressive symptoms. The proposed three-step clinical pathway (universal screening, targeted re-evaluation, and precision intervention) can facilitate large-scale, early identification in university settings. Due to the cross-sectional design, the term "prediction" is not used in a temporal or causal sense; rather, the model estimates the probability of concurrent depressive symptoms.
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