ArticleBMC psychology2026
Heterogeneous mental health trajectories in college students: a three-year longitudinal study using latent class growth modeling.
Article in BMC psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Types and influencing factors of psychological health among freshmen in Chinese art vocational colleges: a latent profile analysis.Frontiers in psychology · 2026Article
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Abstract
backgroundCollege students face significant mental health challenges during their academic journey, yet the heterogeneity in their psychological adaptation patterns remains poorly understood. Traditional variable-centered approaches fail to capture the diverse trajectories that students may follow. Recent studies indicate that 20-30% of Chinese college students experience clinically significant psychological distress, highlighting the need for person-centered analytical approaches.
methodsThis longitudinal study employed formal Latent Class Growth Modeling (LCGM) using the Expectation-Maximization algorithm implemented in Python to identify distinct mental health trajectories among 2,562 Chinese college students assessed at enrollment (T1), sophomore year (T2, 18 months), and junior year (T3, 30 months). The Symptom Checklist-90 (SCL-90) Global Severity Index served as the primary outcome. Model selection was based on a composite score integrating BIC, entropy, bootstrap stability, and clinical relevance.
resultsA four-class solution demonstrated optimal fit (composite score = 0.768, entropy = 0.779, bootstrap stability = 0.933). Four trajectories emerged: Low-Optimal (13.2%; M
conclusionsApproximately one quarter of college students follow a high-risk mental health trajectory requiring targeted intervention. The formal LCGM approach with simultaneous parameter estimation provides robust classification with moderate classification quality (entropy = 0.779) and high bootstrap stability (0.933). These findings support implementing tiered early warning systems based on baseline screening.
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