Heterogeneous Mental Health Trajectories in College Students: A Three-Year Longitudinal Study Using Latent Class Growth Modeling

Background College 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. Methods This 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. Results A four-class solution demonstrated optimal fit (composite score = 0.919, entropy = 0.779, bootstrap stability = 0.940). Four trajectories emerged: Low-Optimal (13.2%; M _T1 = 1.04), Low-Stable (29.8%; M _T1 =1.19), Moderate-Improving (32.9%; M _T1 =1.47), and High-Risk-Improving (24.0%; M _T1 =1.98). Between-class differences were substantial (η² = 0.62). The high-risk class showed the steepest decline (slope = − 0.0100/month)but 31.8% remained above clinical threshold at T3.Growth parameters revealed substantial within-class heterogeneity in the high-risk group (σ² = 0.314). Conclusions Approximately 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 excellent stability. These findings support implementing tiered early warning systems based on baseline screening.