Heterogeneous mental health trajectories in college students: a three-year longitudinal study using latent class...

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.

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.

A 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_T1 = 1.04), Low-Stable (29.8%; M_T1 = 1.19), Moderate-Improving (32.9%; M_T1 = 1.47), and High-Risk-Improving (24.1%; M_T1 = 1.98). The four classes were primarily differentiated by baseline severity (η²baseline = 0.550), with more modest between-class differences in rate of change (η²change = 0.036). The high-risk class showed the steepest decline (slope = - 0.0100/month) but 31.8% remained above clinical threshold at T3. The high-risk class exhibited the largest observed within-class variability at enrollment (T1 SD = 0.562), indicating heterogeneous baseline symptom levels among students in this group.

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 moderate classification quality (entropy = 0.779) and high bootstrap stability (0.933). These findings support implementing tiered early warning systems based on baseline screening.