Heterogeneity within high-risk trajectories: a formal latent class growth modeling approach to college student mental...

Latent Class Growth Modeling (LCGM) identifies distinct developmental trajectories but often treats classes as homogeneous. This study examines within-class heterogeneity in Chinese college students' mental health trajectories, focusing specifically on high-risk groups.

We analyzed 2,562 Chinese college students (74.6% female; M_age = 18.5 years) with complete data across three waves over 30 months (Fall 2022, +18 months, +30 months). Mental health was assessed using the SCL-90 Global Severity Index. We employed formal LCGM with expectation-maximization (EM) algorithms for joint parameter and class membership estimation, validated with 1,000 bootstrap resamples.

A 4-class solution provided optimal fit (BIC = 1353.4, entropy = 0.78, bootstrap stability = 0.92). The Moderate-High-Improving class (24.0%, n = 616) exhibited residual variance 5 to 314 times larger than the other three classes (σ² = 0.314 vs. 0.001-0.061), with three distinct subgroups: substantial improvers (54%), minimal changers (28%), and non-responders/worsening (18%). Baseline screening achieved excellent predictive validity (AUC = 0.97; optimal cutoff GSI ≥ 1.60: sensitivity = 92%, specificity = 89%). A shrinkage-based (empirical Bayes) sensitivity analysis indicated that this three-subgroup pattern is better characterized as a continuous, probabilistic risk gradient than as three discrete subtypes (see Discussion).

High-risk mental health trajectories exhibit substantial within-class heterogeneity often obscured by mean-based interpretations. Findings support continuous monitoring and personalized intervention matching rather than uniform treatment protocols for high-risk students. Routine examination of within-class variance should become standard practice in trajectory research.