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

OBJECTIVE

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.

METHOD: We analyzed 2,562 Chinese college students (74.6% female; Mage = 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.

RESULT: 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).

CONCLUSION

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.