{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T05:55:33Z","timestamp":1791266133918,"version":"4.1.0"},"reference-count":51,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T00:00:00Z","timestamp":1791244800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Psychol."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>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\u2019 mental health trajectories, focusing specifically on high-risk groups.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>\n                      We analyzed 2,562 Chinese college students (74.6% female; M\n                      <jats:sub>age<\/jats:sub>\n                      \u202f=\u202f18.5\u202fyears) with complete data across three waves over 30\u202fmonths (Fall 2022, +18\u202fmonths, +30\u202fmonths). Mental health was assessed using the SCL-90 Global Severity Index. We employed formal LCGM with expectation\u2013maximization (EM) algorithms for joint parameter and class membership estimation, validated with 1,000 bootstrap resamples.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Result<\/jats:title>\n                    <jats:p>\n                      A 4-class solution provided optimal fit (BIC\u202f=\u202f1353.4, entropy\u202f=\u202f0.78, bootstrap stability\u202f=\u202f0.92). The Moderate-High-Improving class (24.0%,\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f616) exhibited residual variance 5 to 314 times larger than the other three classes (\u03c3\n                      <jats:sup>2<\/jats:sup>\n                      \u202f=\u202f0.314 vs. 0.001\u20130.061), with three distinct subgroups: substantial improvers (54%), minimal changers (28%), and non-responders\/worsening (18%). Baseline screening achieved excellent predictive validity (AUC\u202f=\u202f0.97; optimal cutoff GSI\u202f\u2265\u202f1.60: sensitivity\u202f=\u202f92%, specificity\u202f=\u202f89%). 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).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpsyg.2026.1862494","type":"journal-article","created":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T05:21:53Z","timestamp":1791264113000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Heterogeneity within high-risk trajectories: a formal latent class growth modeling approach to college student mental health"],"prefix":"10.3389","volume":"17","author":[{"given":"Yajuan","family":"Li","sequence":"first","affiliation":[{"name":"School of Education Science, Hanshan Normal University","place":["Chaozhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Hanshan Normal University","place":["Chaozhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huanbin","family":"Xue","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Hanshan Normal University","place":["Chaozhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,10,6]]},"reference":[{"key":"ref9001","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1007\/s13142-014-0265-0","article-title":"Introduction to SMART designs for the development of adaptive interventions: with application to weight loss research","volume":"4","author":"Almirall","year":"2014","journal-title":"Transl. 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