{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T12:29:57Z","timestamp":1773059397649,"version":"3.50.1"},"reference-count":53,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T00:00:00Z","timestamp":1759190400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T00:00:00Z","timestamp":1759190400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["asmepublications.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Education"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The high\u2010pressure environment of medical education presents significant challenges to the long\u2010term psychological well\u2010being of medical students. Although anxiety and depression are well\u2010documented among medical students, few studies have explored the developmental trajectories of these symptoms over time. This study aims to explore the two\u2010year developmental trajectories of anxiety and depression symptoms in medical students and identify key predictors of these trajectories.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This longitudinal study involved 810 medical students from a Chinese medical school, with data collected over four waves spanning two years. A total of 730 students completed the baseline survey and were included in the analysis, yielding a valid response rate of 90.1%. Participants completed the Patient Health Questionnaire\u20109 (PHQ\u20109) for depression and the Generalized Anxiety Disorder\u20107 (GAD\u20107) scale for anxiety. Latent Growth Mixture Modelling (LGMM) was used to identify the latent trajectories of depression and anxiety symptoms, with full information maximum likelihood estimation applied to handle missing follow\u2010up data. Regression analysis was conducted to determine predictors of these trajectories.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The four waves of data for both depression and anxiety symptoms fit the model well. Depression followed two trajectories: a slowly decreasing group (92.0%) and a significantly increasing group (8.0%). Anxiety exhibited three trajectories: a low level\u2010slow decreasing group (72.7%), a high level\u2010significantly decreasing group (21.2%) and a low level\u2010significantly increasing group (6.1%). Significant predictors of these trajectories included family structure, quality of relationships with parents and roommates, social support, past suicidal ideation and self\u2010harming behaviour. Higher levels of social support were associated with decreasing symptom trajectories, whereas poor family relationships and past suicidal ideation predicted increasing symptoms.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Depression and anxiety symptoms in medical students follow distinct developmental trajectories, providing a basis for targeted psychological interventions. Strengthening social support should be a priority for educational institutions and policymakers.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/medu.70047","type":"journal-article","created":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T13:25:57Z","timestamp":1759238757000},"page":"418-430","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Mental health longitudinal trajectories and predictors in medical students: Latent growth mixture model analysis"],"prefix":"10.1111","volume":"60","author":[{"given":"Yinhai","family":"Chen","sequence":"first","affiliation":[{"name":"Key Laboratory of Digital\u2010Intelligent Disease Surveillance and Health Governance North Sichuan Medical College  Nanchong China"},{"name":"School of Nursing North Sichuan Medical College  Nanchong 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