Journal of affective disorders · 2025
Latent class analysis on mental health and associated factors in medical and non-medical college students.
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Wen Li-Ying, Zhang Liu, Zhu Li-Jun, Song Jian-Gen, Wang An-Shi, Feng Ying, Tao Yu-Jing, Zhu Yu, Jin Yue-Long, Chang Wei-Wei
Аннотация
BACKGROUND: This study aimed to identify latent classes of mental health status among Chinese college students and to explore the influencing factors that differ between medical and non-medical students.
METHODS: A cross-sectional survey was conducted among 4768 students from four institutions located in Anhui Province, China, utilizing stratified cluster sampling. The survey assessed depressive symptoms, anxiety symptoms, sleep chronotypes, sleep disorders, and suicidal behaviors. Latent class analysis was employed to identify mental health subgroups, and multinomial logistic regression was utilized to analyze the influencing factors.
RESULTS: Three latent classes were identified: C1 (Low Depression/Anxiety - Low Suicidal Behavior, 88.1 %), C2 (High Depression/Anxiety - Low Suicidal Behavior, 8.6 %), and C3 (Moderate Depression/Anxiety - High Suicidal Behavior, 3.3 %). Alcohol consumption, sleep disorders, academic burden, gender, grade, and daily online time significantly predicted these classes. Students with alcohol consumption and sleep disorders were more likely in C2 and C3. Medical students with heavy academic burdens were more likely in C2, while those with light burdens were more likely in C3. Male medical students were more likely in C2 and C3. Non-medical students with heavy and light academic burdens, as well as those in higher grades, were more likely in C2. Non-medical students with 1.5-3 h of daily online time were more likely in C2, and those with <1.5 h were more likely in C3.
CONCLUSIONS: College students' mental health demonstrates significant heterogeneity, with factors such as alcohol consumption, sleep disorders, academic burden, gender, grade, and daily online time serving as key predictors. These findings highlight the pressing need for targeted interventions aimed at addressing specific risk factors, thereby enhancing mental health support services.
METHODS: A cross-sectional survey was conducted among 4768 students from four institutions located in Anhui Province, China, utilizing stratified cluster sampling. The survey assessed depressive symptoms, anxiety symptoms, sleep chronotypes, sleep disorders, and suicidal behaviors. Latent class analysis was employed to identify mental health subgroups, and multinomial logistic regression was utilized to analyze the influencing factors.
RESULTS: Three latent classes were identified: C1 (Low Depression/Anxiety - Low Suicidal Behavior, 88.1 %), C2 (High Depression/Anxiety - Low Suicidal Behavior, 8.6 %), and C3 (Moderate Depression/Anxiety - High Suicidal Behavior, 3.3 %). Alcohol consumption, sleep disorders, academic burden, gender, grade, and daily online time significantly predicted these classes. Students with alcohol consumption and sleep disorders were more likely in C2 and C3. Medical students with heavy academic burdens were more likely in C2, while those with light burdens were more likely in C3. Male medical students were more likely in C2 and C3. Non-medical students with heavy and light academic burdens, as well as those in higher grades, were more likely in C2. Non-medical students with 1.5-3 h of daily online time were more likely in C2, and those with <1.5 h were more likely in C3.
CONCLUSIONS: College students' mental health demonstrates significant heterogeneity, with factors such as alcohol consumption, sleep disorders, academic burden, gender, grade, and daily online time serving as key predictors. These findings highlight the pressing need for targeted interventions aimed at addressing specific risk factors, thereby enhancing mental health support services.