Evidence map›Paper›PMID 42337686›Full record

ArticleBMC psychology2026

Gender differences in mental health profiles among Chinese adolescents: a multiple-group latent profile analysis.

Jichang Guo, Yanpei Pan, Yan Zhao, Xiang Zhang

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Article in BMC psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Jichang GuoSchool of Education Science, Minzu Normal University of Xingyi, Xingyi, China. jichang.guo@hotmail.com.ORCID https://orcid.org/0000-0001-7873-0351
Yanpei PanSchool of Literature and Journalism, Minzu Normal University of Xingyi, Xingyi, China.
Yan ZhaoSchool of Education Science, Minzu Normal University of Xingyi, Xingyi, China.
Xiang ZhangSchool of Education Science, Minzu Normal University of Xingyi, Xingyi, China. zhangxiangpsy@163.com.

Funding

Project of Plan of Guizhou Province Education Science, 2023 2023B048
6 · The paper itself

Abstract

backgroundAdolescent mental health crises are pervasive globally, with notable gender differences, yet existing research using the Dual-Factor Model (DFM) of mental health often relies on oversimplified cutoff scores, limiting insights into heterogeneous profiles. This study aimed to identify latent mental health profiles among Chinese adolescents and examine gender differences using latent profile analysis (LPA) and multiple-group LPA within the DFM framework.

methodsA total of 1,682 Chinese adolescents (Mage = 14.37, SD = 1.23; 55.29% males) completed the Center for Epidemiological Studies Depression Scale (CES-D) and Satisfaction With Life Scale (SWLS). LPA was employed to classify latent mental health profiles, and multiple-group LPA was used to compare profile structures, proportions, and symptom severity between genders through nested model comparisons.

resultsThis study confirmed three optimal mental health profiles in the total sample: (1) mental health profile (62.5% of participants), characterized by low psychological distress across somatic complaints, depressed affect, and positive affect, alongside high life satisfaction; (2) troubled arousal profile (30.5%), marked by moderate subclinical distress and mild impairment in life satisfaction; and (3) severe distress profile (7.0%), exhibiting severe psychological distress across all domains and profound life dissatisfaction. Gender-specific analyses revealed structural differences: males were classified into three profiles consistent with the total sample, while females were optimally categorized into two profiles. Females were disproportionately represented in distressed profiles and showed significantly higher symptom severity in the severe distress profile, particularly in somatic complaints, depressed affect, and life dissatisfaction.

conclusionThis study confirms three distinct mental health profiles among Chinese adolescents, with significant gender differences in profile structure, proportions, and symptom severity. Females are more vulnerable to mental health distress, highlighting the urgent need for gender-sensitive mental health interventions. These findings advance the understanding of adolescent mental health in China and provide a foundation for targeted screening, prevention, and intervention strategies. The DFM-based LPA approach provides a complementary person-centered approach to traditional assessments, enabling the identification of at-risk adolescents who may be overlooked by conventional methods.

Indexed as

Mental DisordersMental HealthPsychological DistressAdolescentChinaFemaleHumansLatent Class AnalysisMalePersonal SatisfactionSex FactorsAdolescentsDual-factor model of mental healthGender differencesLatent profile analysisMental health profiles

Identifiers

PMID42337686
PMCPMC13543427

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.