Ranking factors across multiple domains in predicting adolescent mental health: a Bayesian machine learning approach.

ABSTRACT:

Background

The prevalence of mental health problems among adolescents is on the rise globally, and is a pressing public health concern in many developing countries, including China. While a growing body of epidemiological research has identified potential factors affecting adolescent mental health, few have considered both risk and protective factors across multiple domains or utilized machine learning approaches to identify and rank these factors.

Methods

This is a cross-sectional study based on data from 3,526 adolescent participants aged 11-15 years in the Qu County Study in China, and aims to identify and rank factors across five domains-including sociodemographic factors, academic functioning, extracurricular activities, life experiences, and resilience factors-in predi

SUBMITTER: Zhao S 

PROVIDER: S-EPMC12519815 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature