Frequency and machine learning predictors of severe depressive symptoms and suicidal ideation among university students.

ABSTRACT:

Aims

Prospective studies on the mental health of university students highlighted a major concern. Specifically, young adults in academia are affected by markedly worse mental health status than their peers or adults in other vocations. This situation predisposes to exacerbated disability-adjusted life-years.

Methods

We enroled 1,388 students at the baseline, 557 of whom completed follow-up after 6 months, incorporating their demographic information and self-report questionnaires on depressive, anxiety and obsessive-compulsive symptoms. We applied multiple regression modelling to determine associations - at baseline - between demographic factors and self-reported mental health measures and supervised machine learning algorithms to predict the risk of poorer mental health at f

SUBMITTER: Meda N 

PROVIDER: S-EPMC10387450 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature