A machine learning analysis of risk and protective factors of suicidal thoughts and behaviors in college students.

ABSTRACT:

Objective

To identify robust and reproducible factors associated with suicidal thoughts and behaviors (STBs) in college students.

Methods

356 first-year university students completed a large battery of demographic and clinically-relevant self-report measures during the first semester of college and end-of-year (n = 228). Suicide Behaviors Questionnaire-Revised (SBQ-R) assessed STBs. A machine learning (ML) pipeline using stacking and nested cross-validation examined correlates of SBQ-R scores.

Results

9.6% of students were identified at significant STBs risk by the SBQ-R. The ML algorithm explained 28.3% of variance (95%CI: 28-28.5%) in baseline SBQ-R scores, with depression severity, social isolation, meaning and purpose in life, and positive affect among the

SUBMITTER: Kirlic N 

PROVIDER: S-EPMC8782938 | biostudies-literature | 2023 Aug-Sep

REPOSITORIES: biostudies-literature