Document Type

Article

Publication Date

7-2023

Identifier

DOI: 10.1080/07448481.2021.1947841; PMCID: PMC8782938

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 most important factors. There was a significant reduction in STBs at end-of-year with only 1.8% of students identified at significant risk.

CONCLUSION: Analyses replicated known factors associated with STBs during the first semester of college and identified novel, potentially modifiable factors including positive affect and social connectedness.

Journal Title

Journal of American college health : J of ACH

Volume

71

Issue

6

First Page

1863

Last Page

1872

Comments

Grants and funding

This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.

Publisher's Link: https://www.tandfonline.com/doi/full/10.1080/07448481.2021.1947841

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