A Machine Learning Analysis of Risk and Protective Factors of Suicidal Thoughts and Behaviors in College Students

Autor/inn/en	Kirlic, Namik; Akeman, Elisabeth; DeVille, Danielle C.; Yeh, Hung-Wen; Cosgrove, Kelly T.; McDermott, Timothy J.; Touthang, James; Clausen, Ashley; Paulus, Martin P.; Aupperle, Robin L.
Titel	A Machine Learning Analysis of Risk and Protective Factors of Suicidal Thoughts and Behaviors in College Students
Quelle	In: Journal of American College Health, 71 (2023) 6, S.1863-1872 (10 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Kirlic, Namik)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0744-8481
DOI	10.1080/07448481.2021.1947841
Schlagwörter	Suicide; Cognitive Processes; Student Behavior; Prevention; Risk; College Freshmen; Depression (Psychology); Severity (of Disability); Social Isolation; World Views; Affective Behavior; Interpersonal Relationship + Suchen Sie Ihr Suchwort? Selbstmord; Cognitive process; Kognitiver Prozess; Student behaviour; Schülerverhalten; Prävention; Vorbeugung; Risiko; Studienanfänger; Schweregrad; Soziale Isolation; World view; Weltanschauung; Affective disturbance; Active behaviour; Affektive Störung; Interpersonal relation; Interpersonal relations; Interpersonelle Beziehung; Zwischenmenschliche Beziehung
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01