ArticleJournal of American college health : J of ACH
A machine learning analysis of risk and protective factors of suicidal thoughts and behaviors in college students.
Article in Journal of American college health : J of ACH. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.
- Predictive Performance of Machine Learning for Suicide in Adolescents: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Scalable prediction of suicidal risk in university students: a three steps machine learning approach in university settings.Scientific reports · 2026Article
- Modeling suicidal thoughts and behaviors in university students: integrating suicide theory with interpretable machine learning.BMC psychiatry · 2026Article
- Exploring the Comorbidity Mechanism of Internet Addiction, Insomnia, Depression, and Suicidality Among Chinese College Students Through Network Analysis.Alpha psychiatry · 2026Article
- Predicting suicidal ideation in academic communities using machine learning methods: a cross-sectional study.Lancet regional health. Americas · 2026Article
- Classifying Internet Addiction Using Machine Learning Approach: A Study Among Adolescents in Bangladesh.Public health challenges · 2025Article
- Predictive modeling of adolescent suicidal behavior using machine learning: Key features and algorithmic insights.MethodsX · 2025Review
- Using recursive partitioning to predict presence and severity of suicidal ideation amongst college students.Journal of American college health : J of ACH · 2025Article
- Exploring suicidal thoughts among prospective university students: a study with applications of machine learning and GIS techniques.BMC psychiatry · 2025Article
- Explainable machine learning models predicting the risk of social isolation in older adults: a prospective cohort study.BMC public health · 2025Article
- Automatically extracting social determinants of health for suicide: a narrative literature review.Npj mental health research · 2024Review
- Predicting inmate suicidal behavior with an interpretable ensemble machine learning approach in smart prisons.PeerJ. Computer science · 2024Article
- Machine Learning Techniques to Predict Mental Health Diagnoses: A Systematic Literature Review.Clinical practice and epidemiology in mental health : CP & EMH · 2024Review
- Influencing factors, prediction and prevention of depression in college students: A literature review.World journal of psychiatry · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 4 institutions in 1 country.
Funding
Abstract
objectiveTo identify robust and reproducible factors associated with suicidal thoughts and behaviors (STBs) in college students.
methods356 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 (
results9.6% 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.
conclusionAnalyses replicated known factors associated with STBs during the first semester of college and identified novel, potentially modifiable factors including positive affect and social connectedness.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.