Potential suicide risk among the college student population

Machine Learning Approaches for Identifying Predictors and Different Students' Risk Profiles

Bibliographic Data

ID4406767
AuthorsJessica Dagani (0000-0003-2890-6608, University of Brescia, corresponding author), Chiara Buizza (0000-0002-3339-3539, University of Brescia), Chiara Ferrari (0000-0002-4101-6872, Fondazione Poliambulanza Istituto Ospedaliero), Alberto Ghilardi (0000-0002-7053-7853, University of Brescia)
Year2024
Volume37
Issue1
Pages19-19
Publication date2024-05-17
Open AccessYes
TypeARTICLE
VenuePsicologia Reflexão e Crítica (JOURNAL)
Journal identifiersISSN: 0102-7972 • E-ISSN: 1678-7153
PublisherSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1186/s41155-024-00301-6
PMID38758421
OpenAlexW4397010873
LanguageEN
Citations received2
References cited54

Background Suicide is one of the leading causes of death among young people and university students. Research has identified numerous socio-demographic, relational, and clinical factors as potential predictors of suicide risk, and machine learning techniques have emerged as promising ways to improve risk assessment. Objective This cross-sectional observational study aimed at identifying predictors and college student profiles associated with suicide risk through a machine learning approach. Methods A total of 3102 students were surveyed regarding potential suicide risk, socio-demographic characteristics, academic career, and physical/mental health and well-being. The classification tree technique and the multiple correspondence analysis were applied to define students' profiles in terms of suicide risk and to detect the main predictors of such a risk. Results Among the participating students, 7% showed high potential suicide risk and 3.8% had a history of suicide attempts. Psychological distress and use of alcohol/substance were prominent predictors of suicide risk contributing to define the profile of high risk of suicide: students with significant psychological distress, and with medium/high-risk use of alcohol and psychoactive substances. Conversely, low psychological distress and low-risk use of alcohol and substances, together with religious practice, represented the profile of students with low risk of suicide. Conclusions Machine learning techniques could hold promise for assessing suicide risk in college students, potentially leading to the development of more effective prevention programs. These programs should address both risk and protective factors and be tailored to students' needs and to the different categories of risk

Environmental health · Observational study · Poison control · Population · Psychiatry · Suicide prevention · Suicide Risk · Clinical Psychology · COVID-19 and Mental Health · Human Factors and Ergonomics · Medicine · Mental Health via Writing · Psychology · Suicide and Self-Harm Studies

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