Potential suicide risk among the college student population: machine learning approaches for identifying predictors and different students' risk profiles.

IF 1.5 4区 心理学 Q3 PSYCHOLOGY, MULTIDISCIPLINARY Psicologia-Reflexao E Critica Pub Date : 2024-05-17 DOI:10.1186/s41155-024-00301-6
Jessica Dagani, Chiara Buizza, Clarissa Ferrari, Alberto Ghilardi
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Abstract

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.

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大学生群体的潜在自杀风险:识别预测因素和不同学生风险特征的机器学习方法。
背景:自杀是年轻人和大学生的主要死因之一。研究发现,许多社会人口学、人际关系和临床因素都是自杀风险的潜在预测因素,而机器学习技术已成为改善风险评估的有效方法:这项横断面观察性研究旨在通过机器学习方法确定与自杀风险相关的预测因素和大学生特征:共对 3102 名学生进行了有关潜在自杀风险、社会人口特征、学业生涯、身体/心理健康和幸福感的调查。采用分类树技术和多重对应分析来确定学生的自杀风险特征,并检测自杀风险的主要预测因素:结果:在参与调查的学生中,7%的学生有较高的潜在自杀风险,3.8%的学生有自杀未遂史。心理困扰和酗酒/服用精神药物是自杀风险的主要预测因素,有助于确定高自杀风险的特征:学生有严重的心理困扰,酗酒和服用精神药物的风险为中度/高度。相反,低心理压力、低风险使用酒精和精神活性物质以及宗教信仰则代表了低自杀风险学生的特征:结论:机器学习技术在评估大学生自杀风险方面大有可为,有可能开发出更有效的预防计划。这些计划应同时针对风险因素和保护因素,并根据学生的需求和不同的风险类别量身定制。
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来源期刊
Psicologia-Reflexao E Critica
Psicologia-Reflexao E Critica PSYCHOLOGY, MULTIDISCIPLINARY-
CiteScore
2.60
自引率
0.00%
发文量
35
审稿时长
24 weeks
期刊介绍: Psicologia: Reflexão & Crítica is a journal published three times a year by Programa de Pós-Graduação em Psicologia do Desenvolvimento (Psychology Graduate Program) of the Universidade Federal do Rio Grande do Sul - UFRGS (Federal University of Rio Grande do Sul). Its objective is to publish original works in the psychology field: articles, short reports on research and reviews as well as to present to the scientific community texts which reflect a significant contribution for the psychology field. The short title of the journal is Psicol. Refl. Crít. It must be used regarding bibliographies, footnotes, as well as bibliographical strips and references.
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