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

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.

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

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/highrisk 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.

Machine learning approaches to identify profiles and predictors of psychosocial discomfort among Italian college students / Enfoques de machine learning para identificar perfiles y predictores de malestar psicosocial entre estudiantes universitarios italianos

Abstract Introduction College students live a crucial period of transition from late adolescence to adulthood when they have to deal with important stressful tasks. Thus, university often represents a stressful environment, pushing students to cope with a high academic pressure. As a result, this period constitutes a sensitive age for the onset of mental disorders. Typically, students are not aware of the early signs of their own compromised mental health until symptoms aggravate to an overt disorder. Therefore, it is important to timely detect subthreshold symptoms mostly related to generic mental distress. Objective First, to assess psychophysical well-being and mental distress among college students in northern Italy, and to detect predictors, among socio-demographic and academic characteristics, and risky drug use of these two outcomes. Method The study involved 13,886 students who received an email explaining the purpose of the e-research. The questionnaires used were the General Health Questionnaire (GHQ-12), the University Stress Scale (USS), and a modified version of World Health Organization-ASSIST v3.0. Results 3,754 students completed the web-survey. Students showed poor well-being and mental distress. The strongest predictor of mental distress and compromised well-being was physical health, followed by sex, study field, risky drug use, and academic performance concerns. Discussion and conclusion This study shows that it is very important to promote in college students healthy behaviors in order to increase their physical exercise and reduce substance use. Moreover, it would be desirable to improve academic counselling facilities as an important front-line service to intercept mental health issues among young adults.

Resumen Introducción Los estudiantes universitarios pasan por un periodo crucial en su transición de la adolescencia tardía a la edad adulta, periodo en que tienen que lidiar con tareas estresantes. La universidad representa un entorno estresante, que empuja a los estudiantes a hacer frente a una alta presión académica. Como resultado, este periodo constituye una edad sensible para la aparición de trastornos mentales. En general, los estudiantes no cobran consciencia de los primeros signos de que su propia salud mental está en riesgo sino hasta que los síntomas se agravan y se convierten en un trastorno manifiesto. Por tanto, es importante detectar oportunamente los síntomas subumbrales relacionados ante todo con la angustia mental genérica. Objetivo Evaluar el bienestar psicofísico y la angustia mental entre estudiantes universitarios del norte de Italia, y en segundo lugar, detectar predictores entre las características sociodemográficas y académicas, y el uso de drogas de estos dos resultados. Método En el estudio participaron 13,886 estudiantes que recibieron un correo electrónico que explicaba el propósito de la investigación. Los instrumentos utilizados fueron el Cuestionario de Salud General (GHQ-12), la Escala de Estrés Universitario (USS) y una versión modificada de la Organización Mundial de la Salud-ASSIST v3.0. Resultados 3,754 estudiantes completaron la encuesta en línea. Los estudiantes mostraron bienestar y angustia mental. El predictor más fuerte de angustia mental y bienestar comprometido fue la salud física, seguido del sexo, el campo de estudio, el uso de drogas y el rendimiento académico. Discusión y conclusión Este estudio muestra que es muy importante promover entre los estudiantes universitarios comportamientos saludables para promover el ejercicio físico y reducir el consumo de sustancias. Además, sería deseable mejorar la orientación académica que es un importante servicio de primera línea para interceptar los problemas de salud mental en los estudiantes.