Family Interdependence, Spiritual Perspective, Self-Transcendence, and Depression Among Korean College Students

Machine learning models for predicting risk of depression in Korean college students: Identifying family and individual factors

Background

Depression is one of the most prevalent mental illnesses among college students worldwide. Using the family triad dataset, this study investigated machine learning (ML) models to predict the risk of depression in college students and identify important family and individual factors.

Methods

This study predicted college students at risk of depression and identified significant family and individual factors in 171 family data (171 fathers, mothers, and college students). The prediction accuracy of three ML models, sparse logistic regression (SLR), support vector machine (SVM), and random forest (RF), was compared.

Results

The three ML models showed excellent prediction capabilities. The RF model showed the best performance. It revealed five significant factors responsible for depression: self-perceived mental health of college students, neuroticism, fearful-avoidant attachment, family cohesion, and mother's depression. Additionally, the logistic regression model identified five factors responsible for depression: the severity of cancer in the father, the severity of respiratory diseases in the mother, the self-perceived mental health of college students, conscientiousness, and neuroticism.

Discussion

These findings demonstrated the ability of ML models to accurately predict the risk of depression and identify family and individual factors related to depression among Korean college students. With recent developments and ML applications, our study can improve intelligent mental healthcare systems to detect early depressive symptoms and increase access to mental health services.

Couple- or family-oriented interventions and outcomes for preventing mental health problems and promoting mental health: a scoping review protocol

OBJECTIVE

The objective of this scoping review is to map the literature describing couple- and family-oriented interventions and outcomes to prevent mental health problems and promote mental health.

INTRODUCTION

Literature involving couple- and family-focused mental health interventions has gained increased attention over the past two decades, yet little is known about the efficacy of these interventions for promoting mental health and preventing mental disorders. Despite recognition that the most sustainable method for reducing the increasing burden of mental disorders is primary prevention, there has been no systematic investigation of relevant couple- and family-focused interventions.

INCLUSION CRITERIA

This scoping review will consider studies that include couples or multiple family members who have participated in couple- and family-focused interventions aimed at primary prevention of mental disorders and promotion of mental health. This study will include experimental, quasi-experimental, and observational study designs, as well as qualitative studies. Conference abstracts, posters, editorials, commentaries, and opinion papers will be excluded.

METHODS

Multiple databases will be searched, including PubMed (MEDLINE), CINAHL, ERIC, PsycINFO (via EBSCO), Web of Science, Scopus, and Research Information Sharing Service (RISS). Trial registers and gray literature will also be searched. Two independent reviewers will screen the abstract/title of retrieved citations using inclusion and exclusion criteria. After screening titles and abstracts of identified citations, relevant studies will be retrieved in full. Using a data extraction instrument developed specifically for this review, eligible studies will be extracted and presented in diagrammatic or tabular form, accompanied by a narrative summary.