Classification of Depressive Symptoms Among Bereaved Families of Sewol Ferry Disaster Victims Using Latent Profile Analysis

• 발행기관: 대한신경정신의학회
• 저자명: Yu-Ri Lee Jinhee Hyun Sunju Sohn Jong-Sun Lee Hyu Jung Huh Kyonghwa Kang
• 간행물 정보: 『Psychiatry Investigation』제22권 제6호, 650~659쪽, 전체 10쪽
• 주제분류: 의약학 > 정신과학
• 파일형태: PDF
• 발행일자: 2025.06.17

국문 초록

Objective After the Sewol Ferry Disaster, most empirical studies on the bereaved families of victims conceptually categorized their depressive symptoms. However, the actual depressive features of bereaved families and the factors that influence such features remain unclear. Accordingly, this study aimed to categorize latent types of depression using latent profile analysis based on the sub-variables of the Patient Health Questionnaire-9 and identify the influencing factors for each type. Methods This study included 302 individuals aged ≥15 years who were members of the bereaved families of Sewol Ferry Disaster victims. Data were collected through an online questionnaire survey platform between October 5 and December 13, 2021. Results Latent profiles were divided into three groups: “overall low-level” (LOW), “lethargy and physical symptoms” (LPS), and “overall high-level” (HIGH). The participants with lower levels of social support and higher levels of family relationship stress were more likely to belong to the HIGH than LPS group. Moreover, the participants with higher levels of non-family relationship stress were more likely to belong to the LPS than the LOW group. Furthermore, the participants with poorer physical health and lower levels of social support were more likely to belong to the HIGH than LOW group. Conclusion Since bereaved families with poorer physical symptoms showed a higher risk for depressive symptoms, strategies to prevent their physical health problems are needed to ensure that their depressive symptoms do not become worse in the future.

목차

INTRODUCTION

METHODS

RESULTS

DISCUSSION

REFERENCES

키워드

Depressive disorder Stressdisorders Post-traumatic Bereavement Latent profile analysis