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Using decision tree analysis to understand the influence of social networks on disclosure of HIV infection status

Authors
 Gwang Suk Kim  ;  Mi-So Shim  ;  Jeongmin Yi 
Citation
 AIDS CARE-PSYCHOLOGICAL AND SOCIO-MEDICAL ASPECTS OF AIDS/HIV, Vol.34(1) : 118-126, 2022-01 
Journal Title
AIDS CARE-PSYCHOLOGICAL AND SOCIO-MEDICAL ASPECTS OF AIDS/HIV
ISSN
 0954-0121 
Issue Date
2022-01
MeSH
Cross-Sectional Studies ; Decision Trees ; Disclosure* ; HIV Infections* ; Humans ; Social Networking ; Social Support ; Truth Disclosure
Keywords
HIV ; disclosure ; social network ; social support
Abstract
Disclosure of human immunodeficiency virus (HIV) infection status improves treatment adherence and HIV prevention. Social networks influence such disclosure by people living with HIV/AIDS (PLWH). This study aimed to investigate the disclosure status of Korean PLWH and determine the social network characteristics associated with disclosure. A cross-sectional study design was used, and 148 Korean PLWH answered self-report questionnaires that included items on the characteristics of social networks and disclosure. Logistic regression and decision tree analysis were performed. In total, 81 participants (54.7%) reported disclosing HIV status to the most important supporter. Five factors were found to influence disclosure: age, self-help group participation, living arrangement, social network relationship, and tie strength; three groups had higher percentages of nondisclosure. The findings suggest that healthcare practitioners should provide adequate counseling by considering the characteristics of social networks and disclosure status of PLWH. Researchers should identify high-risk populations using decision tree analysis.
Full Text
https://www.tandfonline.com/doi/full/10.1080/09540121.2021.1954587
DOI
10.1080/09540121.2021.1954587
Appears in Collections:
3. College of Nursing (간호대학) > Dept. of Nursing (간호학과) > 1. Journal Papers
Yonsei Authors
Kim, Gwang Suk(김광숙) ORCID logo https://orcid.org/0000-0001-9823-6107
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/187928
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