Perceived benefits and costs of disclosing HIV diagnosis to family members among people living with HIV in Southern China: an application of a decision-making framework.
Yingxia ZhangXiaoming LiShan QiaoXueying YangYuejiao ZhouZhiyong ShenPublished in: AIDS care (2020)
ABSTRACT People living with HIV (PLWH) would decide whether to disclose their HIV serostatus to others based on the weight of perceived benefits and costs for the disclosure. Using cross-sectional data from 1254 PLWH in Guangxi, China, the study aimed to examine a framework of disclosure decision-making in the context of disclosure to family members (parents and siblings) through exploring the associations between disclosure and perceived benefits and costs of disclosure at individual and interpersonal levels. Univariate and multivariate regression analyses showed that HIV disclosure was associated with perceived benefits at both individual level (stress relief and social support) and interpersonal level (educating others and promoting family stability), but was not associated with perceived costs at either individual level (stigma and confidentiality breaching) or interpersonal level (family conflicts and concerns). Our findings suggest that perceived benefits rather than costs are associated with disclosure to family and play an important role in disclosure decision-making. These results may refine and expand the existing framework on decision-making of HIV disclosure focusing on PLWH's weight of individual benefits and costs. Future interventions highlighting the benefits for their family and other members of their social network may be an effective strategy to promote HIV disclosure to family members.
Keyphrases
- social support
- depressive symptoms
- antiretroviral therapy
- hiv positive
- physical activity
- decision making
- hiv infected
- hiv testing
- human immunodeficiency virus
- mental health
- hiv aids
- hepatitis c virus
- men who have sex with men
- cross sectional
- healthcare
- south africa
- weight loss
- machine learning
- autism spectrum disorder
- deep learning