A model for explaining adherence to antiretroviral therapy in patients with HIV/AIDS: A grounded theory study.
Zahra HosseiniAbbas EbadiTeamur AghamolaeiSaharnaz NedjatPublished in: Health & social care in the community (2022)
Maintaining a high level of adherence to antiretroviral therapy is a challenge among HIV/AIDS patients. The study aimed to explore the process of adherence to treatment with a grounded theory approach to help physicians and planners develop strategies to increase adherence to treatment. We conducted in-depth interviews and a focus-group discussion. The data were collected from 2016 to 2018. The participants were 39 HIV/AIDS patients treated with antiretroviral, their relatives (three people) and two treatment staff. The study was conducted at the Behavioural Counselling Center of Imam Khomeini Hospital, located in Tehran, the capital of Iran. The data were analysed at the stages of "analysis for concepts," "analysis for context," "bringing process into the analysis" and "integrating." We obtained a conceptual model to explain the relationship between the categories. "Motivation" was identified as the core variable and the "Becoming resilient" explained the adherence process. Several factors including the interfering factors, contextual factors and resilience factors were identified. The interfering and contextual factors, in the absence of the resilience factors, lead to decreased motivation and increased poor adherence to treatment. The role of motivation in long-term adherence should be emphasised. We think strategies such as helping individuals with HIV/AIDS to form support networks, empowering and encouraging them to seek spiritual help will motivate them to maintain a long-term use of antiretroviral medications and, hence, become more resilient.
Keyphrases
- hiv aids
- antiretroviral therapy
- hiv infected
- human immunodeficiency virus
- hiv infected patients
- hiv positive
- chronic kidney disease
- end stage renal disease
- emergency department
- type diabetes
- primary care
- climate change
- metabolic syndrome
- machine learning
- glycemic control
- depressive symptoms
- adipose tissue
- hepatitis c virus
- patient reported outcomes
- south africa
- optical coherence tomography
- deep learning
- electronic health record