Assessment of antiretroviral therapy knowledge and willingness of persons with HIV to support its uptake in Uganda
Bibliographic record
Abstract
BACKGROUND: Access to care and treatment services for human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) are hampered by human resource constraints and knowledge gaps about antiretroviral therapy. Training people with HIV/AIDS (PWA) as educators on antiretroviral therapy may help in the expansion of antiretroviral therapy-related knowledge in Africa. The aim of this study was to assess the antiretroviral therapy-specific knowledge, beliefs, and attitudes of PWA as well as their proactive communication with community members and to explore their willingness to serve as support personnel. METHODS: Data were obtained from a large randomized trial of PWA. We analyzed qualitative and quantitative data from 524 PWA aged 18 years and over who initiated home-based antiretroviral therapy in 2003. We assessed knowledge and communication of HIV prevention and treatment messages by PWA to communities using structured messages complemented with other knowledge questions and the willingness of PWA to serve as support persons. Descriptive bivariate associations and logistic regression statistical methods were performed. In addition, qualitative data analysis was used. RESULTS: The level of knowledge about antiretroviral therapy was high among all PWA on several technical attributes. Overall, 90% of PWA reported that they had been consulted by community members for informed opinions on antiretroviral therapy, 70% felt they were opinion leaders on aspects of antiretroviral therapy within the communities, and approximately 70% were willing to be engaged as community support persons. Those who were classified as opinion leaders reported being approached more regularly by community members for expert advice about antiretroviral therapy compared with nonopinion leaders (odds ratio [OR] 11.7; 95% confidence interval [CI] 7.3-18.6), and opinion leaders were significantly more informed on most technical attributes of antiretroviral therapy, such as "who qualifies for antiretroviral therapy based on CD4 count" (OR 1.6, 95% CI 1.1-2.0) and "the need to be evaluated for antiretroviral therapy" (OR 1.8, 95% CI 1.2-2.0). CONCLUSION: Opinion leaders demonstrated correct knowledge and willingness to provide information on antiretroviral therapy care and treatment issues and were, in turn, consulted more frequently for antiretroviral therapy advice compared with nonopinion leaders. Training opinion leaders to work as community support personnel may increase knowledge about antiretroviral therapy in underserved communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".