“I Do Not Take My Medicine while Hiding” - A Longitudinal Qualitative Assessment of HIV Discordant Couples’ Beliefs in Discordance and ART as Prevention in Uganda
Bibliographic record
Abstract
BACKGROUND: HIV negative members of serostatus discordant couples are at high risk for HIV acquisition, but few interventions are in place to target them in sub-Saharan Africa. METHODS: In this study, we interviewed 28 couples, 3 times over a period of one year to understand their perceptions and attitudes around discordance, their relationship dynamics, their HIV risk behaviour, their beliefs and attitudes about antiretroviral therapy (ART) and their views of the community perceptions of discordance and treatment for HIV. RESULTS: Findings revealed that at baseline there were multiple complex explanations and interpretations about discordance among discordant couples and their surrounding community. Shifts in beliefs and attitudes about discordance, HIV risk reduction and ART over time were enabled through re-testing negative members of discordant couples and repeat counselling but some beliefs remain solidly embedded in cultural imperatives of the importance of childbearing as well as culturally determined and enforced gender roles. CONCLUSIONS: Interventions that aim to target discordant couples must embrace the complex and dynamic understandings of HIV diagnosis and treatment in context of fluid relationships, and changing beliefs about HIV risk and treatment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".