Deliberative dialogues in rural South Africa reveal public support and communication challenges in HIV cure research: uMkhanyakude district, KwaZulu-Natal
Bibliographic record
Abstract
BACKGROUND: There is a need to build public support for the conduct of HIV cure research. It is important to enhance the public understanding of HIV cure strategies in resource-constrained settings with high HIV prevalence. Adopting deliberative approaches allows information to be shared interactively, building knowledge on the topic and unpacking the complexity of the science. METHODS: We conducted deliberative community dialogues in rural KwaZulu-Natal, South Africa, with the Africa Health Research Institute community engagement team and Community Advisory Board members. We discussed the public understanding of 'HIV cure' and current scientific approaches. Detailed written notes were taken in anonymised format. Data were later analysed thematically, with three main themes identified: (i) understanding of HIV cure trials; (ii) HIV scientific procedures; and (iii) HIV cure messaging. RESULTS: Participants' questions and discussion revealed support for research on HIV cure but limited knowledge and understanding. Participants had questions about trial procedures, risks of antiretroviral therapy interruptions, efficacy, cost and monitoring of viral loads. There is need for simple, accessible language about the complex science to avoid misunderstanding among community members. CONCLUSION: On going challenges in effectively communicating complex scientific concepts highlight the importance of ongoing community engagement and education in HIV cure research.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.013 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".