Understanding of HIV cure research in a rural community with high prevalence: the case of uMkhanyakude district, KwaZulu-Natal, South Africa
Bibliographic record
Abstract
BACKGROUND: Curing HIV has become a scientific priority with the development of HIV cure-related research collaborations and increasing clinical efforts. However, for potential study communities the meaning of HIV cure-related research needs to be fully understood. METHODS: We conducted qualitative research in rural KwaZulu-Natal, South Africa investigating the knowledge and understanding of HIV cure. We used deliberative approaches to facilitate in-depth discussions. Five deliberative group discussions were conducted in IsiZulu (the local language) with five participants in each group. Data were audio-recorded and translated verbatim and transcribed into English in anonymised format. Data were later analysed thematically with three main themes identified: knowledge of HIV cure, HIV cure terminology and HIV cure trials. RESULTS: Our findings showed that participants had a limited understanding of HIV cure-related research, a lack of trust regarding HIV cure science and participating in future cure trials. There were no local language terms used to describe HIV cure terminology, although several suggestions were shared in the discussions. CONCLUSIONS: Understanding the level of knowledge of rural populations regarding HIV cure-related research is essential for tailoring research and intervention strategies that meet their specific needs and circumstances. This can increase participation in the research and inform future HIV cure strategies.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".