‘Why would you promote something that is less percent safer than a condom?’: Perspectives on partially effective HIV prevention technologies among key populations in South Africa
Bibliographic record
Abstract
New biomedical prevention technologies (NPTs) for HIV, including oral Pre-Exposure Prophylaxis, and vaginal and rectal microbicides and HIV vaccines in development, may contribute substantially to controlling the HIV epidemic. However, their effectiveness is contingent on product acceptability and adherence. We explored perceptions and understanding of partially effective NPTs with key populations in South African townships. From October 2013 to February 2014, we conducted six focus groups and 18 individual interviews with Xhosa-speaking adolescents (n = 14), adult men who have sex with men (MSM) (n = 15), and adult heterosexual men (n = 9) and women (n = 10), and eight key informant (KI) interviews with healthcare workers. Interviews/focus groups were transcribed and reviewed using a thematic approach and framework analysis. Overall, participants and KIs indicated scepticism about NPTs that were not 100% efficacious. Some participants equated not being 100% effective with not being completely safe, and thus not appropriate for dissemination. KIs expressed concerns that promoting partially effective NPTs would encourage substitution of a more effective with a less effective method or encourage risk compensation. Educational and social marketing interventions that address the benefits and appropriate use of partially effective NPTs, including education and support tailored for frontline service providers, are needed to prepare for successful NPT implementation in South Africa.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".