Barriers to the uptake of postexposure prophylaxis among Nairobi-based female sex workers
Bibliographic record
Abstract
INTRODUCTION: Female sex workers (FSWs) in sub-Saharan Africa are at a particularly high risk for HIV infection. Postexposure prophylaxis (PEP) is available as part of an HIV care and prevention program through dedicated FSW clinics in Nairobi, Kenya, but is underutilized. We evaluated PEP knowledge, access, and adherence among clinic attendees. METHODS: An anonymous questionnaire was administered to unselected HIV-uninfected FSWs. Participants were dichotomized into high and low HIV risk categories based on self-reported sexual practices. Prior PEP use, knowledge, and adherence were then evaluated. RESULTS: One hundred and thirty-four HIV-uninfected FSWs participated, with 64 (48%) categorized as being at high risk for HIV acquisition. High-risk FSWs were less likely to have heard of or accessed PEP than lower risk FSWs (37.5 vs. 58.6%, P = 0.014; and 21.9 vs. 40.6%, P = 0.019, respectively). Among higher risk FSWs, those who had accessed PEP were more likely to report treatment for a genital infection (71.4 vs. 42.0%, P = 0.049) or sex with an HIV-infected man (62.5 vs. 37.5%, P = 0.042) during the last 6 months. However, only 35.7% of high-risk women accessing PEP completed a full course of treatment, and noncompleters were more likely to report prior unprotected sex with an HIV-infected man (P = 0.023). CONCLUSION: Despite freely available PEP for Nairobi-based FSWs, women at highest risk were less likely to have heard of PEP, access PEP, or complete the full course of therapy once initiated. Program delivery needs to be improved to ensure that FSW most at risk are able to benefit from this resource.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".