HIV prevention programme with young women who sell sex in Mombasa, Kenya: learnings for scale‐up
Bibliographic record
Abstract
INTRODUCTION: In 2018, the National AIDS and sexually transmitted infection (STI) Control Programme developed a national guidelines to facilitate the inclusion of young women who sell sex (YWSS) in the HIV prevention response in Kenya. Following that, a 1-year pilot intervention, where a package of structural, behavioural and biomedical services was provided to 1376 cisgender YWSS to address their HIV-related risk and vulnerability, was implemented. METHODS: Through a mixed-methods, pre/post study design, we assessed the effectiveness of the pilot, and elucidated implementation lessons learnt. The three data sources used included: (1) monthly routine programme monitoring data collected between October 2019 and September 2020 to assess the reach and coverage; (2) two polling booth surveys, conducted before and after implementation, to determine the effectiveness; and (3) focus group discussions and key informant interviews conducted before and after intervention to assess the feasibility of the intervention. Descriptive analysis was performed to produce proportions and comparative statistics. RESULTS: During the intervention, 1376 YWSS were registered in the programme, 28% were below 19 years of age and 88% of the registered YWSS were active in the last month of intervention. In the survey, respondents reported increases in HIV-related knowledge (61.7% vs. 90%, p <0.001), ever usage of pre-exposure prophylaxis (8.5% vs. 32.2%, p < 0.001); current usage of pre-exposure prophylaxis (5.3% vs. 21.1%, p<0.002); ever testing for HIV (87.2% vs. 95.6%, p <0.04) and any clinic visit (35.1 vs. 61.1, p <0.001). However, increase in harassment by family (11.7% vs. 23.3%, p<0.04) and discrimination at educational institutions (5.3% vs. 14.4%, p<0.04) was also reported. In qualitative assessment, respondents reported early signs of success, and identified missed opportunities and made recommendations for scale-up. CONCLUSIONS: Our intervention successfully rolled out HIV prevention services for YWSS in Mombasa, Kenya, and demonstrated that programming for YWSS is feasible and can effectively be done through YWSS peer-led combination prevention approaches. However, while reported uptake of treatment and prevention services increased, there was also an increase in reported harassment and discrimination requiring further attention. Lessons learnt from the pilot intervention can inform replication and scale-up of such interventions in Kenya.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".