Quantifying Delay in First Contact With HIV Programs Among Young Women Engaged in Sex Work in Mombasa, Kenya: A Time-To-Event Analysis
Bibliographic record
Abstract
BACKGROUND: Young women engaged in sex work (YSW) experience a disproportionately high burden of HIV, yet most HIV programs for sex workers are not designed to reach YSW. METHODS: Longitudinal data from the start of sex work are infeasible, but cross-sectional surveys may help identify "contact gaps." We used data from the 2015 Transitions Study, a cross-sectional survey of sexually active women aged 14-24 who self-identified as a sex worker in Mombasa, Kenya. We created a virtual cohort using self-reported event timing described relative to the survey date. We quantified the time from self-identification as sex worker to the initial program contact ("contact gap") and employed time-to-event analyses to estimate and characterize factors associated with the rate of program contact. RESULTS: Of 392 YSW included in the time-to-event analyses, only 47 (12%) reported program contact, with a median time of 12 months (interquartile range: 2‒24). The rate of program contact per 100 person-months was 0.52 [95% confidence interval (CI): 0.38 to 0.68], and when applied to the estimated population size of YSW in Mombasa, the minimum contact gap was 11,532 person-years. A shorter contact gap was associated with older age when first negotiated money for sex [adjusted hazard ratio: 1.2 (95% CI: 1.0 to 1.5)] and perceiving earning money through sex work as easy [11.5 (2.8 to 47.7)]. CONCLUSIONS: A large contact gap highlights the need to reshape HIV prevention services for YSW across their life course. Despite limitations, cross-sectional data could help estimate the contact gap and support program monitoring and evaluation.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".