A cross-sectional survey exploring HIV and HCV prevalence among men who purchase sex in Dnipro, Ukraine
Bibliographic record
Abstract
HIV programming in Ukraine largely targets "key population" groups. Men who purchase sex are not directly reached. The aim of our study was to explore the prevalence of sexually transmitted and blood-borne infections (STBBIs) among men who purchase sex from female sex workers.Following geographic mapping and population size estimation at each "hotspot", we conducted a cross-sectional bio-behavioural survey with men who purchase sex between September 2017 and March 2018 in Dnipro, Ukraine. Eligibility criteria included purchasing sex services at a "hotspot" and being ≥ 18 years. Participants completed a structured questionnaire, followed by HIV/HCV rapid testing and a dried blood spot (DBS) sample collection for confirmatory serology.The study enrolled 370 participants. The median age was 32 (interquartile range [IQR] = 27-38) and the median age of first purchase of sexual services was 22 (IQR = 19-27). Over half (56%) of participants reported ever testing for HIV; four participants (2%, N = 206) reported having tested positive for HIV, with three out of the four reporting being on ART. Forty percent of participants had ever tested for HCV, with three (2%, N = 142) having ever tested positive for HCV. In DBS testing, nine participants (2.4%) tested positive for HIV and 24 (6.5%) tested positive for ever having an HCV infection.Prevalence of HIV and HCV in this population was high. Given high rates of study enrolment and testing, efforts should be made to reach men who purchase sex with expanded STBBI programming.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".