HIV testing behaviour and HIV prevalence among female sex workers in Ukraine: findings from an Integrated Bio-Behavioural Survey, 2013–2014
Bibliographic record
Abstract
OBJECTIVES: Ukraine has one of the largest HIV epidemics in Europe, with high prevalence among female sex workers (FSWs). We aimed to identify factors associated with HIV testing and receipt of the test result in the last 12 months, HIV prevalence and self-reported positive status among FSWs in Ukraine. METHODS: We used data from an Integrated Bio-Behavioural Survey among FSWs conducted in 2013-2014. The survey methodology combined three sampling strategies: time and location sampling, respondent-driven sampling and key informant recruitment. We used multivariable regression to identify factors associated with self-reported HIV testing in the last 12 months, HIV prevalence and self-reported positive status among FSWs living with HIV. Explored factors included: age, age at first sex, age at entry into sex work, education, marital status, employment status beside sex work, condom use with last paying or non-paying sexual partner, drug or alcohol consumption and sex work venue. RESULTS: Recent HIV testing was low overall with only 63.2% of FSWs reported having tested and received their test result in the last 12 months prior to the survey. HIV prevalence was 7.1% overall, but only 45.0% of FSWs living with HIV were aware of their HIV status. Testing in the last 12 months with receipt of test result was less common among FSWs who used drugs ever in life (adjusted OR (AOR) 0.7, 95% CI 0.6 to 0.9), women soliciting clients indoors (AOR 0.8, 95% CI 0.7 to 0.9) and those not using a condom with last paying sexual partner (AOR 0.3, 95% CI 0.2 to 0.5). HIV positivity was associated with history of ever using drugs (AOR 2.3, 95% CI 1.4 to 3.6) and soliciting clients outdoors (AOR 1.5, 95% CI 1.1 to 2.0). Women working indoors were less aware of their positive status (AOR 0.1, 95% CI 0.1 to 0.9). CONCLUSION: HIV prevalence is high among FSWs in Ukraine, and testing and knowledge of one's status remain insufficient. HIV testing programmes need to expand with strategies to reach specific subgroups of FSWs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".