Awareness and Utilization of HIV Testing and Prevention Services Among Female Sex Workers in Dnipro, Ukraine: Implications for Prevention Program Strengthening From the Dynamics Study
Bibliographic record
Abstract
Introduction: Approximately 240,000 people live with HIV in Ukraine, concentrated among key populations, including sex workers. Non-governmental organizations (NGOs) play an important role in the funding and delivery of HIV testing and prevention services in Ukraine. These services are set within the context of national healthcare reforms as well as ongoing armed conflict. This study seeks to describe and understand the usage of HIV testing and prevention services among sex workers in the eastern Ukrainian city of Dnipro. Methods: A cross-sectional bio-behavioral survey was administered in September 2017-March 2018 among 560 sex workers working in Dnipro. Descriptive analyses of survey data are presented alongside multivariable logistic regression models identifying factors associated with NGO awareness and HIV testing in the past 12 months; adjusted odds ratios (AOR) and 95% confidence intervals (95% CI) are reported. Results: Sixty-two percent of respondents were aware of NGOs offering HIV services. Sixty-eight percent had tested for HIV in the past 12 months, and 51% of those who reported the location of their most recent test were tested at an NGO. Those with 5-9 years in sex work had greater odds of being aware of NGOs (AOR = 5.5, 95%CI: 3.2-9.7) and testing for HIV (AOR = 3.4, 95%CI: 2.0-6.0) compared to those new to the profession. Contact with outreach workers was strongly associated with increased odds of testing (AOR = 13.0, 95%CI: 7.0-24.0). Sex workers in "offices" (brothel-like venues) reported higher odds of testing than all other workplaces, while those in entertainment venues (AOR = 0.3, 95%CI: 0.2-0.5) and public places (AOR = 0.2, 95%CI: 0.1-0.3) reported lower rates. Receiving prevention services, such as free condoms, was associated with increased testing (AOR = 16.9, 95%CI: 9.7-29.3). Discussion: NGOs in Dnipro, Ukraine play an important role in HIV testing and prevention for women involved in sex work. However, focused efforts should be placed on supporting access to these services for women that are newer to sex work, and those working in entertainment venues or public places. Outreach workers appear to support access to HIV prevention information and supplies and facilitate linkages to HIV testing for sex workers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".