Violence, Condom Breakage, and HIV Infection Among Female Sex Workers in Benin, West Africa
Bibliographic record
Abstract
OBJECTIVE: To examine the relationship between violence, condom breakage, and HIV prevalence among female sex workers (FSWs). METHODS: Data were obtained from the 2012 cross-sectional integrated biological and behavioral survey conducted in Benin. Multivariable log-binomial regression was used to estimate the adjusted prevalence ratios of HIV infection and condom breakage in relation to violence toward FSWs. A score was created to examine the relationship between the number of violence types reported and HIV infection. RESULTS: Among the 981 women who provided a blood sample, HIV prevalence was 20.4%. During the last month, 17.2%, 13.5%, and 33.5% of them had experienced physical, sexual, and psychological violence, respectively. In addition, 15.9% reported at least 1 condom breakage during the previous week. There was a significant association between all types of violence and HIV prevalence. The adjusted prevalence ratios of HIV were 1.45 (95% confidence interval [95% CI], 1.05-2.00), 1.42 (95% CI, 1.02-1.98), and 1.41 (95% CI, 1.08-1.41) among those who had ever experienced physical, sexual, and psychological violence, respectively. HIV prevalence increased with the violence score (P = 0.002, test for trend), and physical and sexual violence were independently associated with condom breakage (P = 0.010 and P = 0.003, respectively). CONCLUSIONS: The results show that violence is associated with a higher HIV prevalence among FSWs and that condom breakage is a potential mediator for this association. Longitudinal studies designed to analyze this relationship and specific interventions integrated to current HIV prevention strategies are needed to reduce the burden of violence among FSWs.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".