Intravaginal practices among HIV‐negative female sex workers along the US–Mexico border and their implications for emerging HIV prevention interventions
Bibliographic record
Abstract
OBJECTIVE: To describe intravaginal practices (IVPs) among female sex workers (FSWs) who inject drugs in two cities-Tijuana and Ciudad Juarez-on the border between the USA and Mexico. METHODS: Data for a secondary analysis were obtained from interviews conducted as part of a randomized controlled trial in FSWs who injected drugs between October 28, 2008, and May 31, 2010. Eligible individuals were aged at least 18years and reported sharing injection equipment and having unprotected sex with clients in the previous month. Descriptive statistics were used to assess frequency and type of IVPs. Logistic regression was used to assess correlates of IVPs. RESULTS: Among 529 FSWs who completed both surveys, 229 (43.3%) had performed IVPs in the previous 6months. Factors independently associated with IVPs were reporting any sexually transmitted infection in the previous 6months (adjusted odds ratio [aOR] 1.8, 95% confidence interval [CI] 1.1-3.1; P=0.03), three or more pregnancies (aOR 1.9, 95% CI 1.1-3.2; P=0.02), and having clients who became violent when proposing condom use (aOR 5.8, 95% CI 1.0-34.3; P=0.05), which are all factors related to inconsistent condom use. CONCLUSION: Screening for IVPs could help to identify FSW at increased risk of HIV, and facilitate conversations about specific risk-reduction methods.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".