Distribution of Sexually Transmitted Diseases and Risk Factors by Work Locations Among Female Sex Workers in Tijuana, Mexico
Bibliographic record
Abstract
BACKGROUND: Sex work is regulated in the Zona Roja (red light district) in Tijuana, Mexico, where HIV and sexually transmitted disease (STD) prevalence is high among female sex workers (FSWs). We examined the spatial distribution of STDs by work venue among FSWs in Tijuana. METHODS: FSWs aged 18 years and older who reported unprotected sex with ≥ 1 client in the past 2 months underwent testing for HIV, syphilis, gonorrhea, and Chlamydia. HIV/STDs were mapped by venue (i.e., bar, hotel) and Getis-Ord Gi statistics were used to identify geographic hotspots. High-risk venues were then identified using a standardized STD ratio (high risk defined as a ratio ≥ 1.25). Logistic regression was used to assess correlates of working at a high risk venue. RESULTS: Of 474 FSWs, 176 (36.4%) had at least 1 bacterial sexually transmitted infection (STI); 36 (7.6%) were HIV-positive. Within the Zona Roja, 1 venue was identified as a geographic "hotspot," with a higher than expected number of HIV/STD-positive FSW (P < 0.05) as compared to neighboring venues. Using the STD ratio definition, 11 venues were identified as high-risk; FSWs working in these locations had higher education, were more likely to report always using drugs with sex, and having mostly US clients. They were less likely to be registered FSWs or to live at their work venue. CONCLUSIONS: A relatively few number of sex work venues accounted for a large proportion of the HIV/STI burden among FSWs in Tijuana. Structural interventions that focus on sex work venues could help increase STI diagnosis, prevention, and treatment among FSWs in Tijuana.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".