Individual, Interpersonal, and Social-Structural Correlates of Involuntary Sex Exchange Among Female Sex Workers in Two Mexico–U.S. Border Cities
Bibliographic record
Abstract
BACKGROUND: To investigate individual, interpersonal, and social-structural factors associated with involuntary sex exchange among female sex workers (FSWs) along the Mexico-U.S. border. METHODS: In 2010 to 2011, 214 FSWs from Tijuana (n = 106) and Ciudad Juarez (n = 108) aged ≥ 18 years who reported lifetime use of heroin, cocaine, crack, or methamphetamine, having a stable partner, and having sold/traded sex in the past month completed quantitative surveys and HIV/sexually transmitted infection testing. Logistic regression was used to identify correlates of involuntary sex exchange among FSWs. RESULTS: Of 214 FSWs, 31 (14.5%) reported involuntary sex exchange These women were younger at sex industry entry [adjusted odds ratio (AOR): 0.84/1-year increase, 95% confidence interval (CI): 0.72 to 0.97] and were significantly more likely to service clients whom they perceived to be HIV/sexually transmitted infection-infected (AOR: 12.41, 95% CI: 3.15 to 48.91). In addition, they were more likely to have clients who used drugs (AOR: 7.88, 95% CI: 1.52 to 41.00), report poor working conditions (AOR: 3.27, 95% CI: 1.03 to 10.31), and report a history of rape (AOR: 4.46, 95% CI: 1.43 to 13.91). CONCLUSIONS: Involuntary sex exchange is disproportionate among FSWs who begin to exchange sex at a younger age, and these women experience elevated risk of violence and HIV/STIs related to their clients' behaviors and their working conditions. These data suggest the critical need for evidence-based approaches to preventing sexual exploitation of women and girls and to reducing harm among current sex workers. Multilevel interventions for all females who exchange sex and their clients that target interpersonal and social-structural risks (eg, measures to improve safety and reduce exploitation within the workplace) are needed.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".