Sex Workers Perspectives on Strategies to Reduce Sexual Exploitation and HIV Risk: A Qualitative Study in Tijuana, Mexico
Bibliographic record
Abstract
Globally, female sex workers are a population at greatly elevated risk of HIV infection, and the reasons for and context of sex industry involvement have key implications for HIV risk and prevention. Evidence suggests that experiences of sexual exploitation (i.e., forced/coerced sex exchange) contribute to health-related harms. However, public health interventions that address HIV vulnerability and sexual exploitation are lacking. Therefore, the objective of this study was to elicit recommendations for interventions to prevent sexual exploitation and reduce HIV risk from current female sex workers with a history of sexual exploitation or youth sex work. From 2010-2011, we conducted in-depth interviews with sex workers (n = 31) in Tijuana, Mexico who reported having previously experienced sexual exploitation or youth sex work. Participants recommended that interventions aim to (1) reduce susceptibility to sexual exploitation by providing social support and peer-based education; (2) mitigate harms by improving access to HIV prevention resources and psychological support, and reducing gender-based violence; and (3) provide opportunities to exit the sex industry via vocational supports and improved access to effective drug treatment. Structural interventions incorporating these strategies are recommended to reduce susceptibility to sexual exploitation and enhance capacities to prevent HIV infection among marginalized women and girls in Mexico and across international settings.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".