Drug-Using Female Sex Workers and HIV Risk: A Systematic Review of the Global Literature
Bibliographic record
Abstract
This review examines the global literature concerning HIV risk among drug-using female sex workers (DU-FSWs). In the context of HIV prevention, the possible synergetic effects of sexual risk and drug-related risk merit a systematic review to get a better understanding of such effects among this highly vulnerable population. In particular, we look at research on the association between drug use and HIV risk among female sex workers (FSWs) in terms of multiple indicators such as HIV infection, needle sharing, and unprotected sex.The current review, through synthesizing the findings from 41 studies conducted in multiple nations, reveals a complex picture of HIV risk for DU-FSWs across diverse societies. Research findings are mixed but tend to show that drug-related and sex-related risk behaviors accelerated the risk of HIV/STI among DU-FSWs, underscoring considerable vulnerabilities. However, findings about the level of the association and significance, as well as the mechanisms of HIV transmission, are inconsistent among various empirical studies. The variations in findings may be attributed to the specificities of diverse social contexts, various characteristics of the study samples, and different measurements in different studies. The mixed findings point to the need for more empirical studies targeting DU-FSWs to understand how drug use and sexual risk interactively affect this population differently in different social contexts. Future research should focus on multiple-level risk/preventive factors, assess the overlap between drug-using networks and sexual networks, and identify the synergetic dynamics between drug use and sex work. Development of conceptual frameworks and methodological innovations are also 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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".