Associations between drug use and risk behaviours for HIV and sexually transmitted infections among female sex workers in Yunnan, China
Bibliographic record
Abstract
This is a cross-sectional study of 399 subjects conducted to explore the association between drug use and risk behaviour for HIV and sexually transmitted infections (STIs) among female sex workers (FSWs), and also to study the prevalence of HIV/STIs among drug-using FSWs (DUFSWs) and non-DUFSWs in Yunnan province of China. Demographic information, mobility, sexual and drug-using behaviours were collected and subjects were tested for HIV/STIs. Mean age was 27 years (SD = ±7.8) and 94 (23.6%) tested positive for recent opiate use. Compared with non-DUFSWs, DUFSWs had a significantly higher prevalence of HIV (38% versus 4%, P ≤ 0.001), herpes simplex virus type 2 (HSV-2; 92% versus 60%, P ≤ 0.001) and STIs (95.7% versus 69.2%, P ≤ 0.001). DUFSWs had a significantly longer duration of commercial sex work compared with non-DUFSWs (median 5 versus 1 years, P ≤ 0.001), and had at least two clients in the last working day. DUFSWs were also more likely to work in low-end commercial sex venues and frequented a greater number of work locations than non-DUFSWs. Compared with non-DUFSWs, DUFSWs were more likely to exhibit riskier sexual behaviours and greater workplace mobility. Interventions targeting drug-related behavioural changes are needed urgently in this population in order to reduce rates of HIV and STIs.
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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 | 0.000 | 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".