Lifetime and past-month substance use and injection among street-based female sex workers in Iran
Bibliographic record
Abstract
BACKGROUND: Street-based female sex workers (FSWs) are highly at risk of HIV and other harms associated with sex work. We assessed the prevalence of non-injection and injection drug use and their associated factors among street-based FSWs in Iran. METHODS: We recruited 898 FSWs from 414 venues across 19 major cities in Iran between October 2016 and March 2017. Correlates of lifetime and past-month non-injection and injection drug use were assessed through multivariable logistic regression models. Adjusted odds ratios (AOR) and 95% confidence intervals (CI) were reported. RESULTS: Lifetime and past-month non-injection drug use were reported by 60.3% (95% CI 51, 84) and 47.2% (95% CI 38, 67) of FSWs, respectively. The prevalence of lifetime and past-month injection drug use were 8.6% (95% CI 6.9, 10.7) and 3.7% (95% CI 2.6, 5.2), respectively. Recent non-injection drug use was associated with divorced marital status (AOR 2.00, 95% CI 1.07, 3.74), temporary marriage (AOR 4.31 [1.79, 10.40]), had > 30 clients per month (AOR 2.76 [1.29, 5.90]), ever alcohol use (AOR 3.03 [1.92, 6.79]), and history of incarceration (AOR 7.65 [3.89, 15.30]). Similarly, lifetime injection drug use was associated with ever alcohol use (AOR 2.74 [1.20-6.20]), ever incarceration (AOR 5.06 [2.48-10.28]), and ever group sex (AOR 2.44 [1.21-4.92]). CONCLUSIONS: Non-injection and injection drug use are prevalent among street-based FSWs in Iran. Further prevention programs are needed to address and reduce harms associated with drug use among this vulnerable population in Iran.
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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 | 0.000 | 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".