Sexual behaviour, structural vulnerabilities and HIV prevalence among female sex workers in Pakistan
Bibliographic record
Abstract
BACKGROUND: We sought to describe differences in individual and structural vulnerabilities faced by female sex workers (FSWs) in Pakistan between 2006 and 2011, and to characterise risk factors for inconsistent condom use and HIV prevalence in this population. METHODS: To describe differences in vulnerabilities, we analysed behavioural data from serial cross-sectional surveys conducted across nine cities in 2006 and 2011. Using data from 12 cities in 2011, we used logistic regression to characterise risk factors for (a) inconsistent condom use in the past month (N=6987), and (b) HIV (N=4301). RESULTS: Compared to FSWs in 2006, FSWs in 2011 were significantly more likely to solicit clients via cell phones, and to report a larger client volume and anal sex with clients, but also consistent condom use with clients (30.0% vs 23.6% in 2006). In 2011, independent risk factors for inconsistent condom use with clients included: recent sexual violence, recent sex with a person who injects drugs, and absence of programme exposure. HIV prevalence was 0.63% (95% CI 0.43% to 0.92%) in 2011, and was associated with a recent history of injection drug use and absence of programme exposure. CONCLUSIONS: While condom use with clients was higher in 2011, protective behaviours remained low and vulnerabilities related to sex work may have risen. HIV is emerging in this population and an adaptive HIV prevention programme that addresses different vulnerabilities and the intersection of sexual networks with injection drug use is 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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".