Uptake of a women‐only, sex‐work‐specific drop‐in center and links with sexual and reproductive health care for sex workers
Bibliographic record
Abstract
OBJECTIVE: To longitudinally examine female sex workers' (FSWs') uptake of a women-only, sex-work-specific drop-in service and its impact on their access to sexual and reproductive health (SRH) services. METHODS: For the present longitudinal analysis, data were drawn from the AESHA (An Evaluation of Sex Workers' Health Access) study, a community-based, open, prospective cohort of FSWs from Vancouver, BC, Canada. Data obtained between January 2010 and February 2013 were analyzed. Participants are followed up on a semi-annual basis. Multivariable logistic regression using generalized estimating equations was used to identify correlates of service uptake. RESULTS: Of 547 FSWs included in the present analysis, 330 (60.3%) utilized the services during the 3-year study period. Service use was independently associated with age (adjusted odds ratio [AOR] 1.04; 95% confidence interval [CI] 1.03-1.06), Aboriginal ancestry (AOR 2.18; 95% CI 1.61-2.95), injection drug use (AOR 1.67; 95% CI 1.29-2.17), exchange of sex for drugs (AOR 1.40; 95%CI 1.15-1.71), and accessing SRH services (AOR 1.65; 95% CI 1.35-2.02). CONCLUSION: A sex-work-specific drop-in space for marginalized FSWs had high uptake. Women-centered and low-threshold drop-in services can effectively link marginalized women with SRH services.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".