Conducting HIV/AIDS Research With Indoor Commercial Sex Workers: Reaching a Hidden Population
Bibliographic record
Abstract
BACKGROUND: Although comprising up to 80% of the commercial sex industry in Canada, indoor female sex workers (FSW) are generally not represented in research because they are a hidden population and difficult to access. OBJECTIVES: This paper describes a community-academic partnership model that was established to gain access to, deliver outreach services to, and conduct community-based research with the indoor commercial sex industry in four cities in British Columbia. METHODS: The project employed an ongoing community consultation, peer-delivered approach to reaching this overlooked segment of the commercial sex industry. Peers (former and current FSW) were hired, trained, and supported as outreach workers and participated in the development, implementation, and evaluation of the project. Outreach teams visited sex establishments to deliver harm reduction materials and provide education, support, and referrals. The teams developed rapport with establishment managers and staff to facilitate research recruitment and data collection. The community team leader met with managers in targeted business to describe the study and elicit permission to recruit workers. The team leader conducted in-person interviews with consenting FSW. OBSERVATIONS: During the first 2 years of the project, more than 50 sex establishments were visited by outreach teams and 37 allowed repeat visits. Research interviews have been conducted with 49 FSW in seven establishments from four cities. CONCLUSIONS: Although the high cost in terms of time and resources must be recognized, this project represents a successful research and outreach model that permits access to the hidden commercial sex industry.
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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 | 0.012 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".