An Evaluation of Indoor Sex Workers’ Sexual Health Access in Metro Vancouver: Applying an Occupational Health & Safety Lens in the Context of Criminalization
Bibliographic record
Abstract
The criminalization of sex work has been consistently shown to undermine workers’ Occupational Health and Safety (OHS), including sexual health. Drawing on the ‘Guide to OHS in the New Zealand Sex Industry’ (the Guide), we assessed barriers to sexual health best practices among indoor sex workers in Metro Vancouver, Canada, in the context of ongoing criminalization. Part of a longstanding community-based study, this analysis drew on 47 qualitative interviews (2017–2018) with indoor sex workers and third parties. Participants’ narratives were analyzed drawing on a social determinants of health framework and on the Guide with specific focus on sexual health. Our findings suggest that sex workers and third parties utilize many sexual health strategies, including use of Personal Protective Equipment (PPE) and peer-driven sexual health education. However, participant narratives demonstrate how structural factors such as criminalization, immigration, and stigma limit the accessibility of additional OHS best practices outlined in the Guide and beyond, including access to non-stigmatizing sexual health assessments, and distribution of diverse PPE by third parties. Our current study supports the need for full decriminalization of sex work, including im/migrant sex work, to allow for the uptake of OHS guidelines that support the wellbeing and autonomy of all sex workers.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".