Intersectional associations between citizenship, English fluency and racialisation on access to health and sex work community services: findings from a prospective cohort of sex workers in Canada (2014–2022)
Bibliographic record
Abstract
OBJECTIVES: To examine the intersectional associations between migration experiences and use of health and sex work community-based services among women sex workers. DESIGN: Data were drawn from An Evaluation of Sex Workers Health Access, a community-based cohort of sex workers from September 2014 to February 2022. Bivariate and multivariable regression with generalised estimating equations (GEEs) using interaction terms was used to separately model associations between intersectional aspects of the migration experience (citizenship, English fluency and Asian identity) and service access outcomes. SETTING: Diverse community-based sex work venues in Metro Vancouver, Canada. PARTICIPANTS: 652 cis and trans women sex workers, with 149 (22.8%) being immigrants/migrants born outside of Canada (n=149). PRIMARY OUTCOME MEASURES: (1) Accessing health services when needed and (2) utilisation of sex work community-based services. RESULTS: In separate adjusted multivariable GEE models, we found significantly reduced odds of accessing health services when needed for women without Canadian citizenship and with limited English fluency, as well as those lacking Canadian citizenship but speaking fluently. Significantly reduced odds of accessing health services were also found among sex workers without Canadian citizenship and who identified as Asian. Regarding using sex work community-based services, women sex workers lacking Canadian citizenship and with limited English fluency, and those who were Asian and lacked Canadian citizenship, had low odds of using sex work community-based services. CONCLUSIONS: Findings show a gradient in the relationship between intersectional experiences of lack of citizenship, limited English fluency and Asian identity on sex workers' access to health services and sex work community-based services. Culturally responsive and language-tailored services that attend to and address these intersecting forms of structural marginalisation, along with the full decriminalisation of all aspects of sex work, and the removal of punitive sex work-related immigration policies, are recommended.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".