Gaps in health coverage for racialized im/migrant sex workers in metro Vancouver: Findings of a community-based cohort study (2014–2021)
Bibliographic record
Abstract
Background: Sex workers face substantial health inequities related to sexual health and gender-based violence, many of which are amplified for the large proportion of workers who are racialized im/migrants. While criminalization and stigma are known barriers to health care for this population, we know little about health insurance coverage, and in particular how this relates to im/migration experience and racialization. We examined associations between im/migration status, duration, and racialization on gaps in health insurance coverage in a cohort of women sex workers. Methods: Analyses used data from a prospective, community-based cohort of women sex workers in Vancouver, BC (Sept 2014-August 2021). Interviewer-administered questionnaires were by experiential (current/former sex workers) and community-based staff. We developed multivariable logistic regression confounder models with generalized estimating equations (GEE) to examine associations between migration and racialization exposures of interest and health insurance coverage. Results: Of 644 sex workers, 411 (63.8%) reported lacking health insurance coverage for services needed during the 7-year study. In multivariable GEE analysis, precarious im/migration status (adjusted odds ratio (AOR) 2.37, 95% confidence interval (CI) 1.56 - 3.60), recent (AOR 4.22, 95% CI 2.42 - 7.35) and long-term (AOR 2.13, 95% CI 1.54 - 2.96) migration, and being a racialized Asian im/migrant (AOR 3.06, 95% CI 2.14 - 4.39) were associated with recent lack of health insurance coverage. Conclusion: Policy and program reforms are needed to decouple health insurance access from immigration status, remove mandatory waiting periods for health insurance coverage, and ensure that provincial insurance provides sufficient coverage for marginalized women's healthcare needs.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".