Barriers to Governmental Income Supports for Sex Workers during COVID-19: Results of a Community-Based Cohort in Metro Vancouver
Bibliographic record
Abstract
The COVID-19 pandemic has brought into stark focus the economic inequities faced by precarious, criminalized and racialized workers. Sex workers have been historically excluded from structural supports due to criminalization and occupational stigma. Given emerging concerns regarding sex workers' inequitable access to COVID-19 income supports in Canada and elsewhere, our objective was to identify prevalence and correlates of accessing emergency income supports among women sex workers in Vancouver, Canada. Data were drawn from a longstanding community-based open cohort (AESHA) of cis and trans women sex workers in Metro Vancouver from April 2020-April 2021 (n = 208). We used logistic regression to model correlates of access to COVID-19 income supports. Among 208 participants, 52.9% were Indigenous, 6.3% Women of Colour (Asian, Southeast Asian, or Black), and 40.9% white. Overall, 48.6% reported accessing income supports during the pandemic. In adjusted multivariable analysis, non-injection drug use was associated with higher odds of accessing COVID-19 income supports (aOR: 2.58, 95% CI: 1.31-5.07), whereas Indigenous women faced reduced odds (aOR 0.55, 95% CI 0.30-1.01). In comparison with other service workers, access to income supports among sex workers was low overall, particularly for Indigenous sex workers, demonstrating the compounding impacts of colonization and disproportionate criminalization of Indigenous sex workers. Results highlight the need for structural supports that are low-barrier and culturally-safe to support sex workers' health, safety and dignity.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".