Poor working conditions and work stress among Canadian sex workers
Bibliographic record
Abstract
BACKGROUND: While sex work is often considered the world's oldest profession, there remains a dearth of research on work stress among sex workers (SWs) in occupational health epidemiological literature. A better understanding of the drivers of work stress among SWs is needed to inform sex work policy, workplace models and standards. AIMS: To examine the factors that influence work stress among SWs in Metro Vancouver. METHODS: Analyses drew from a longitudinal cohort of SWs, known as An Evaluation of Sex Workers' Health Access (AESHA) (2010-14). A modified standardized 'work stress' scale, multivariable linear regression with generalized estimating equations was used to longitudinally examine the factors associated with work stress. RESULTS: In multivariable analysis, poor working conditions were associated with increased work stress and included workplace physical/sexual violence (β = 0.18; 95% confidence interval (CI) 0.06, 0.29), displacement due to police (β = 0.26; 95% CI 0.14, 0.38), working in public spaces (β = 0.73; 95% CI 0.61, 0.84). Older (β = -0.02; 95% CI -0.03, -0.01) and Indigenous SWs experienced lower work stress (β = -0.25; 95% CI -0.43, -0.08), whereas non-injection (β = 0.32; 95% CI 0.14, 0.49) and injection drug users (β = 0.17; 95% CI 0.03, 0.31) had higher work stress. CONCLUSIONS: Vancouver-based SWs' work stress was largely shaped by poor work conditions, such as violence, policing, lack of safe workspaces. There is a need to move away from criminalized approaches which shape unsafe work conditions and increase work stress for SWs. Policies that promote SWs' access to the same occupational health, safety and human rights standards as workers in other labour sectors are also needed.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".