Seeing pre-screened, regular clients associated with lower odds of workplace sexual violence and condom refusal amidst sex work criminalization : findings of a community-based cohort of sex workers in Metro Vancouver, Canada (2010-2019)
Bibliographic record
Abstract
Background Research that accurately represents how characteristics of sex work clients relate to sex workers’ labour conditions is crucial for informing evidence-based legislation which upholds sex workers’ human rights. As little quantitative research has examined how seeing regulars (repeat clients) impacts sex workers’ occupational safety, particularly under ‘end-demand’ criminalization in Canada, our study aimed to explore how seeing mostly regulars shapes workplace sexual violence and client condom refusal. Methods We drew on longitudinal data from a community-based open cohort of 900+ sex workers in Vancouver, recruited via time-location sampling during day and late-night outreach to indoor, outdoor, and online solicitation spaces. We used logistic regression analyses and multivariable GEE confounder models to 1) describe correlates of seeing mostly pre-screened, regular clients, 2) identify associations between seeing mostly regulars and odds of experiencing occupational outcomes of workplace sexual violence and client condom refusal, and 3) examine the interaction between seeing mostly regulars and work environment on workplace sexual violence and client condom refusal. Results Participants’ median age was 35, and 55.6% had completed high school. Over the 9-year study (n=925), 20.9% (193) experienced 282 events of workplace sexual violence and 40.2% (372) faced 702 events of client condom refusal. In multivariable GEE confounder models, seeing mostly regulars was associated with reduced odds of sexual violence (AOR 0.73, 95%CI 0.53-1.02, p=0.067) and client condom refusal (AOR 0.70, 95%CI 0.57-0.86). In multivariable GEE confounder models examining the additive interaction between seeing mostly regulars and work environment, participants who saw mostly regulars and primarily worked in outdoor or informal indoor venues faced significantly lower odds of experiencing workplace sexual violence (AOR 0.69, 95%CI 0.49-0.95) and client condom refusal (AOR 0.64, 95%CI 0.52 -0.80) relative to those who worked in the same venues and did not see mostly regulars. Conclusion Our findings highlight protective effects of seeing pre-screened regulars within a criminalized setting. Removal of ‘end-demand’ client criminalization is needed to enable sex workers to effectively screen clients, support HIV/STI prevention, and advance sex workers’ human rights.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".