Strategies and Challenges in Preventing Violence Against Canadian Indoor Sex Workers
Bibliographic record
Abstract
OBJECTIVES: To examine indoor sex workers' strategies in preventing workplace violence and influential socio-structural conditions. METHODS: Data included qualitative interviews with 85 sex workers in British Columbia, Canada, from 2014 through 2016. For analyses, we used interpretive thematic techniques informed by World Health Organization position statements on violence. RESULTS: Robbery, nonpayment, financial exploitation, and privacy violations were frequent types of violence perpetrated by clients, landlords, and neighbors. We identified 2 themes that depicted how sex workers prevented violence and mitigated its effects: (1) navigating physical spaces and (2) navigating client relationships. CONCLUSIONS: Sex workers' diverse strategies to prevent violence and mitigate its effects are creative and effective in many circumstances. These are limited, however, by the absence of legal and public health regulations governing occupational health and safety and stigma associated with sex work. Public Health Implications. Occupational health and safety regulatory policies that set conditions for clients' substance and condom use within commercial sex transactions are required. Revisions to the current legal regulations governing prostitution are critical to support optimal work environments that reduce the likelihood of violence. These revisions must recognize sex work as a form of labor versus victimization.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".