Negotiating Safety and Sexual Risk Reduction With Clients in Unsanctioned Safer Indoor Sex Work Environments: A Qualitative Study
Bibliographic record
Abstract
OBJECTIVES: We examined how unique, low-barrier, supportive housing programs for women who are functioning as unsanctioned indoor sex work environments in a Canadian urban setting influence risk negotiation with clients in sex work transactions. METHODS: We conducted 39 semistructured qualitative interviews and 6 focus groups with women who live in low-barrier, supportive housing for marginalized sex workers with substance use issues. All interviews were transcribed verbatim and thematically analyzed. RESULTS: Women's accounts indicated that unsanctioned indoor sex work environments promoted increased control over negotiating sex work transactions, including the capacity to refuse unwanted services, negotiate condom use, and avoid violent perpetrators. Despite the lack of formal legal and policy support for indoor sex work venues in Canada, the environmental-structural supports afforded by these unsanctioned indoor sex work environments, including surveillance cameras and support from staff or police in removing violent clients, were linked to improved police relationships and facilitated the institution of informal peer-safety mechanisms. CONCLUSIONS: This study has drawn attention to the potential role of safer indoor sex work environments as venues for public health and violence prevention interventions and has indicated the critical importance of removing the sociolegal barriers preventing the formal implementation of such programs.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.003 | 0.002 |
| 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".