Structural Determinants of Health among Im/Migrants in the Indoor Sex Industry: Experiences of Workers and Managers/Owners in Metropolitan Vancouver
Bibliographic record
Abstract
BACKGROUND: Globally, im/migrant women are overrepresented in the sex industry and experience disproportionate health inequities. Despite evidence that the health impacts of migration may vary according to the timing and stage of migration (e.g., early arrival vs. long-term migration), limited evidence exists regarding social and structural determinants of health across different stages of migration, especially among im/migrants engaged in sex work. Our aim was to describe and analyze the evolving social and structural determinants of health and safety across the arrival and settlement process for im/migrants in the indoor sex industry. METHODS: We analyzed qualitative interviews conducted with 44 im/migrant sex workers and managers/owners working in indoor sex establishments (e.g., massage parlours, micro-brothels) in Metropolitan Vancouver, Canada in 2011; quantitative data from AESHA, a larger community-based cohort, were used to describe socio-demographic and social and structural characteristics of im/migrant sex workers. RESULTS: Based on quantitative data among 198 im/migrant workers in AESHA, 78.3% were Chinese-born, the median duration in Canada was 6 years, and most (86.4%) serviced clients in formal indoor establishments. Qualitative narratives revealed diverse pathways into sex work upon arrival to Canada, including language barriers to conventional labour markets and the higher pay and relative flexibility of sex work. Once engaged in sex work, fear associated with police raids (e.g., immigration concerns, sex work disclosure) and language barriers to sexual negotiation and health, social and legal supports posed pervasive challenges to health, safety and human rights during long-term settlement in Canada. CONCLUSIONS: Findings highlight the critical influences of criminalization, language barriers, and stigma and discrimination related to sex work and im/migrant status in shaping occupational health and safety for im/migrants engaged in sex work. Interventions and policy reforms that emphasize human rights and occupational health are needed to promote health and wellbeing across the arrival and settlement process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".