Recommendations to Improve Services and Supports for Domestically Sex Trafficked Persons Derived from the Insights of Health Care Providers
Bibliographic record
Abstract
Janice Du Mont,1,2 Frances Montemurro,1 Rhonelle Bruder,1,2 C Emma Kelly,1 Frances Recknor,1 Robin Mason1,2 1Women’s College Research Institute, Women’s College Hospital, Toronto, ON, M5S 1B2, Canada; 2Dalla Lana School of Public Health, University of Toronto, Toronto, ON, M5T 3M7, CanadaCorrespondence: Janice Du Mont, Women’s College Research Institute, Women’s College Hospital, 76 Grenville St, Toronto, ON, M5S 1B2, Canada, Email janice.dumont@wchospital.caAbstract: Health care providers are highly likely to encounter persons who have been domestically sex trafficked and, therefore, possess valuable insights that could be useful in understanding and improving existing services and supports. In-depth interviews were conducted with 31 health care providers residing and working in Canada’s largest province, Ontario. Results were analyzed using Braun and Clarke’s analytical framework. Across providers, a key theme was identified: “Facilitators to improve care”, which was comprised of two sub-themes, “Address needs in service provision” and “Center unique needs of survivors”. From these results, eight wide-ranging recommendations to improve services and supports were developed (eg, Jointly mobilize an intersectoral, collaborative, and coordinated approach to sex trafficking service provision; Employ a survivor-driven approach to designing and delivering sex trafficking services). These recommendations hold the potential to enhance services in Canada and beyond by reducing barriers to access and care, facilitating disclosure, aiding in recovery, and empowering those who have been domestically sex trafficked.Keywords: domestic, human trafficking, health care, recommendations, sex trafficking, social services
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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 | 0.039 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.008 | 0.014 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".