A protocol for a qualitative study on sex trafficking: Exploring knowledge, attitudes, and practices of physicians, nurses, and social workers in Ontario, Canada
Bibliographic record
Abstract
INTRODUCTION: There has been limited research on sex trafficking in Canada from a health and health care perspective, despite U.S. research which points to health care providers as optimally positioned to identify and help those who have been sex trafficked. We aim to better understand health care providers' knowledge about, attitudes towards, and care of those who have been sex trafficked in Ontario, Canada. METHODS AND ANALYSIS: Using a semi-structured interview guide, we will interview physicians, nurses, and social workers working in a health care setting in Ontario until data saturation is reached. An intersectional lens will be applied to the study; analysis will follow the six analytic phases outlined by Braun and Clarke. In the development of this study, we consulted the consolidated criteria for reporting qualitative research (COREQ) with regards to reflexivity and study design. We will continue to consult this checklist as the study progresses and in the writing of our analysis and findings. DISCUSSION: To our knowledge, this will be the first study of its kind in Canada. The results hold the potential to inform the development of standardized training on sex trafficking for health care providers. Results of the study may be useful in addressing sex trafficking in other jurisdictions.
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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 | 0.077 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.020 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.050 | 0.005 |
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".