Exploring the experiences of wellbeing, health, and healthcare among women who have been domestically sex trafficked in Ontario, Canada: A qualitative study protocol
Bibliographic record
Abstract
INTRODUCTION: Although there is a growing body of evidence to suggest that persons who have been sex trafficked can suffer devastating health consequences and often face challenges accessing suitable care that addresses their health and overall well-being, little existing research has adopted a survivor-informed approach. Centering the voices of sex-trafficked women in this research will provide valuable insights into their health-related experiences and can help lay the foundation for survivor-centric healthcare responses. METHODS AND ANALYSIS: Using a semi-structured interview guide, we will interview women who have been domestically sex trafficked in Ontario; recruitment will continue until data saturation is reached. Interview questions and prompts will elicit information about women's experiences prior to, during, and after their trafficking ordeal, with particular attention paid to their encounters with healthcare providers. Intersectionality theory will inform strategies for recruitment, data collection, and data analysis. Data will be analyzed deductively as well as inductively using Braun and Clarke's six phases of reflexive thematic analysis. The study's design was informed by the consolidated criteria for reporting qualitative research (COREQ), which ensures a comprehensive and robust reporting of interview data. We will continue to adhere to the COREQ checklist throughout the data collection, analysis, and findings write-up phases, helping to ensure methodological accuracy and transparency. DISCUSSION: To our knowledge, this will be the first Canada-specific investigation to apply intersectionality theory to explore the experiences of well-being, health, and healthcare from the perspectives of women who have been domestically sex trafficked. The results of this study hold the potential to improve responses to trafficking within the healthcare sector. Specifically, the findings could be used to inform the development of education materials and curricula for medical students and continuing professional education for health and allied healthcare providers. They could also inform the creation of patient experience surveys and intake forms for sex trafficked patients.
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 | 0.022 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".