Challenges to Supporting Domestically Sex Trafficked Persons: In-Depth Interviews with Service Providers
Bibliographic record
Abstract
Domestic sex trafficking is an emergent area of study with problematic gaps in our understanding of the challenges that inhibit client recovery. As social service providers are often on the frontlines of care provision, in this study, we explored the challenges they experienced when serving domestically sex trafficked adolescents and adults. Semi-structured interviews were conducted with 15 providers in Ontario, Canada’s largest province, and thematically analyzed. Our study found that providers faced systemic-, provider-, and client-related challenges, including insufficient funding, a dearth of (appropriate) shelter and/or housing, problems with healthcare and health professionals, entrenched biases within law enforcement, the weight of emotional work, fear for themselves and their clients, survivors’ misgivings about the systems established to assist them, and their unresolved concurrent mental health issues. By exploring intersections among various challenges facing service providers with the goal of improving services for domestically sex trafficked persons in Canada, we contribute to discourses informing research, policy, and practice considerations in various jurisdictions, working toward achieving UN Sustainable Development Goals 5 and 16 (specifically targets 5.2, 16.1, and 16.2).
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".