A child-centered approach to trauma- and violence-informed interventions with children who have experienced sex trafficking: Qualitative findings from emergency department providers in Ontario, Canada
Bibliographic record
Abstract
More than 60% of people who have experienced sex trafficking access an emergency department (ED) for trafficking-related concerns while they are being exploited. Yet, tailored assessment, intervention, and referral practices in the ED remain underexplored. Thus, we conducted virtual, semi-structured interviews with 12 multidisciplinary healthcare providers at four pediatric EDs across Ontario to explore how they respond to presentations of child sex trafficking. Through an intersectional, reflexive thematic analysis, narratives illuminated how childhood is redefined in the context of child sex trafficking, with healthcare providers perceiving children who have experienced sex trafficking (CEST) as more mature and less innocent due to their exploitative experiences. These shifting perceptions highlighted the disparate power dynamics that produce and reproduce these children’s exploitative vulnerabilities, both within the child-trafficking and child-provider relationships, prompting providers to tailor interventions that incorporate a child-centered approach to trauma- and violence-informed care. This approach considered both developmental needs and the socio-structural factors that implicitly impact children because of their age. In doing so, providers working in Ontario’s pediatric EDs are paving the way for integrating a child-centered trauma- and violence-informed approach as a universal standard of practice.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.015 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".