Preparing residents to deal with human trafficking
Bibliographic record
Abstract
BACKGROUND: Victims of human trafficking (HT) are predisposed to numerous health concerns. Many encounter health care practitioners during captivity, but awareness and knowledge among front-line physicians is low. Limited data exist on attempts to address this within residency training programmes. Formal curriculum time in residency is limited and online modules may be a useful educational option. METHODS: Residents in family medicine, emergency medicine and general paediatrics at the University of Alberta were invited to participate. They completed short surveys to assess knowledge both before and after completing an online learning module either individually (n = 15) or in a facilitated session (n = 17). Baseline and post-intervention changes in self-reported and tested knowledge were assessed. RESULTS: Thirty-two residents completed the pre-intervention survey: only 6% self-identified as somewhat knowledgeable on HT and 16% knew the red flags used to identify victims. Eighty-one percent wanted this topic incorporated into residency training, but only 6% and 25% had received education previously in residency or medical school, respectively. Thirteen percent were comfortable supporting victims, and 6% reported knowing how to provide support. Twenty residents completed the post-intervention survey, with improvements in both self-reported (p < 0.001) and tested (p = 0.005) knowledge of HT. Residents also reported being more prepared to identify victims (p < 0.001), more comfortable supporting victims (p < 0.001) and more confident in knowing how to support victims (p < 0.001). DISCUSSION: Baseline HT knowledge in residents providing first-contact care appears limited. Residency programmes should consider providing more HT education in order to improve competency in care. Although an online module was shown to be effective, protected time might be necessary for the widespread adoption of online education delivery.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".