Human trafficking screening in Saskatoon Emergency Departments: What can be learned from high-risk patient presentations?
Bibliographic record
Abstract
OBJECTIVE: Studies have shown that Emergency Department physicians have little to no training in recognizing and supporting victims of human trafficking despite being uniquely situated to identify and intervene on behalf of these patients. We assessed if screening for human trafficking was being completed by emergency physicians in three Saskatoon emergency departments. METHODS: We performed a retrospective chart review of patients presenting to three Saskatoon emergency departments deemed to potentially be at risk of human trafficking, based on discharge diagnosis. Of the 223 included charts, data extracted included sex, age, ethnicity, chief complaint, diagnosis, disposition, HT Screening (Y/N), specific quotes relating to HT, time of visit, intimate partner violence (Y/N), and travel history. Both quantitative and qualitative thematic analyses were conducted on this data. RESULTS: None of the charts (0%) included in this study had any documentation around screening for human trafficking. Furthermore, 21.1% of the high-risk patient charts included in this study -- which included many patients with a discharge diagnosis of sexually transmitted disease or pelvic inflammatory disease -- did not contain a documented sexual history. Thematic analysis revealed that the patients included in this study frequently had challenges with sexual health, substance use, and houselessness. CONCLUSION: This study found that Emergency physicians in Saskatoon were not routinely screening for human trafficking. Implementation of further training is needed to help these physicians recognize and subsequently support potential victims of human trafficking.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".