Health advocacy for refugees: Medical student primer for competence in cultural matters and global health.
Bibliographic record
Abstract
PROBLEM BEING ADDRESSED: Canadian family physicians serve a patient population that is increasingly diverse, both culturally and linguistically. Family medicine needs to take a leadership role in developing social accountability and cultural sensitivity among physicians. OBJECTIVE OF PROGRAM: To train medical students to work with newly arriving refugees, to foster competence in handling cultural issues, to raise awareness of global health, and to engage medical students in work with underserviced populations in primary care. PROGRAM DESCRIPTION: The program is composed of an Internet-based training module and a self-assessment quiz focused on global and refugee health, a workshop to increase competence in cultural matters, an experience working with at least 1 refugee family at a shelter for newly arriving refugees, family physician mentorship, and a debriefing workshop at the end of the experience. Students who complete this program are eligible for further electives at a refugee health clinic. CONCLUSION: The program has been received enthusiastically by students, refugees, and family physicians. Working with refugees provides a powerful introduction to issues related to global health and competence in cultural matters. The program also provides an opportunity for medical students to work alongside family physicians and nurtures their interest in working with disadvantaged populations.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".