An Undergraduate Medical Education Framework for Refugee and Migrant Health: Curriculum Development and Conceptual Approaches
Bibliographic record
Abstract
Abstract Background: International migration, especially forced migration, highlights important medical training needs including cross-cultural communication, human rights, as well as global health competencies for physical and mental healthcare. This paper responds to the call for a ‘trauma informed’ refugee health curriculum framework from medical students and global health faculty. Methods: We used a mixed-methods approach to develop a guiding medical undergraduate refugee and migrant health curriculum framework. We conducted a scoping review, key informant faculty interviews and e-surveys, and then, integrated our results into a competency-based curriculum framework with values and principles, learning objectives and curriculum delivery methods and evaluation. Results: The majority of our Canadian medical faculty respondents reported some refugee health learning objectives within their undergraduate medical curriculum. The most prevalent learning objective topics included access to care barriers, social determinants of health for refugees, cross-cultural communication skills, global health epidemiology, challenges and pitfalls of providing care and mental health. We report competency-based learning objectives and primary and secondary topics. We also discuss curriculum delivery and evaluation methods such as community service learning with reflection exercises. Conclusions: This guiding undergraduate medical education curriculum suggests integrating cross-cultural communication skills, exploration of access to care barriers for newcomers, and system approaches to improve refugee and migrant healthcare. Programs should also consider social determinants of health, community service learning and the development of links to community resettlement and refugee organizations.
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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 | 0.026 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 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".