An undergraduate medical education framework for refugee and migrant health: Curriculum development and conceptual approaches
Bibliographic record
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 interviews with global health faculty with follow-up 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 proposed a curriculum framework that incorporates values and principles, competency-based learning objectives, curriculum delivery (i.e., community service learning), and evaluation methods. CONCLUSIONS: The results of this study informed the development of a curriculum framework that integrates cross-cultural communication skills, exploration of barriers towards accessing care 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".