Morning in refugee health: an introduction for medical students
Bibliographic record
Abstract
Purpose – Migration is increasing worldwide. health care practitioners must provide care to migrants in a culturally competent manner that is sensitive to cultural, political and economic contexts shaping health and illness. Many studies have provided strong evidence that health providers benefit from training in cross-cultural care. Cultural competence education of medical students during their early learning can begin to address attitudes and responsiveness toward refugees. At Memorial University in Canada, the authors designed “Morning in Refugee Health”, an innovative program in cultural competency training for first year medical students in the Clinical Skills and Ethics course. The purpose of this paper is to discuss these issues. Design/methodology/approach – Here the authors introduce the curriculum and provide the rationale for the specific pedagogical techniques employed, emphasizing the consideration of culture in its relation to political and economic contexts. The authors describe the innovation of training standardized patients (SPs) who are themselves immigrants or refugees. The authors explain how and why the collaboration of community agencies and medical school administration is key to the successful implementation of such a curriculum. Findings – Medical students benefit from early pre-clinical education in refugee health. Specific attention to community context, SP training, small group format, linkages between clinical skills and medical ethics, medical school administrative and community agency support are essential to development and delivery of this curriculum. As a result of the Morning in Refugee Health, students initiated a community medical outreach project for newly arriving refugees. Originality/value – The approach is unique in three ways: integration of training in clinical skills and ethics; training of SPs who are themselves immigrants or refugees; and reflection on the political, economic and cultural contexts shaping health and health care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".