Matching physicians to newly arrived refugees in a context of physician shortage: innovation through advocacy
Bibliographic record
Abstract
Purpose – Access to a continuum of care from a family physician is an essential component of health and wellbeing. Refugees have particular barriers to accessing medical care. The MUN MED Gateway Project is a medical student initiative in partnership with a refugee settlement agency that provides access to and continuity of health care for new refugees, while offering medical students exposure to cross-cultural health care. This paper aims to report on the first six years of the project. Design/methodology/approach – Here the paper reports on: client patient uptake and demographics, health concerns identified through the project, and physician uptake and rates of patient-physician matches. Findings – Results demonstrate that the project integrates refugees into the health care system and facilitates access to medical care. Moreover, it provides learning opportunities for students to practice cross-cultural health care, with high engagement of medical students and high satisfaction by family physicians involved. Originality/value – Research has shown that student run medical clinics may provide less than optimum care to marginalized patients. Transient staff, lack of continuity of care, and limited budgets are some challenges. The MUN MED Gateway Project is markedly different. It connects patients with the mainstream medical system. In a context of family physician shortage, this student-run clinic project provides access to medical care for newly arrived refugees in a way that is effective, efficient, and sustainable.
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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 | 0.028 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".