Identifying Potential Areas of Change to Medical School Curricula to Better Support Canadian Rural Healthcare Physicians in Serving Indigenous Populations
Bibliographic record
Abstract
Indigenous peoples in Canada experience a comparatively poorer level of health relative to other Canadians. Factors contributing to this include limited healthcare accessibility and other disparities including anti-Indigenous racism. This project is a thematic analysis which draws on the experiences of rural physicians currently working with Indigenous populations to suggest changes that can be made to medical school curricula. The authors collected response data from physicians via convenience sampling at a 3-day rural and remote medicine conference, inquiring into the participants’ training, practice, and experiences with providing healthcare to Indigenous patients. The study included data from 29 participants across Canada who have practiced medicine in a rural setting (population <50 000) for at least 5 years. The emerging themes were grouped into four categories: systemic barriers to healthcare access (physical distance, impact of colonialism), physicians’ personal barriers in providing care (lack of cultural awareness, time and resources), physician support (Indigenous community involvement, pay model, access to training), and suggestions for how to support future rural physicians (curricular changes, Indigenous representation, exposure to Indigenous peoples and communities during training). Based on these results, we propose to curriculum developers the importance of increasing exposure to rural and Indigenous communities during medical school and increasing the supports available to medical school students interested in rural and Indigenous healthcare.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".