Scoping review of current challenges and circumstances impacting Indigenous applications to Canadian medical schools
Bibliographic record
Abstract
Introduction: Considering the relevant 2015 Truth and Reconciliation Commission recommendations, this paper reviews the current state of Canadian medical schools' Indigenous admissions processes and explores continued barriers faced by Indigenous applicants. Methods: A summary of literature illustrating disadvantages for Indigenous applicants of current admissions tools is presented. A grey literature search of current admissions requirements, interview processes, and other relevant data from each medical school was performed. Tables comparing differences in their approaches are included. A calculation of Indigenous access to medical school seats compared to the broader Canadian population was conducted. Gaps in execution are explored, culminating in a table of recommendations. Results: Despite formal commitments to reduce barriers, Indigenous applicants to medical school in Canada still face barriers that non-Indigenous applicants do not. Most programs use tools for admission known to disadvantage Indigenous applicants. Indigenous applicants do not have equitable access to medical school seats. Facilitated Indigenous stream processes first ensure Indigenous applicants meet all minimum requirements of Canadian students, and then require further work. Discussion: Seven years after the Truth and Reconciliation Commission called on Canadian universities and governments to train more Indigenous health care providers, there has been limited progress to reduce the structural disadvantages Indigenous students face when applying to medical school. Based on best practices observed in Canada and coupled with relevant Indigenous-focused literature, recommendations are made for multiple stakeholders. Conclusions: The study was limited by the data available on numbers of Indigenous applicants and matriculants. Where available, data are not encouraging as to equitable access to medical school for Indigenous populations in Canada. These findings were presented at the International Congress of Academic Medicine 2023 Conference, April 2023, Quebec City, Canada.
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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 | 0.045 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.028 | 0.042 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| 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".