Leading Change from Within: Student-Led Reforms to Advance Anti-Racism within Medical Education
Bibliographic record
Abstract
Racism, physician biases against Indigenous, Black, and racialized people, and the resultant poor health outcomes have been the subject of many institutional position statements and calls to action. Across Canada, undergraduate medical education programs have recognized the importance of addressing racism, but material changes to curriculum and learning environments to incorporate anti-racist lenses have yet to be actualized. To bridge a gap seen within the curriculum, the authors of this manuscript led the co-development, organization, and implementation of a student-led anti-racism initiative at the University of Calgary's Cumming School of Medicine. The initiative consisted of a class-wide anti-racism training session and a strategic review of student governance policies, including elections and decision-making processes through an anti-racist lens to advance equity within student learning environments. Anti-racism praxis was embedded within the co-creation of the anti-racism training by incorporating cultural safety and ethical engagement principles along with paid consultations with racialized students and faculty to identify pertinent topics and inform training priorities. Through this initiative, the authors offer an approach for the larger medical community to consider in their own local efforts to advance anti-racism advocacy and curricular change. This initiative highlighted the unique role of students in disrupting the status quo and modeling an anti-racist lens in their actions and self-governance.
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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 | 0.034 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".