Developing an Anti-Racism Tool Kit for Medical Education: A Pre-Clerkship Curriculum Audit
Bibliographic record
Abstract
Background Racialized health inequities are poorly addressed in medical education, often presenting race without social context and perpetuating racialized biases. Reframing the understanding of race as a social construct and incorporating anti-racism education into medical curricula are essential to mitigate health inequities. Our study describes an anti-racism curriculum audit conducted for the University of Ottawa Undergraduate Medical Education pre-clerkship program. This audit informed the development of an anti-racism toolkit designed to serve as a systematic guide for medical schools undertaking curriculum reforms. Methods A comprehensive anti-racism curriculum audit was conducted on pre-clerkship curriculum content from May 2020 to August 2021. Content flagged for concern was categorized into 4 themes: insufficient representation of racialized populations, race-based generalizations, cultural insensitivities, and reinforcement of stereotypes. Results The curriculum audit evaluated 772 course modules, completed by 18 medical students. A total of 224 (31.6%) modules contained one or more racial biases. The most prevalent concern was insufficient representation of racialized populations, identified in 145 flagged comments (55.1%). Curriculum content also perpetuated race-based generalizations ( n = 75 flagged comments, 28.5%), racial stereotypes ( n = 23 flagged comments, 8.8%), and cultural insensitivities ( n = 20 flagged comments, 7.6%). Conclusions This anti-racism curriculum audit revealed a lack of diverse representation, alongside the persistence of race-based generalizations, stereotypes, and cultural insensitivities in a large proportion of the pre-clerkship curriculum. An anti-racist lens and curriculum are necessary to reduce bias in medical education and empower medical students to provide equitable care to the diverse Canadian patient population.
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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 | 0.022 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".