“This shouldn’t be our job to help you do this”: exploring the responses of medical schools across Canada to address anti-Black racism in 2020
Bibliographic record
Abstract
BACKGROUND: Protests against police brutality and anti-Black racism were catalyzed by the murder of George Floyd and other Black and racialized people in spring 2020. Addressing anti-Black racism had been historically minimized as an institutional priority across Canadian medical schools, but many swiftly released statements broadly condemning racism. Given that little has been documented about how institutions are responding with action, we sought to explore Black medical students' and senior faculty perspectives on Canadian medical schools' efforts to address anti-Black racism in 2020. METHODS: We conducted a qualitative, instrumental case study, grounded in critical race theory. We recruited Black medical students and deans (or delegated senior faculty administrators) and we conducted virtual, individual, semi-structured interviews with participants between Oct. 5, 2020, and Jan. 16, 2021. Interviews were transcribed and iteratively analyzed through inductive and deductive techniques. RESULTS: We interviewed 19 participants, including 8 medical students (6 in pre-clerkship; all of whom identified as Black) and 11 senior faculty administrators (4 deans, 7 delegate faculty administrators; 3 racialized). We had at least 1 student or faculty participant from 13 medical schools, and no student or faculty participants from the 4 medical schools in Quebec. Nearly all represented medical schools were described as "starting from scratch" in their responses, having previously failed to acknowledge or address anti-Black racism. In the absence of diverse faculty leaders, participants indicated that medical schools primarily relied on Black medical students to drive institutional responses, unfairly burdening students during an already difficult period. At the time of the interviews, a range of initiatives were in the planning stages or underway, and were largely related to admissions and curriculum reform. INTERPRETATION: We found that medical schools relied heavily on Black medical students to inform and drive their institutional responses related to anti-Black racism in 2020, which these students found burdensome. Medical schools lacked intrinsic capacity because of the paucity of Black faculty - a direct result of historical and ongoing structural anti-Black racism in medicine. Institutional accountability remains critical, and further research is needed to show the extent to which medical schools in Canada are successfully addressing anti-Black racism.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.038 | 0.018 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".