Experienced or witnessed racism and microaggressions during medical education: an exploratory survey of medical learners at a large Canadian medical school
Bibliographic record
Abstract
Background: As institutions strive to incorporate Equity, Diversity, Inclusion, Indigeneity, and Accessibility (EDIIA) principles into their policies and curricula, various forms of discrimination persist within the medical education system. The objective of this study was to understand learner experiences related to racism, discrimination and microaggressions in a large Canadian medical school to ultimately inform future efforts to address issues identified. Methods: This survey-based study was distributed to all current medical students and residents at a large Canadian University. Questions focused on lived and witnessed experiences of microaggressions, discrimination or racism during medical education. We computed descriptive statistics and risk ratios for experienced or witnessed events. Results: The survey response rate was 12.4% (321/2579), with 26% of participants self-identifying as Black, Indigenous or People of Color (BIPOC). During medical education, 30% of respondents reported experiencing racism or microaggressions, while 51% reported witnessing these events. Attending physicians (31%) and patients/families (22%) were most likely to be identified as responsible. Common proposed solutions by respondents included: anonymous reporting systems, dedicated counsellors from BIPOC groups, education of healthcare professionals on microaggressions and discrimination and increased peer/faculty support. Conclusions: Among participants, this study described a high rate of witnessed or experienced racism or microaggressions during medical education, leading to local interventions to improve the psychological safety of learners.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".