Experiences of racism of Black medical students and residents in Montréal: “I wear my stethoscope around my neck at all times”
Bibliographic record
Abstract
Background: Black students and residents experience racism in medical school. This qualitative study documents Black students' and residents' experiences of racism using Critical Race Theory (CRT) and explores their coping mechanisms using the theatrical metaphor. Methods: We conducted semi-structured interviews with four Black medical students and residents (two medical students and two residents) studying in Montréal and analyzed their experiences through counter-stories. We identified themes related to their experiences of racism during medical training and their coping mechanisms. Results: Our analysis reveals these experiences of racism occur in academic and clinical settings (classes, internships, social interactions with peers, faculty, and patients, and through the curriculum), in the form of microaggressions. The analysis also indicates that Black students and residents try to cope with racism using a hyper-ritualization strategy to better fit in (e.g., clothing, behaviours). Conclusion: Considering that Black students and residents experience various forms of racism (subtle or explicit) during their medical training, these findings urge us to increase awareness about racism of students, residents, teachers and health care workers in universities and teaching hospitals. Pathways to increase the representation of Black students and residents seem to be part of the solution, but improving the learning environment must be a priority to achieve racial justice in medical training in Québec.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".