Barriers to Black Medical Students and Residents Pursuing and Completing Surgical Residency in Canada: A Qualitative Analysis
Bibliographic record
Abstract
BACKGROUND: The limited available data suggest that the Canadian surgical workforce does not reflect the racial diversity of the patient population it serves, despite the well-established benefits of patient-provider race concordance. There have been no studies to date that characterize the systemic and individual challenges faced by Black medical students in matching to and successfully finishing training in a surgical specialty within a Canadian context that can explain this underrepresentation. STUDY DESIGN: Using critical qualitative inquiry and purposive sampling to ensure sex, geographical, and student or trainee year heterogeneity, we recruited self-identifying Black medical students and surgical residents across Canada. Online in-depth semistructured interviews were conducted and transcribed verbatim. Transcripts were analyzed through an inductive reflexive narrative thematic process by 4 analysts. RESULTS: Twenty-seven participants including 18 medical students and 9 residents, were interviewed. The results showed 3 major themes that characterized their experiences: journey to and through medicine, perceptions of the surgical culture, and recommendations to improve the student experience. Medical students identified lack of mentorship and representation as well as experiences with racism as the main barriers to pursuing surgical training. Surgical trainees cited systemic racism, lack of representation, and insufficient safe spaces as the key deterrents to program completion. The intersection with sex exponentially increased these identified barriers. CONCLUSIONS: Except for a few surgical programs, medical schools across Canada do not offer a safe space for Black students and trainees to access and complete surgical training. An urgent change is needed to provide diverse mentorship that is transparent, acknowledges the real challenges related to systemic racism and biases, and is inclusive of different racial and ethnic backgrounds.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.025 | 0.010 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".