Patients, Pride, and Prejudice: Exploring Black Ontarian Physicians’ Experiences of Racism and Discrimination
Bibliographic record
Abstract
PURPOSE: Black physicians' and trainees' experiences of racism are not well documented in Canada, reflecting a knowledge gap needing correction to combat racism in Canadian health care. The authors undertook a descriptive study of Black physicians and trainees in the Canadian province of Ontario. The goal of this study was to report upon racism experienced by participant Ontarian physicians to challenge the purported rarity of racism in Canadian health care. METHOD: An anonymous online survey of physicians and trainees who self-identify as Black (African/Afro-Canadian/African American/Afro-Caribbean) was administered in March and April 2018 through the Black Physicians' Association of Ontario (BPAO) listserv. The survey was modeled on qualitative interview guides from American studies. Snowball sampling was employed whereby BPAO members forwarded the survey to eligible colleagues (non-BPAO members) to maximize responses. Survey data were analyzed and key themes described. RESULTS: Survey participants totalled 46, with a maximal response rate of 38%. Participants reported positive experiences of collegiality with Black colleagues and strong bonds with Black patients. Negative discrimination experiences included differential treatment and racism from peers, superiors, and patients. Participants reported race as a major factor in their selection of practice location, more so than selection of career. Participants also expressed a lack of mentorship, and there was a strong call for increased mentorship from mentors with similar ethno-racial backgrounds. CONCLUSIONS: This study challenges the notion that racism within Canadian health care is rare. Future systematic collection of information regarding Black physicians' and trainees' experiences of racism will be key in appreciating the prevalence and nature of these experiences.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".