Epistemic racism in the health professions: A qualitative study with Black women in Canada
Bibliographic record
Abstract
Systemic racism within health care is increasingly garnering critical attention, but to date attention to the racism experienced by health professionals themselves has been scant. In Canada, anti-Black racism may be embodied in structures, policies, institutional practices and interpersonal interactions. Epistemic racism is an aspect of systemic racism wherein the knowledge claims, ways of knowing and 'knowers' themselves are constructed as invalid, or less credible. This critical interpretive qualitative study examined the experiences of epistemic racism among 13 healthcare professionals across Canada who self-identified as Black women. It explores the ways knowledge claims and expert authority are discredited and undermined, despite the attainment of professional credentials. Three themes were identified: 1. Not being perceived or portrayed as credible health professionals; 2. Requiring invisible labour to counter professional credibility 'deficit'; and 3. Devaluing knowledge while imposing stereotypes. The Black women in our study faced routine epistemic racism. They were not afforded the position of legitimate knower, expert, authority, despite their professional credentials as physicians, nurses and occupational therapists. Their embodied cultural and community knowledges were disregarded in favour of stereotyped assumptions. Adopting the professional comportment of 'Whiteness' was one way these health care providers strived to be perceived as credible professionals. Their experiences are characteristic of 'misogynoir', a particular form of racism directed at Black women. Anti-Black epistemic racism constitutes one way Whiteness is perpetuated in health professions institutions.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.045 | 0.021 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".