Anti-Black discrimination in primary health care: a qualitative study exploring internalized racism in a Canadian context
Bibliographic record
Abstract
OBJECTIVES: A growing body of evidence points to persistent health inequities within racialized minority communities, and the effects of racial discrimination on health outcomes and health care experiences. While much work has considered how anti-Black racism operates at the interpersonal and institutional levels, limited attention has focused on internalized racism and its consequences for health care. This study explores patients' attitudes towards anti-Black racism in a Canadian health care system, with a particular focus on internalized racism in primary health care. DESIGN: This qualitative study employed purposive maximal variation and snowball sampling to recruit and interview self-identified Black persons aged 18 years and older who: (1) lived in Montréal during the COVID-19 pandemic, (2) could speak English or French, and (3) were registered with the Québec health insurance program. Adopting a phenomenological approach, in-depth interviews took place from October 2021 to July 2022. Following transcription, data were analyzed thematically. RESULTS: Thirty-two participants were interviewed spanning an age range from 22 years to 79 years (mean: 42 years). Fifty-nine percent of the sample identified as women, 38% identified as men, and 3% identified as non-binary. Diversity was also reflected in terms of immigration experience, financial situation, and educational attainment. We identified three major themes that describe mechanisms through which internalized racism may manifest in health care to impact experiences: (1) the internalization of anti-Black racism by Black providers and patients, (2) the expression of anti-Black prejudice and discrimination by non-Black racialized minority providers, and (3) an insensitivity towards racial discrimination. CONCLUSION: Our study suggests that multiple levels of racism, including internalized racism, must be addressed in efforts to promote health and health care equity among racialized minority groups, and particularly within Black communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".