Anti-Black racism in Canadian health care: a qualitative study of diverse perceptions of racism and racial discrimination among Black adults in Montreal, Quebec
Bibliographic record
Abstract
BACKGROUND: Racism has been shown to impact the health of Black persons through its influence on health care, including its expression through implicit biases in provider training, attitudes, and behaviours. Less is known about the experiences of racism in contexts outside of the USA, and how race and racism interact with other social locations and systems of discrimination to shape Black patients' experiences of racism in health care encounters. To help address this gap, this study examined diverse Black individuals' perceived experiences of, and attitudes towards, anti-Black racism and racial discrimination in Canadian health care, specifically in Montreal, Quebec. METHODS: This descriptive qualitative study adopted a social constructionist approach. Employing purposive maximal variation and snowball sampling strategies, eligible study participants were: self-identified Black persons aged 18 years and older who lived in Montreal during the COVID-19 pandemic, who could speak English or French, and who were registered with the Quebec medical insurance program. In-depth interviews were conducted, and a Framework Analysis approach guided the systematic exploration and interpretation of data using an intersectionality lens. RESULTS: We interviewed 32 participants, the majority of whom were women (59%), university educated (69%), and modestly comfortable financially (41%), but diverse in terms of age (22 to 79 years), country of origin, and self-defined ethnicity. We identified five major themes demonstrating substantial variations in perceived racism in health care that are influenced by unique social locations such as gender identity, age, and immigration history: (1) no perceptions of racism in health care, (2) ambiguous perceptions of racism in health care, (3) perceptions of overt interpersonal racism in health care, (4) perceptions of covert interpersonal racism in health care (including the downplaying of health concerns, stereotyping, and racial microaggressions), and (5) perceptions of systemic racism in health care. CONCLUSIONS: Perceptions of anti-Black racism and racial discrimination in Canadian health care are complex and may include intra-racial group differences. This study begins to address the dearth of empirical research documenting experiences of anti-Black racism in health care in Quebec, highlighting a continued need for serious consideration of the ways in which racism may manifest in the province, as well as a need for anti-racist advocacy. Advancing racial health equity requires greater sensitivity from providers and decision makers to variations in Black patients' health care experiences, towards ensuring that they have access to high quality and equitable health care services.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".