‘Race’ matters: racialization and egalitarian discourses involving Aboriginal people in the Canadian health care context
Bibliographic record
Abstract
The major purpose of this paper is to examine how 'race' and racialization operate in health care. To do so, we draw upon data from an ethnographic study that examines the complex issues surrounding health care access for Aboriginal people in an urban center in Canada. In our analysis, we strategically locate our critical examination of racialization in the 'tension of difference' between two emerging themes, namely the health care rhetoric of 'treating everyone the same,' and the perception among many Aboriginal patients that they were 'being treated differently' by health care providers because of their identity as Aboriginal people, and because of their low socio-economic status. Contrary to the prevailing discourse of egalitarianism that paints health care and other major institutions as discrimination-free, we argue that 'race' matters in health care as it intersects with other social categories including class, substance use, and history to organize inequitable access to health and health care for marginalized populations. Specifically, we illustrate how the ideological process of racialization can shape the ways that health care providers 'read' and interact with Aboriginal patients, and how some Aboriginal patients avoid seeking health care based on their expectation of being treated differently. We conclude by urging those of us in positions of influence in health care, including doctors and nurses, to critically reflect upon our own positionality and how we might be complicit in perpetuating social inequities by avoiding a critical discussion of racialization.
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.090 | 0.063 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.009 |
| 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".