Classism and Everyday Racism as Experienced by Racialized Health Care Users: A Concept Mapping Study
Bibliographic record
Abstract
In Toronto, Canada, 51.5 % of the population are members of racialized groups. Systemic labor market racism has resulted in an overrepresentation of racialized groups in low-income and precarious jobs, a racialization of poverty, and poor health. Yet, the health care system is structured around a model of service delivery and policies that fail to consider unequal power social relations or racism. This study examines how racialized health care users experience classism and everyday racism in the health care setting and whether these experiences differ within stratifications such as social class, gender, and immigration status. A concept mapping design was used to identify mechanisms of classism and everyday racism. For the rating activity, 41 participants identified as racialized health care users. The data analysis was completed using concept systems software. Racialized health care users reported "race"/ethnic-based discrimination as moderate to high and socioeconomic position-/social class-based discrimination as moderate in importance for the challenges experienced when receiving health care; differences within stratifications were also identified. To improve access to services and quality of care, antiracist policies that focus on unequal power social relations and a broader systems thinking are needed to address institutional racism within the health care system.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".