Experiences of everyday racism in Toronto’s health care system: a concept mapping study
Bibliographic record
Abstract
BACKGROUND: In Canada, there is longstanding evidence of health inequities for racialized groups. The purpose of this study is to understand the effect of current health care policies and practices on racial/ethnic groups and in particular racialized groups at the level of the individual in Toronto's health care system. METHODS: This study used a semi-qualitative study design: concept mapping. A purposive sampling strategy was used to recruit participants. Health care users and health care providers from Toronto and the Greater Toronto Area participated in all four concept mapping activities. The sample sizes varied according to the activity. For the rating activity, 41 racialized health care users, 23 non-racialized health care users and 11 health care providers completed this activity. The data analysis was completed using the concept systems software. RESULTS: Participants generated 35 unique statements of ways in which patients feel disrespect or mistreatment when receiving health care. These statements were grouped into five clusters: 'Racial/ethnic and class discrimination', 'Dehumanizing the patient', 'Negligent communication', 'Professional misconduct', and 'Unequal access to health and health services'. Two distinct conceptual regions were identified: 'Viewed as inferior' and 'Unequal medical access'. From the rating activity, racialized health care users reported 'race'/ethnic based discrimination or everyday racism as largely contributing to the challenges experienced when receiving health care; statements rated high for action/change include 'when the health care provider does not complete a proper assessment', 'when the patient's symptoms are ignored or not taken seriously', 'and 'when the health care provider belittles or talks down to the patient'. CONCLUSIONS: Our study identifies how racialized health care users experience everyday racism when receiving health care and this is important to consider in the development of future research and interventions aimed at addressing institutional racism in the health care setting. To support the elimination of institutional racism, anti-racist policies are needed to move beyond cultural competence polices and towards addressing the centrality of unequal power social relations and everyday racism in the health care system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".