Examining Social Exclusion by ‘Race’/Ethnicity in Toronto’s Health Care System: A Scientific Realist-inspired Concept Mapping Study
Bibliographic record
Abstract
With widening health inequities, developing an effective policy of action requires a better understanding of the mechanisms through which health inequities are distributed. The purpose of this doctoral research is to understand the effect of current hospital policies and practices on racial/ethnic groups at the level of the individual in Toronto’s health care system. The overall goal is to provide the explanatory power necessary to change policies and practices. Using concept mapping, a semi-qualitative study design, participants identified experiences of social exclusion when receiving health care in Toronto’s health care system. A political economy theory with a scientific realist philosophy of science was used to identify social causal mechanisms and explain how and why everyday racism occurs in Toronto’s health care setting. From the brainstorming activity, participants generated 35 unique statements of ways in which patients feel disrespect and mistreatment when receiving health care. From the sorting and mapping activity, 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 of social exclusion were identified: ‘Viewed as inferior’ and ‘Unequal medical access’. Clusters rated highly for ‘race’/ethnic based discrimination were ‘Racial/ethnic and class discrimination’ and ‘Dehumanizing the patient’. Racialized health care users reported ‘race’/ethnic based discrimination or everyday racism as largely contributing to the challenges experienced when receiving health care. From the go-zone data, statements rated high for ‘race’/ethnic based discrimination and taking action/change were ‘when the patient's symptoms are ignored or not taken seriously’, ‘when the health care provider does not provide the requested information’, and ‘when the health care provider belittles or talks down to the patient’. To eliminate institutional racism, political will is needed to eliminate the ideology of racial inferiority often used for the purpose of justifying racist actions/unequal treatment. Anti-racist policies are needed to move beyond cultural competence polices and towards addressing the centrality of unequal power relations and everyday racism in health care. This has implications for nursing leadership, health care provider education, health care organizations, and health care practice.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.019 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".