Black Canadians’ Experiences of Structural Racism in Ontario’s Healthcare System
Bibliographic record
Abstract
Background: Despite Canada’s universal healthcare system, Black Canadians continue to experience inequitable access to services. While much of the literature has examined urban centres such as Toronto and Montreal, little is known about experiences in mid-sized cities where healthcare systems are less resourced and culturally diverse. This study explores how structural racism and intersecting barriers shape healthcare access for Black Canadians in Kingston and London, Ontario. Methods: We conducted a qualitative secondary analysis of 25 semi-structured interviews with Black parents and caregivers (2023–2024) from a mixed-methods study on early learning and childcare. Although the parent study focused on childcare, participants frequently described healthcare challenges, which prompted a re-analysis of these narratives. Using a hybrid inductive and deductive thematic analysis, transcripts were coded through the lens of Critical Race Theory (CRT) and Structural Violence Theory (SVT), emphasizing both systemic inequities and community-based strategies of resilience. Results: Five overarching themes emerged: (1) Institutional Racism and Cultural Incompetence, where participants reported dismissal, disbelief, and lack of culturally responsive care; (2) Structural Violence and Delayed Access, where immigration-linked exclusions, long wait times, and economic barriers limited access; (3) Intersectionality, where race, gender, immigration, and socioeconomic status compounded disadvantage, with Black women describing gendered racism in maternal care; (4) Informational and Communication Barriers, where newcomers relied on informal networks and Google due to weak institutional guidance; and (5) Community-Based Resilience, where churches, diaspora networks, and midwifery care provided culturally safe alternatives to formal systems. Conclusion: Findings demonstrate that healthcare inequities for Black Canadians in smaller cities are not incidental but structurally embedded. CRT and SVT reveal how systemic racism and bureaucratic inaction reproduce harm while communities develop parallel infrastructures of care. Addressing these inequities requires race-conscious health policies, investment in culturally safe services outside metropolitan centres, and intentional inclusion of Black voices in healthcare planning.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.052 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".