Systemic Racism in Canadian Healthcare: Narrative Review and Policy Analysis of Racial Disparities and Institutional Barriers
Bibliographic record
Abstract
Background: Systemic racism in Canadian healthcare is deep-rooted, generating inequities in workforce diversity and patient care. Black, racialized, and Indigenous communities encounter heightened barriers to accessing medical care and career advancement due to institutionally rooted biases. Despite Canada’s single-payer, universally accessible care, studies have documented widespread inequities in access, care, and health outcomes. The exclusion of foreign-trained healthcare professionals who benefited from the Canadian Immigration Point-Based Comprehensive Ranking System (CRS) from the labor force further entrenches inequities, mirroring systemic biases [14]. Addressing these issues is crucial for ensuring equitable healthcare delivery. Objective: This narrative review critically assesses systemic racism in Canadian healthcare, with consideration for racial inequality in patient care, career barriers for racialized healthcare professionals, and institution policies with a discriminatory intention. It identifies the structural barriers that preserve inequity and proposes policy-guided recommendations for systemic reform. Methods: This narrative review synthesizes empirical research, government reports, and case studies to examine systemic racism in Canadian healthcare. Sources were selected based on relevance, credibility, and publication within the last 15 years. Inclusion criteria focused on studies examining racial disparities in healthcare access, professional barriers, and policy interventions. Case studies were chosen based on their legal and policy significance, particularly those highlighting systemic failures leading to patient harm. Thematic analysis was used to categorize key issues, ensuring a comprehensive policy-driven discussion. Results: The review identifies three primary systemic barriers: 1. Racial biases in patient care lead to delayed treatment, misdiagnoses, and higher mortality rates among Black and Indigenous patients. 2. Institutional racism in healthcare workforce structures restricts opportunities for racialized healthcare professionals, limiting diversity in medical leadership. 3. Credentialing barriers disproportionately affect internationally trained physicians (ITPs), preventing them from contributing to Canada’s overburdened healthcare system. Case studies highlight the severe consequences of healthcare discrimination. Brian Sinclair, an Indigenous man, died after being ignored for 34 hours in a Winnipeg ER. Joyce Echaquan, an Atikamekw woman, live-streamed racist abuse from nurses before her death. These cases underscore the urgent need for systemic policy reforms to prevent further medical neglect. Conclusion: Several evidence-based policy interventions are necessary to dismantle racism in Canadian healthcare. Some of these interventions include mandatory anti-racism and cultural competency training for Healthcare professionals, the collection of race-based health data to track disparities and inform policies, and fair credentialing processes for international medical school graduates to address workforce shortages. Independent accountability and review processes must also be established to prevent medical abuse. By taking such actions, a fairer, accessible, and effective system will ensure that racialized communities receive the care they deserve. [M1]References should be numbered in order of appearance. Please rearrange all the references to appear in numerical order.
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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 | 0.022 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.018 | 0.024 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".