Systemic Racism in Canadian Healthcare: Narrative Review and Policy Analysis of Racial Disparities and Institutional Barriers
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,022 | 0,058 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,018 | 0,024 |
| Études des sciences et des technologies | 0,007 | 0,004 |
| Communication savante | 0,007 | 0,003 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».