Systemic Racism in Canadian Healthcare: An Empirical Policy Analysis of Racial Disparities and Institutional Barriers
Bibliographic record
Abstract
This project supports a qualitative policy analysis exploring systemic racism in Canadian healthcare. Drawing on public inquiries, government reports, legal documents, peer-reviewed literature, and media investigations, the study identifies and analyzes structural barriers that contribute to racial health disparities in Canada. The purpose of the research is to move beyond anecdotal reports and toward a structured, evidence-informed understanding of how institutional racism affects both patient outcomes and healthcare workforce participation—particularly for Indigenous, Black, and racialized communities, as well as internationally trained professionals. The analysis is organized around four central themes: Bias in Patient Care Workforce Discrimination Credentialing Barriers for Internationally Trained Practitioners (ITPs) Institutional Accountability and Oversight Expected outcomes include actionable policy recommendations focused on: Embedding anti-racism principles into legislation and regulation. Reforming licensing and credentialing systems. Establishing equity-focused accountability frameworks. Expanding the use of disaggregated race-based health data. This OSF project hosts the data used in the study, including: A thematic coding matrix. Workforce statistics from CIHI and other public sources. Case study summaries. The Excel dataset underlying all tables and results. This work contributes to ongoing efforts to address health inequities and offers a reproducible model for health systems research on institutional racism in high-income countries.
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 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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.022 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".