Broken Promises: Racism and Access to Medicines in Canada
Bibliographic record
Abstract
BACKGROUND: Discriminatory policies, attitudes, and practices have had deleterious impacts on the health of Black, Indigenous, and other racialized groups. The aim of this study was to investigate racism as barrier to access to medicines in Canada. The study investigated the characteristics of structural racism and implicit biases that affect medicines access. METHODS: A scoping review using the STARLITE literature retrieval approach and analysis of census tract data in Toronto, Ontario, Canada, were undertaken. Government documents, peer-reviewed articles from public policy, health, pharmacy, social sciences, and gray literature were reviewed. RESULTS: Structural racism that created barriers to access to medicines and vaccines was identified in policy, law, resource allocation, and jurisdictional governance. Institutional barriers included health care providers' implicit biases about racialized groups, immigration status, and language. Pharmacy deserts in racialized communities represented a geographic barrier to access. CONCLUSION: Racism corrupts and impedes equitable allocation and access to medicine in Canada. Redefining racism as a form of corruption would obligate societal institutions to investigate and address racism within the context of the law as opposed to normative policy. Public health policy, health systems, and governance reform would remove identified barriers to medicines, vaccines, and pharmaceutical services by racialized groups.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".