Systemic Anti-Blackness in Healthcare: What the COVID-19 Pandemic Revealed about Anti-Black Racism in Canada
Bibliographic record
Abstract
Throughout the COVID-19 pandemic, there have been numerous examples of how systemic racism and racist stereotypes stigmatized those who contracted and transmitted the virus. This systemic racism predates the pandemic, and is itself endemic in healthcare service, delivery and education as evidenced by the treatment of Black students, residents and doctors. While public health officials, healthcare providers and medical schools may claim to be colour-blind, the documented experiences of Black and Indigenous people and people of colour - particularly those who are queer or trans - demonstrate otherwise. In this paper, the author focuses on the experiences that Black people have in healthcare settings and reflects on what has been revealed during the COVID-19 pandemic, including how systemic historical, contemporary and ongoing anti-Black racism continues to negatively impact health outcomes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.036 | 0.024 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.011 |
| 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".