Beyond the reckoning: Addressing structural anti-Black racism in population and public health
Bibliographic record
Abstract
The absence of historical context in public health has contributed to the persistent neglect of structural racism as a major determinant of health inequities. This commentary critically highlights the omissions of the Lalonde Report, a foundational document in population health, and explores the enduring impacts of slavery, colonialism, and systemic racism on Black health in Canada. Anti-Black racism continues to shape racial health inequities through economic, political, and social marginalization. However, public health research, policy, and practice have largely failed to address the structural dimensions of racism. Yet, Black communities in Canada have long resisted these injustices through grassroots movements, community advocacy, and systems transformation. A decolonial, anti-racist approach is necessary to disrupt these patterns, shift power and resources, and promote health equity. This requires interdisciplinary education, policy change, and investments in social systems that promote equity for Black communities. Centring community-led solutions, institutional accountability, and leveraging legislative tools are critical steps toward meaningful change. Public health must actively engage in dismantling white supremacy to achieve equitable health outcomes for Black communities.
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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 | 0.023 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.065 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.012 | 0.028 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".