Combating Physician-Assisted Genocide and White Supremacy in Healthcare through Anti-Oppressive Pedagogies in Canadian Medical Schools to Prevent the Coercive and Forced Sterilization of Indigenous Women
Bibliographic record
Abstract
Coercive and forced sterilization of Indigenous Peoples are acts of genocide that are rooted in colonialism and white supremacy and require fundamental changes to undergraduate medical education. I (Erika Campbell) draw upon the Truth and Reconciliation Commission of Canada’s 24th Call to Action, which calls for “skills-based training in intercultural competency, conflict resolution, human rights, and anti-racism” in medical schools. Additionally, I draw upon Call for Justice 7.6 from the Reclaiming Power and Place: The Final Report of the National Inquiry into Missing and Murdered Indigenous Women and Girl, which calls upon institutions and health service providers be educated in areas including, but not limited to: the history of colonialism in the oppression and genocide of Inuit, Métis, and First Nations Peoples; anti-bias and anti-racism; local language and culture; and local health and healing practice. I analyzed the responses of all 17 undergraduate medical programs in Canada to determine how they incorporated anti-racism within their medical education to meet the Calls to Action and Justice. All undergraduate medical programs include some form of cultural learning, which I argue does not directly challenge racism and colonialism. As such, I advocate for the implementation of anti-oppressive pedagogies within curricula to facilitate the unlearning of colonial rhetoric. I further argue the implementation of anti-oppressive pedagogies within education will contribute to the eradication of the ongoing genocide of Indigenous Peoples and white supremacy within our healthcare systems.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.015 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".