Decolonizing Health Care: Reconciliation Roles and Responsibilities for White Settlers
Bibliographic record
Abstract
The purpose of this paper is to enhance a nascent discussion to white settlers about how they can be active participants in reconciliation action to decolonize health care—by way of truths. I start with an examination of settler denial and settler truth-telling about Indigenous genocide, along with the deadliness of white settler health care racism, which results in embodied oppression—oppression that is the root of Indigenous inequities in the social determinants of health (SDH). White settler privilege is emphasized, including persistent impacts of Western, Eurocentric, and biomedical knowledge dominance in health care, and related suppression of Indigenous knowledge systems and healing traditions. I analyze how white settlers can engage in performing decolonization with critical perspectives on the SDH, allyship, and anti-racist, anti-oppressive health care. Although persistent white settler acts of racism, including systemic racism in health, legal, and educational systems, make reconciliation seem an impossible goal, we continue to be ethically bound to walk alongside Indigenous peoples in the Truth and Reconciliation’s Commission’s Calls to Action.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.031 | 0.043 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.006 | 0.012 |
| 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".