Decolonizing health care: Challenges of cultural and epistemic pluralism in medical decision‐making with Indigenous communities
Bibliographic record
Abstract
The Truth and Reconciliation Commission of Canada made it clear that understanding the historical, social, cultural, and political landscape that shapes the relationships between Indigenous peoples and social institutions, including the health care system, is crucial to achieving social justice. How to translate this recognition into more equitable health policy and practice remains a challenge. In particular, there is limited understanding of ways to respond to situations in which conventional practices mandated by the state and regulated by its legal apparatus come into direct conflict with the values and autonomy of Indigenous individuals, communities, and nations. In this paper, we consider two cases of conflict between Indigenous and biomedical perspectives to clarify some of the competing values. We argue for the importance of person- and people-centered approaches to health care. These value conflicts must be understood at multiple levels to clarify their personal, social, cultural, and political dimensions. Taking into account the divergence between epistemic cultures and communities allows us to understand the multiple narratives deployed in decision-making processes in clinical, community, and juridical contexts. Recognizing the knowledge claims of Indigenous peoples in health care can help clinicians avoid reinforcing the divides created by the structural and institutional legacy of colonialism. This analysis also provides ways to adjudicate conflicts in health care decision-making by disentangling cultural, political, medical, and pragmatic issues to allow for respectful dialogue. Insofar as the engagement with cultural pluralism in health care rights is conducted with reciprocal recognition, the medical community and Indigenous peoples can address together the difficult question of how to integrate different epistemic cultures in the health care system.
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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 | 0.201 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.039 | 0.188 |
| Scholarly communication | 0.030 | 0.021 |
| Open science | 0.006 | 0.039 |
| Research integrity | 0.017 | 0.025 |
| 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".