The Intersection of Indigenous Peoples in Canada and Medical Assistance in Dying: A Scoping Review of the Current Literature
Bibliographic record
Abstract
INTRODUCTION: Medical assistance in dying (MAID) was legalized in Canada in 2016, creating a significant shift in end-of-life care. However, Indigenous populations face unique challenges with MAID due to historical mistreatment, cultural differences, and systemic barriers within the health care system. OBJECTIVES: By means of this scoping review, we identify the critical gap in understanding Indigenous experiences with MAID. We also seek to identify barriers and facilitators to providing culturally appropriate care and to incorporate Indigenous perspectives into MAID policies and practices. RESULTS: We identified key themes, including the need for cultural sensitivity and safety, the importance of community and family involvement, communication barriers, and policy and legislative considerations. FUTURE DIRECTIONS: From our findings, we highlight the necessity of engaging Indigenous communities in developing MAID services to ensure they are culturally appropriate and respectful. Addressing these needs is essential to providing respectful end-of-life care for Indigenous peoples in Canada. We also highlight significant gaps in the literature and the urgent need for further research to ensure that MAID services are culturally appropriate and aligned with Indigenous values and needs. DEFINITION: In this article, the term "Indigenous peoples" refers to the original peoples of North America and their descendants. The Canadian Constitution recognizes three distinct groups of Indigenous peoples: First Nations, Inuit, and Métis.
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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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.019 | 0.033 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".