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Record W4414228188 · doi:10.4337/9781802204353.00047

Indigenous perspectives on regulating assisted dying: views from Canada and Australia

2025· book-chapter· en· W4414228188 on OpenAlexaboutno aff
Constance MacIntosh

Bibliographic record

VenueEdward Elgar Publishing eBooks · 2025
Typebook-chapter
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousLegislaturePublic healthTraditional knowledgeCultural safetyPublic policy

Abstract

fetched live from OpenAlex

Canada and Western Australia are both jurisdictions that have legalized medically assisted dying. They also each have sizable Indigenous populations. This chapter surveys the public record formed in these countries during the deliberative processes to enact the legislative regimes, focusing on the comments and submissions by Indigenous participants and representative organizations. In each country, this record forms the bulk of publicly available information about the views and perspectives of the Indigenous communities, as there is a lack of research on this matter. The public record reveals commonalities and differences, both between Indigenous peoples in Canada versus Australia, and also between Indigenous individuals in each state. Important commonalities include discussions about the cultural appropriateness of assisted dying, concerns about the impacts of health inequities, and calls for significant Indigenous involvement, on an organizational or self-governance level. In-depth and ongoing consultation with Indigenous communities and representative organizations is also a priority.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.103
Threshold uncertainty score0.750

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0370.022
Scholarly communication0.0100.004
Open science0.0020.005
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.128
GPT teacher head0.351
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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