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Record W4415819534 · doi:10.7202/1121332ar

Assisted Dying in Canada: Ideology Masquerading as Medicine?

2025· article· en· W4415819534 on OpenAlexvenueaboutno aff
Scott Y. H. Kim

Bibliographic record

VenueCanadian Journal of Bioethics · 2025
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsnot available
Fundersnot available
KeywordsIdeologyDemocracyState (computer science)Public healthPreferenceHealth careMedical practiceMedical lawAssisted suicide

Abstract

fetched live from OpenAlex

The Canadian medical assistance in dying (MAID) law is commonly understood as a type of medically-based euthanasia and/or assisted suicide (EAS) law. I argue it is instead an autonomy-only EAS regime masquerading as such. The public guidance on the law’s broad eligibility criteria that direct current practice employs novel meanings to familiar terms used in healthcare. The result is that the law gives the impression that Canadian MAID is about something medical, but in reality operates as an autonomy-only model, i.e., “death on autonomous demand” clothed as medical treatment. The implications are significant. It allows bypassing the debates that an autonomy-only system (which is not inherently medical) would need to address, such as whether autonomy-only EAS comports with common morality, whether and how the health care system should be involved, how many resources the state should invest in promoting a death-on-request system, etc. The law also distorts the practice of medicine by using patients’ subjective preference (i.e., patients’ preference), rather than science and evidence, as determining medical standards and in defining terms such as ‘incurable.’ This masquerade has been reinforced by Canadian officials in their public statements. The result seems to be a Canadian public highly supportive of a law that they do not fully understand and indeed would likely disapprove of in its current form, if they did. The Canadian MAID law is a product of flawed democratic policymaking.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.496
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.206
GPT teacher head0.441
Teacher spread0.235 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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 routes2
Has abstractyes

Explore more

Same venueCanadian Journal of BioethicsSame topicPalliative Care and End-of-Life IssuesFrench-language works237,207