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Record W2727567052 · doi:10.1016/j.eurpsy.2017.02.289

Medical assistance in dying: The Canadian experience

2017· article· en· W2727567052 on OpenAlexaffabout
K. Sonu Gaind

Bibliographic record

VenueEuropean Psychiatry · 2017
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsHumber River Regional Hospital
Fundersnot available
KeywordsCharterDeclarationLegislationSupreme courtMental illnessPolitical scienceTask forceAssisted suicideMental healthLawTask (project management)MedicinePublic administrationPsychiatry

Abstract

fetched live from OpenAlex

Canada is in the midst of implementing new and rapidly evolving policies on medical assistance in dying (MAID). Following the landmark Canadian Supreme Court Carter v. Canada ruling in February 2015, the former prohibition against physician-assisted death was deemed to violate the Canadian Charter of Rights and Freedoms. The Court provided until 2016 for development of national legislation and policies that allowed for physician-assisted dying in cases of “grievous and irremediable” illness and “intolerable suffering”. This session will review shifting public, societal and medical concepts regarding assisted dying and the Canadian experience to date, including evolving local and national policies that have been developed to allow medical assistance in dying in certain circumstances. We will also review work of the Canadian psychiatric association task force on medical assistance in dying (presented by the Task Force Chair), with a focus on challenges and issues relevant to mental health and mental illness. Disclosure of interest The author has not supplied his declaration of competing interest.

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)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.111
Threshold uncertainty score0.807

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.007
Science and technology studies0.0340.008
Scholarly communication0.0060.002
Open science0.0030.007
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0100.001

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 head0.108
GPT teacher head0.419
Teacher spread0.310 · 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 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
Published2017
Admission routes2
Has abstractyes

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