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Record W4375831667 · doi:10.1177/23333936231167309

Medical Assistance in Dying: A Review of Canadian Health Authority Policy Documents

2023· review· en· W4375831667 on OpenAlexafffundabout
Robyn Thomas, Barbara Pesut, Gloria Puurveen, Sally Thorne, Carol Tishelman, Betsy Leimbigler

Bibliographic record

VenueGlobal Qualitative Nursing Research · 2023
Typereview
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersInstitute of AgingCanadian Institutes of Health Research
KeywordsConfidentialityDignityAbandonment (legal)Context (archaeology)ConscienceVulnerability (computing)Public relationsHealth carePolitical sciencePublic administrationLaw

Abstract

fetched live from OpenAlex

The purpose of this study was to describe policies developed by English-speaking Canadian health authorities to guide multi-disciplinary healthcare practice in the context of MAID. Seventeen policies from 9 provinces and 3 territories were identified and analyzed thematically. Themes developed from these documents related to ensuring a team approach to care, supporting informed patient choice, creating region-specific guidance on eligibility criteria and safeguards, accommodating conscientious objection, and making explicit organizational responsibilities. Ethical language concerned vulnerability, non-judgmental care, dignity, non-abandonment, confidentiality, moral conscience, and diverse cultural values. Overall, these policies addressed important risk mitigation strategies, acknowledged important social contracts, and supported ethical practice. Collectively, these policies outline important considerations in the evolving Canadian context for other jurisdictions seeking to create policy around assisted death.

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)0.019
metaresearch head score (Gemma)0.049
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.083
Threshold uncertainty score0.605

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.049
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0230.047
Science and technology studies0.0040.003
Scholarly communication0.0050.002
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.686
GPT teacher head0.729
Teacher spread0.043 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations10
Published2023
Admission routes3
Has abstractyes

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