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Record W3019469656 · doi:10.7202/1068553ar

MEDICAL ASSISTANCE IN DYING (MAID)

2020· article· en· W3019469656 on OpenAlexvenueaboutno aff
Alona Amurao

Bibliographic record

VenueCanadian social work review · 2020
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsnot available
Fundersnot available
KeywordsDignityAutonomyRight to dieNursingPalliative careInjusticeAssisted suicideInclusion (mineral)PsychologySocial workConfusionMedicinePolitical sciencePsychiatryLawSocial psychology

Abstract

fetched live from OpenAlex

Medical Assistance in Dying (MAID) is a legal federal framework for medical practitioners to assist in the cessation of life upon request from eligible patients who seek assisted death in order to die peacefully and with dignity. MAID’s ‘mentally competent’ eligibility criteria currently create confusion for social workers because they provide little guidance on how to best implement the desired practices intended to support the aims of MAID. Secondly, current criteria pose challenges for vulnerable populations, particularly patients with amyotrophic lateral sclerosis (ALS). ALS patients who are deemed mentally incapable are denied access to MAID, suffering in pain every day until they die. Canada’s MAID policy infringes on their autonomy, and removes their choice to die with dignity. This injustice calls for further reconsideration of the ways MAID can be reformed to serve dying Canadians who are falling through the cracks of MAID. Policy recommendations include inclusion of advanced directives and substitute decision makers. Due to this unequal access in health care services, this concern constitutes a social work issue. Recommendations for social work include increasing competency, and advocacy regarding the provision of MAID.

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)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.456
Threshold uncertainty score0.917

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0030.003
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.002

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 head0.166
GPT teacher head0.420
Teacher spread0.254 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations4
Published2020
Admission routes2
Has abstractyes

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