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Record W4406029029 · doi:10.1177/09697330241305556

The moral web of accessibility to medical assistance in dying: Reflections from the Canadian context

2025· article· en· W4406029029 on OpenAlexafffundabout
Barbara Pesut, Sally Thorne

Bibliographic record

VenueNursing Ethics · 2025
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsContext (archaeology)LegalizationLegislationReferralRelation (database)Public relationsHealth carePsychologySociologyPolitical scienceNursingInternet privacyBusinessMedicineLawComputer scienceGeography

Abstract

fetched live from OpenAlex

In this paper, we reflect on factors that seem to have influenced the accessibility of medical assistance in dying (MAID) in the Canadian context. Since legalization in 2016, the uptake of MAID has increased rapidly to equal or exceed rates in other countries. In that MAID implementation involves numerous ethical/moral complexities, we consider four factors that appear to have influenced this growth. First, we reflect on the vague language contained within the legislation that has been interpreted by a community of practice in which making MAID accessible is an important priority. Second, we consider policies of effective referral and self-referral that have been strategies for enhancing accessibility in relation to a wider context that contains conscientious objection. Third, we examine the apparent impact of centralized clinical teams and coordination services that have enhanced accessibility for persons residing in rural and remote areas. Fourth, we reflect on ways in which public awareness of MAID has been enhanced through policies that enable healthcare providers to introduce the topic of MAID as an option within advance care planning. We conclude with a consideration of how these intersecting factors may be shaping the moral complexity inherent in the idea of making MAID accessible.

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)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.753
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.432
GPT teacher head0.559
Teacher spread0.127 · 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.

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

Citations3
Published2025
Admission routes3
Has abstractyes

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