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Record W4392398299 · doi:10.1177/23333936241228233

The Evolving Complexities of MAID Care in Canada From a Nursing Perspective

2024· article· en· W4392398299 on OpenAlexafffundabout
Barbara Pesut, Sally Thorne, Kenneth Chambaere, Margaret Hall, Catharine J. Schiller

Bibliographic record

VenueGlobal Qualitative Nursing Research · 2024
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsUniversity of Northern British ColumbiaSimon Fraser UniversityOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsLegislationNegotiationNursingPerspective (graphical)Context (archaeology)LegislatureInclusion (mineral)Health careQualitative researchPsychologyMedicinePolitical scienceSociologyLawSocial psychology

Abstract

fetched live from OpenAlex

Medical Assistance in Dying (MAID) legislation has evolved rapidly in Canada with significant impacts on nursing practice. The purpose of this paper is to describe evolving complexities in legislative context and practice standards that influence the experiences nurse practitioners and registered nurses have with MAID. Qualitative interviews were conducted with 25 registered nurses and 10 nurse practitioners from diverse contexts across Canada. Participants described their practices and considerations when discussing MAID as part of advance care planning; their use of, and challenges with, waivers of consent; their practice considerations in negotiating the complexities of clients for whom death is not reasonably foreseeable; and their moral wrestling with the inclusion of MAID for persons whose sole underlying medical condition is mental illness. Findings illustrate the moral complexities inherent in the evolving legislation and the importance of robust health and social care systems to the legal and ethical implementation of MAID in Canada.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.499
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.300
GPT teacher head0.602
Teacher spread0.302 · 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 designQualitative
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

Citations14
Published2024
Admission routes3
Has abstractyes

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