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Record W2810848292 · doi:10.1111/nuf.12280

Medical assistance in dying (MAiD): Canadian nurses’ experiences

2018· article· en· W2810848292 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueNursing Forum · 2018
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsNarrativeSensemakingNursingDistressPsychologyHealth careMedicinePublic relationsPolitical sciencePsychotherapistLaw

Abstract

fetched live from OpenAlex

Medical assistance in dying (MAiD) represents a historic change in Canadian society and the provision of end-of-life care. In this descriptive narrative inquiry, 17 nurses were interviewed during the first 6 months of assisted dying becoming a legal option for patients in Canada. Nurses' experiences of either providing care for a patient who had chosen MAiD, or declining to participate in MAiD, were explored. Findings describe three themes and eight storylines of the impact of MAiD on nurses' view of the profession, clinical practice, and personally. While most nurses perceived MAiD as an extension of the profession and their nursing practice, a small number also expressed moral distress as they grappled with assisted dying. Narratives illustrated an ongoing sensemaking process and spectrum of emotions. These findings offer insight and provide direction for nurses and managers in this new clinical and legal reality. Further research is needed to understand more fully the moral distress of some nurses, as well as the importance of communicating openly and nonjudgmentally with patients, families, and the health-care team.

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.

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)0.004
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.708
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.065
GPT teacher head0.505
Teacher spread0.440 · 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