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Record W4405923616 · doi:10.1097/njh.0000000000001102

Nurses' Perceptions of Practice Supports Related to Medical Assistance in Dying

2024· article· en· W4405923616 on OpenAlexaffabout
Jill Henderson, Jennifer Stephens, Lorraine M. Thirsk

Bibliographic record

VenueJournal of Hospice and Palliative Nursing · 2024
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsAthabasca UniversityFleming College
Fundersnot available
KeywordsDebriefingNursingCLARITYContext (archaeology)PerceptionAmbiguityQualitative researchPsychologyNursing practiceMedicineMedical educationSociology

Abstract

fetched live from OpenAlex

Since the inception of medical assistance in dying (MAiD) in Canada in 2016, the health care system continues to refine MAiD delivery models. The frameworks informing nursing practice related to MAiD are subject to variability across the country, leading to nursing role ambiguity and barriers in relational practice. Using critical incident technique, this qualitative research study explores the experiences of 7 Canadian nurses engaging with patients seeking MAiD. Semistructured interviews were conducted to understand the nurses' perceptions of the helping, hindering, and desired elements of current nursing practice supports within the context of MAiD. Eighteen significant incidents were included in the data analysis. Findings demonstrate that gaps in practice support exist related to nursing role clarity, educational support to enrich therapeutic communication skills, and staff-focused resources, such as debriefing and improved communication networks. Understanding nursing experiences within this context highlights the need for more consistent nursing practice frameworks and clinical practice supports to facilitate improved therapeutic relationships and patient care.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.143
Threshold uncertainty score0.376

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.050
GPT teacher head0.470
Teacher spread0.420 · 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 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

Citations1
Published2024
Admission routes2
Has abstractyes

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