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Record W2981320929 · doi:10.1111/jan.14252

Investigating palliative care nurse attitudes towards medical assistance in dying: An exploratory cross‐sectional study

2019· article· en· W2981320929 on OpenAlexaff
Laurie Freeman‐Gibb, Kathryn Pfaff, Lauren Kopchek, Jordyn Liebman

Bibliographic record

VenueJournal of Advanced Nursing · 2019
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsPalliative careNursingCross-sectional studyExploratory researchSocial supportMedicinePsychologyFamily medicineSocial psychology

Abstract

fetched live from OpenAlex

AIM: To investigate palliative care nurse attitudes towards medical assistance in dying. DESIGN: An exploratory cross-sectional study design. METHODS: A mailed letter recruited participants with data collection occurring on a secure online survey platform between November 2017-February 2018. Data analyses included descriptive and bivariate statistics and stepwise linear regression. RESULTS: Palliative care nurse attitudes towards medical assistance in dying were explained by perceived expertise in the social domain of palliative care, personal importance of religion/faith, professional importance of religion/faith, and nursing designation. CONCLUSION: This study reveals the perceived importance of religion, versus religious affiliation alone, as significant in influencing provider attitudes towards assisted dying. Further research is needed to understand differences in attitudes between Registered Nurses and Registered Practical Nurses and how the social domain of palliative care influences nurse attitude. IMPACT: Organizations must prioritize nursing input, encourage open interprofessional dialogue and provide support for ethical decision-making, practice decisions, and conscientious objection surrounding medical assistance in dying. Longitudinal nursing studies are needed to understand the impact of legislation on quality and person-centred end-of-life care and the emotional well-being/retention of palliative care nurses.

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)0.001
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 score0.058
Threshold uncertainty score0.647

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.126
GPT teacher head0.483
Teacher spread0.357 · 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

Citations29
Published2019
Admission routes1
Has abstractyes

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