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Record W2939667850 · doi:10.17483/2368-6669.1179

Canadian Nursing Students’ Experiences with Medical Assistance in Dying

2019· article· en· W2939667850 on OpenAlexaffvenueabout
Cedar McMechan, Anne Bruce, Rosanne Beuthin

Bibliographic record

VenueQuality Advancement in Nursing Education - Avancées en formation infirmière · 2019
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsIsland HealthUniversity of Victoria
Fundersnot available
KeywordsThematic analysisNursingNurse educationAutonomyConfusionPsychologyQualitative researchPerceptionMedicineSociologyPolitical science

Abstract

fetched live from OpenAlex

The recent introduction of medical assistance in dying (MAiD) has significant implications for healthcare workers, nurse educators, and society at large. Nursing students are being asked to participate directly or indirectly with a medically assisted death. Little is known about nursing students’ experiences with or attitudes toward MAiD in Canada.The purpose of this study was to explore the experiences of fourth-year nursing students in caring for patients who choose MAiD. The aim was to understand students’ perceptions of their educational preparation regarding MAiD and identify potential gaps in existing knowledge and skills. A qualitative design and thematic analysis were used. Nine senior nursing students were interviewed using semi-structured questions. Themes and corresponding sub-themes were inductively developed and include: (1) role confusion (where do student nurses fit? and fear of saying the wrong thing); (2) honouring patient autonomy (nurse as advocate-- not judge, and MAiD as ‘normal’ nursing); (3) professional tensions (entangled emotions and intellect, and surfacing hidden values); (4) students’ recommendations for education. Nursing students report support and interest in having opportunities to participate in MAiD. Nevertheless, they feel ill-equipped and perceive nurses are also unclear about their roles and responsibilities in the provision of MAiD.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.486
Threshold uncertainty score0.648

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.059
GPT teacher head0.475
Teacher spread0.416 · 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

Citations8
Published2019
Admission routes3
Has abstractyes

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