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Record W2907081385 · doi:10.1097/njh.0000000000000486

Medical Assistance in Dying

2019· article· en· W2907081385 on OpenAlexaffabout
Grace Suva, Tasha Penney, Christine McPherson

Bibliographic record

VenueJournal of Hospice and Palliative Nursing · 2019
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsRegistered Nurses' Association of Ontario
Fundersnot available
KeywordsLegislationNursingMedical practiceHealth careMedicinePsychologyPolitical scienceMedical educationLaw

Abstract

fetched live from OpenAlex

In June 2016, Bill C-14 or Medical Assistance in Dying legislation became law in Canada. With this law came changes to nurses' (ie, nurse practitioner, registered nurse, registered practical nurse) scopes of practice, roles, and responsibilities. While federal law, regulatory, and organizational policies are developed to inform nurses about the practice of medical assistance in dying, there is little evidence examining how nurses' roles and responsibilities are enacted in practice. Therefore, a scoping review was conducted to synthesize the evidence on nurses' roles and responsibilities in relation to medical assistance in dying and to identify gaps in the literature. A secondary aim was to identify organizational supports for nurses to effectively and ethically engage in medical assistance in dying. Using a recognized and rigorous scoping review methodology, the findings from 24 research studies were synthesized in this article. The analysis highlights the importance of effective health care professional engagement with the individual in the decision-making process and of the need to educate, support, and include nurses in providing medical assistance in dying. Overall, the current research on medical assistance in dying is limited in Canada, and more attention is needed on the role of the nurse.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.297

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.075
GPT teacher head0.432
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

Citations33
Published2019
Admission routes2
Has abstractyes

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