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Record W2547167148 · doi:10.1136/eb-2016-102515

Advance care planning and palliative care

2016· article· en· W2547167148 on OpenAlexaff
Roberta Heale, Helen Noble

Bibliographic record

VenueEvidence-Based Nursing · 2016
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsLaurentian University
Fundersnot available
KeywordsPalliative careAdvance care planningNursingMedicine

Abstract

fetched live from OpenAlex

EBN Perspectives brings together key issues from the commentaries in one of our nursing topic themes. This article is part of Evidence Based Nursing (EBN) Perspectives . In this series, commentaries from the past 2 years from a specific nursing theme are brought together and highlights are discussed. The topic for this edition is advance care planning and palliative care. From October 2014 to the October 2016 edition, 12 commentaries were published on the chosen topic. Key themes are extrapolated from these commentaries, and the implications for practice and future research are explored. The 12 commentaries are presented in box 1 and grouped into themes of patient and family/loved ones involvement; nursing advocacy; healthcare processes. Box 1 ### Evidence Based Nursing commentaries on advance care planning and palliative care (October 2014–October 2016) Themes: patient and family involvement; nurse as advocate for patient at end-of-life and processes for implementation of advance care planning (ACP) and palliative care Theme 1: Patient and family/loved ones involvement 1. Threats to parents' roles during the process of their child dying in the paediatric intensive care unit http://ebn.bmj.com/content/19/4/118.extract 2. What ‘a good death’ means for bereaved family carers http://ebn.bmj.com/content/19/2/59.extract 3. Carers providing end-of-life care at home have limited formal support in managing medications http://ebn.bmj.com/content/18/4/115.extract 4. Clarification of the common aspects of dignity in end-of-life care http://ebn.bmj.com/content/18/3/76.extract 5. Home death versus hospital death: …

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.497
Threshold uncertainty score0.468

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.163
GPT teacher head0.457
Teacher spread0.294 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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