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Record W2781990135 · doi:10.1097/njh.0000000000000406

Palliative Nursing Summit

2018· article· en· W2781990135 on OpenAlexaff
Mi‐Kyung Song, Clareen Wiencek, Brenda Nevidjon, Debbie Fraser, Polly Mazanec

Bibliographic record

VenueJournal of Hospice and Palliative Nursing · 2018
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsAthabasca University
Fundersnot available
KeywordsNursingSummitPalliative careAdvance care planningMedicineReimbursementDocumentationInclusion (mineral)Nurse educationPrimary nursingTeam nursingHealth carePsychology

Abstract

fetched live from OpenAlex

Advance care planning (ACP) is an essential component of quality palliative care that requires expert communication skills. Nurses are often the health care provider patients and families rely on when exploring their values and preferences and making treatment decisions. Therefore, communication and ACP was one of the 3 areas of practice addressed during the Palliative Nursing Summit. This article summarizes patient outcomes and nursing actions recommended by summit participants related to communication and ACP. Areas addressed included education, clinical care, research, and policy/regulation. Recommended patient outcomes included the honoring of patient/family preferences and the inclusion of ACP discussions during routine care and across the life span. Recommended nursing actions included the following: (1) nursing education (both undergraduates and practicing nurses) and competencies related to communication and ACP be developed and implemented; (2) primary palliative care, including communication and ACP, be included in the practice standards of all nursing specialties; (3) health care systems support conversations about ACP and related documentation; (4) research be conducted related to the implementation of patient/family preferences and related health care utilization; and (5) regulation and reimbursement be crafted to support nursing practice related to ACP and related conversations at the nurses' full level of expertise.

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 score0.129
Threshold uncertainty score0.769

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.125
GPT teacher head0.456
Teacher spread0.331 · 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

Citations34
Published2018
Admission routes1
Has abstractyes

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