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Record W2791710871 · doi:10.1097/njh.0000000000000425

Development of Nurse Practitioner Competencies for Advance Care Planning

2018· article· en· W2791710871 on OpenAlexaff
Roberta Heale, Lori Rietze, Laura Hill, Stacey Roles

Bibliographic record

VenueJournal of Hospice and Palliative Nursing · 2018
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsNOSM UniversityCanadian Association of Occupational TherapistsHealth Sciences NorthLaurentian University
Fundersnot available
KeywordsDelphi methodDelphiHealth careAdvance care planningNursingPlan (archaeology)Nurse practitionersMedical educationMedicinePsychologyComputer sciencePolitical sciencePalliative care

Abstract

fetched live from OpenAlex

This article describes the development of nurse practitioner (NP) competencies for advance care planning. Nurse practitioners are well positioned to implement advance care planning with their patients; however, very few patients have an advance care plan. A modified Delphi method was used to engage NPs in achieving consensus for advance care planning competencies. In round 1, draft competencies were developed from the findings of a survey of NP beliefs, knowledge, and level of implementation of advance care planning. In round 2, 29 NPs participated in the evaluation of the draft competencies and their components. Revisions were made, and a final round was conducted where 15 of the original NP participants confirmed their consensus with the final document. The final document includes 4 competencies, each with several elements: (1) Clinical Practice, (2) Consultation and Communication, (3) Advocacy, and (4) Therapeutic Management. Advance care planning competencies will provide NPs with a guide that can be used to ensure that they are able to clearly identify their distinct role in advance care planning. These competencies may inform the integration of advance care planning in a variety of health care settings and with other health care providers.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.272
Threshold uncertainty score0.322

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 head0.114
GPT teacher head0.457
Teacher spread0.343 · 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 designQualitative
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

Citations18
Published2018
Admission routes1
Has abstractyes

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