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Record W2329965365 · doi:10.1097/njh.0000000000000021

Educating Nurses for Palliative Care

2014· article· en· W2329965365 on OpenAlexaff
Barbara Pesut, Richard Sawatzky, Kelli Stajduhar, Barbara McLeod, Lynnelle Erbacker, Eric K. H. Chan

Bibliographic record

VenueJournal of Hospice and Palliative Nursing · 2014
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusMichael Smith Health Research BCTrinity Western UniversityUniversity of British ColumbiaProvidence Health CareFraser HealthCentre for Advancing Health OutcomesWestern University
Fundersnot available
KeywordsPalliative careModerationNursingMedicinePsychologyFamily medicineMedical education

Abstract

fetched live from OpenAlex

An aging population requires that nurses in all areas of practice be knowledgeable about high-quality palliative care. The purpose of this scoping review was to summarize the available evidence for providing palliative care education for nurses. Searches were conducted in the spring of 2012 of 5 electronic databases using controlled vocabulary. English-language articles published between 2001 and 2011 were included in the review, yielding a sample of 58 studies. Findings reviewed included country and setting of study; palliative knowledge taught; methods, number of hours, and duration of education; study design; and evaluation methods. Eighty-six percent of studies reported positive outcomes. Effect size calculations for 9 outcome measures resulted in large (n = 1), moderate (n = 4), and small (n = 4) effects in a positive direction. However, effect sizes were heterogeneous, suggesting moderator variables. Although there appears to be an overall positive effect of palliative education, findings from this scoping review illustrate the diversity of educational approaches and lack of rigorous study designs, making it difficult to make recommendations for an evidence-based approach to educating nurses in palliative care.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.209
Threshold uncertainty score0.590

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.105
GPT teacher head0.466
Teacher spread0.361 · 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

Citations25
Published2014
Admission routes1
Has abstractyes

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