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Record W2089795752 · doi:10.1155/2011/907172

An Examination of Palliative or End-of-Life Care Education in Introductory Nursing Programs across Canada

2011· article· en· W2089795752 on OpenAlexaffabout
Donna M. Wilson, Barbara L. Goodwin, Jessica A. Hewitt

Bibliographic record

VenueNursing Research and Practice · 2011
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsUniversity of ManitobaUniversity of Alberta
Fundersnot available
KeywordsPracticumMedicineCurriculumNursingPalliative careEnd-of-life careNurse educationMedical educationPopulationPedagogyPsychology

Abstract

fetched live from OpenAlex

An investigation was done to assess for and describe the end-of-life education provided in Canadian nursing programs to prepare students for practice. All 35 university nursing schools/faculties were surveyed in 2004; 29 (82.9%) responded. At that time, all but one routinely provided this education, with that school developing a course (implemented the next year). As compared to past surveys, this survey revealed more class time, practicum hours, and topics covered, with this content and experiences deliberately planned and placed in curriculums. A check in 2010 revealed that all of these schools were providing death education similar to that described in 2004. These findings indicate that nurse educators recognize the need for all nurses to be prepared to care for dying persons and their families. Regardless, more needs to be done to ensure novice nurses feel capable of providing end-of-life care. Death education developments will be needed as deaths increase with population aging.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.965
Threshold uncertainty score0.250

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0040.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.290
GPT teacher head0.547
Teacher spread0.257 · 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 source (direct Gemma or distilled Codex), 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
Published2011
Admission routes2
Has abstractyes

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