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Record W2060862643 · doi:10.12927/cjnl.2009.21153

Improving Gerontology Content in Baccalaureate Nursing Education through Knowledge Transfer to Nurse Educators

2009· article· en· W2060862643 on OpenAlexafffundvenueabout
Lynn McCleary, Katherine S. McGilton, Véronique Boscart, Abe Oudshoorn

Bibliographic record

VenueNursing leadership · 2009
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsBrock University
FundersCanadian Institutes of Health Research
KeywordsNursingNurse educationCurriculumGerontological nursingTeam nursingNursing researchMedicinePsychologyMedical educationPedagogy

Abstract

fetched live from OpenAlex

Across practice settings, most nursing care is provided to older adults. Yet most nurses receive limited education to care for older adults, especially those with complex needs. A Knowledge Exchange Institute for Geriatric Nursing Education brought together 31 Canadian nursing faculty members and nursing doctoral students and provided them with tools and resources to enhance teaching and curriculum in baccalaureate nursing programs. Guided by the Knowledge-to-Action Process model, participants received usable summaries of the best research evidence about care for older adults and tools to increase the likelihood of successful integration of these resources in their teaching and curriculum. Feedback from participants indicates that their personal goals and the goals of the Knowledge Exchange were met. Through a public interactive wiki, participants and others will continue the process of knowledge exchange to improve nursing education and nursing care for older persons.

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)0.008
metaresearch head score (Gemma)0.029
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.318
GPT teacher head0.448
Teacher spread0.129 · 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

Citations23
Published2009
Admission routes4
Has abstractyes

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