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CREATING A CULTURAL SHIFT

2003· article· en· W1977659910 on OpenAlexaff
Belinda Parke, Donna Cohen Ross, Lesley Moss

Bibliographic record

VenueJournal for Nurses in Staff Development · 2003
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsRoyal Canadian NavyFraser HealthIsland Health
Fundersnot available
KeywordsSpecialtyGerontological nursingAcute careNursingOlder peopleMedicineGeriatric careAged carePsychologyGerontologyFamily medicineHealth care

Abstract

fetched live from OpenAlex

As the number of older adults in acute care rises, acute care specialty nurses must gain gerontological knowledge in their nursing practice with older patients. A blend of two bodies of knowledge and competencies is required to care effectively for older adults in the hospital. To this end, a gerontological enrichment program was developed to improve care to hospitalized older patients by augmenting acute care nurses competencies with gerontological skill, knowledge, and abilities. This article will explain the implementation details, the anticipated outcomes for nurses and older acutely ill patients, the evaluation process, and the strategies that are in place to maintain the gains.

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)0.013
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0200.028
Scholarly communication0.0130.011
Open science0.0020.029
Research integrity0.0040.009
Insufficient payload (model declined to judge)0.0060.001

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 head0.055
GPT teacher head0.431
Teacher spread0.376 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations3
Published2003
Admission routes1
Has abstractyes

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