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Record W2104249032 · doi:10.12927/cjnl.2004.17018

Setting the Climate for Evidence-Based Nursing Practice: What Is the Leader's Role?

2004· article· en· W2104249032 on OpenAlexaffvenue
Sonia Udod, W. Dean Care

Bibliographic record

VenueNursing leadership · 2004
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsNursingObligationSituatedWork (physics)Nursing practiceEvidence-based practiceNursing researchClinical PracticePsychologyMedicinePolitical scienceAlternative medicine

Abstract

fetched live from OpenAlex

Nurses are being challenged today to justify their practice. Many clinical and policy decisions in nursing are based upon isolated, ritualistic and unsystematic forms of clinical practice. The growing movement towards establishing evidence-based nursing practice (EBNP) is situated in a systematic appraisal of the best evidence available. Nurse leaders have an obligation to cultivate sound clinical and economic practices leading to quality patient care and positive work life environments for nurses.

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)0.194
metaresearch head score (Gemma)0.343
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.194
Threshold uncertainty score0.994

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1940.343
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0070.003
Science and technology studies0.0200.032
Scholarly communication0.0530.033
Open science0.0080.037
Research integrity0.0240.028
Insufficient payload (model declined to judge)0.0050.004

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 head0.576
GPT teacher head0.543
Teacher spread0.032 · 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.

Study designNot applicable
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

Citations39
Published2004
Admission routes2
Has abstractyes

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