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Resources to Enhance Evidence-based Nursing Practice

2001· review· en· W1973168680 on OpenAlexaff
Donna Ciliska, Janet Pinelli, Alba DiCenso, Nicky Cullum

Bibliographic record

VenueAACN Clinical Issues Advanced Practice in Acute & Critical Care · 2001
Typereview
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCritical appraisalNursing practiceNursingEvidence-based practiceEvidence-based nursingNursing researchBest evidencePatient carePsychologyMEDLINEMedicineMedical educationAlternative medicinePolitical science

Abstract

fetched live from OpenAlex

Evidence-based practice means integrating the best available research evidence with information about patient preferences, clinician skill level, and available resources to make decisions about patient care. Barriers to the use of research-based evidence occur when time, access to journal articles, search skills, critical appraisal skills, and understanding of the language used in research are lacking. Resources are available to overcome these barriers and support an evidence-based nursing practice. This article highlights available resources and describes strategies that nurses can use to develop and sustain an evidence-based nursing practice.

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)0.013
metaresearch head score (Gemma)0.482
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.972
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.482
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0000.004
Open science0.0020.000
Research integrity0.0020.009
Insufficient payload (model declined to judge)0.0010.004

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 head0.467
GPT teacher head0.749
Teacher spread0.282 · 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; both teacher heads agree on what is shown here.

Study designOther design
Domainnot available
GenreReview

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

Citations91
Published2001
Admission routes1
Has abstractyes

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