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Evidence-based Nursing Web Sites: Finding the Best Resources

2001· review· en· W2092888059 on OpenAlexaff
Maja Morris, Shannon D. Scott, Carole A. Estabrooks

Bibliographic record

VenueAACN Clinical Issues Advanced Practice in Acute & Critical Care · 2001
Typereview
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsThe InternetPaceWeb resourceNursing practiceNursing researchNursingWeb siteEvidence-based practiceKnowledge managementPsychologyInternet privacyWorld Wide WebComputer scienceMedicineAlternative medicineGeography

Abstract

fetched live from OpenAlex

Evidence-based nursing is becoming an increasingly widespread phenomenon in the nursing profession. As the evidence-based nursing movement grows, the Internet/World Wide Web has become a vital information link for keeping pace with current science and medical advancements. This article describes Internet resources currently available to support evidence-based nursing practice, presents practical search methods for locating these resources, and suggests criteria for evaluating the "evidence" available on the Internet. Results of an Internet search for Web sites that met the proposed criteria for support of an evidence-based nursing practice located only three sites. The sites are described and evaluated for their usefulness. The authors demonstrate that although many Internet resources are available to nurses, few sites provide information or evidence supported by valid research.

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)0.038
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: Review · Consensus signal: Review
Teacher disagreement score0.022
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.038
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0220.021
Science and technology studies0.0010.001
Scholarly communication0.0050.008
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0110.003

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 head0.442
GPT teacher head0.683
Teacher spread0.241 · 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
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

Citations14
Published2001
Admission routes1
Has abstractyes

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