MétaCan
Menu
Back to cohort

A Survey Study of Pediatric Nurses' Use of Information Sources

2006· article· en· W2056556898 on OpenAlexaff
M. Loretta Secco, Roberta L. Woodgate, Andrea Hodgson, SANDI KOWALSKI, JANNELLE PLOUFFE, PATRICIA R. ROTHNEY, Doris M. Sawatzky-Dickson, E Suderman

Bibliographic record

VenueCIN Computers Informatics Nursing · 2006
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsCape Breton University
Fundersnot available
KeywordsInformation systemInterpersonal communicationNursingPsychologyMedical educationComputer scienceMedicineSocial psychology

Abstract

fetched live from OpenAlex

This survey study explored use of different information sources among a convenience sample of 113 bedside pediatric nurses. The study was guided by three interrelated concepts: types of information sources, levels of evidence, and computer skill. The Nursing Information Use Survey measured use of information sources, impact of information sources on nursing care, barriers to information, and expectations that a computerized clinical desktop or patient information management system would improve patient care. Significant correlations between use of interpersonal and non-computer-based information and non-computer- and computer-based information supported the conceptual model. Use of traditional, non-computer information sources such as textbooks and print-based journals was higher among baccalaureate, compared with diploma, prepared nurses. Nurses with greater computer and online searching skill used more computer-based information. Findings suggested that strategies to improve nurses' computer and information searching skills may promote use of higher-level evidence in planning nursing care.

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)0.004
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.996
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.143
GPT teacher head0.450
Teacher spread0.307 · 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 designObservational
DomainMethods
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

Citations24
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueCIN Computers Informatics NursingSame topicHealth Sciences Research and EducationFrench-language works237,207