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Record W2117489570 · doi:10.3414/me9222

Evaluating the Impact of Hybrid Electronic-paper Environments Upon Novice Nurse Information Seeking

2009· article· en· W2117489570 on OpenAlexaff
Louise Lemieux‐Charles, Lynn Nagle, Günther Eysenbach

Bibliographic record

VenueMethods of Information in Medicine · 2009
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsRecallAffect (linguistics)CognitionInformation seekingPsychologyComputer scienceApplied psychologyNursingMedicineMedical educationCognitive psychologyCommunicationInformation retrieval

Abstract

fetched live from OpenAlex

OBJECTIVE: The purpose of this study was to investigate the effects of hybrid environments (i.e. where part of the patient record is paper-based and part of it is electronic) upon aspects of novice nurse information seeking (i.e. amount of information accessed, choice of key information sources, type of information and use of information seeking tactics). METHODS: A within group, laboratory, experimental study was conducted using two simulated environments (i.e. a paper environment and a hybrid environment). Thirty-five novice nurses participated in the study. RESULTS: Findings revealed significant differences between the paper and hybrid environments in terms of their effects upon aspects of novice nurse information seeking. Subjects accessed: 1) less information in the hybrid environment than the paper environment, 2) more non-electronic sources of information were accessed by novice nurses in the hybrid environment, and 3) novice nurses used more passive information seeking tactics in the hybrid environment than the paper environment. Qualitative findings from the cued recall data revealed subjects experienced increased cognitive load in the hybrid environment. CONCLUSIONS: Hybrid environments may affect aspects of novice nurse information seeking. Future research should explore the effects of hybrid environments upon the information seeking of other types of health professionals (e.g. physicians, physiotherapists) with differing levels of expertise (i.e. novice, intermediate and expert).

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)0.018
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.855
Threshold uncertainty score0.681

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.124
GPT teacher head0.607
Teacher spread0.482 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations36
Published2009
Admission routes1
Has abstractyes

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