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Record W2553094137

Discourse / Discours - A Formative Evaluation of Nurses' Use of Electronic Devices in a Home Care Setting

2013· article· en· W2553094137 on OpenAlexvenueno aff
Diane Doran, Cheryl Reid‐Haughian, Autumn Marie Chilcote, Yu Bai

Bibliographic record

VenueCanadian Journal of Nursing Research · 2013
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsFormative assessmentFocus groupSocial capitalSample (material)NursingPsychologyData collectionUser satisfactionMedicineMedical educationComputer sciencePedagogySociology
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this study was to evaluate the implementation of a clinical information system (CIS) in a community setting. The researchers used a mixed-method design involving interviews, focus groups, and surveys. An independent cross-sectional sample of nurses was surveyed. At time 1 a total of 118 nurses responded and at time 2 a total of 81. Respondents were moderately satisfied with features of the CIS. User satisfaction was positively associated with access to structural and electronic resources and social capital and negatively associated with nurses' age at time 1. Social capital was positively associated with user satisfaction at time 2. Younger age was associated with overall research use at both time 1 and time 2. Research use was negatively associated with evaluation and feedback but positively associated with formal interactions. This evaluation identified the importance of educational support, user-centred design, and responsiveness to successful implementation of CISs in a community setting.

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)0.042
metaresearch head score (Gemma)0.109
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.224

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0420.109
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0030.004
Scholarly communication0.0040.003
Open science0.0010.005
Research integrity0.0010.002
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 head0.180
GPT teacher head0.570
Teacher spread0.390 · 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 designQualitative
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

Citations0
Published2013
Admission routes1
Has abstractyes

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