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Record W2082585648 · doi:10.2202/1548-923x.2123

Assessment of Electronic Health Record Usability with Undergraduate Nursing Students

2011· article· en· W2082585648 on OpenAlexaff
Stephanie P. Jones, Lorie Donelle

Bibliographic record

VenueInternational Journal of Nursing Education Scholarship · 2011
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsWestern University
Fundersnot available
KeywordsUsabilityCurriculumDocumentationConfidentialityMedical educationHealth information technologyHealth informaticsNurse educationHealth careApprenticeshipNursingInformaticsMedicinePsychologyComputer sciencePublic healthPedagogyEngineering

Abstract

fetched live from OpenAlex

Health information technology (HIT), and specifically electronic health records (EHR), are recognized as fundamental tools for collecting, storing, retrieving, and monitoring patient care and information. However, few schools of nursing have incorporated theoretical or practical aspects of HIT competencies within the educational curriculum. The purpose of this study was to conduct a usability assessment to explore undergraduate nursing student electronic health record documentation knowledge and skill, using a patient case scenario to inform the development of an informatics-based undergraduate nursing curriculum. Three themes were identified: "Being a Novice User/Practitioner," "Confidentiality and Security," and "Repetition and Practice." Integration of the EHR into nursing curriculum will allow students an EHR apprenticeship with the potential to enhance understanding and skill of nursing processes, documentation, and critical thinking. Findings will also guide teaching and learning strategies that will respond to rising expectations for competency with health information technology.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.159
GPT teacher head0.563
Teacher spread0.403 · 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 designObservational
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

Citations29
Published2011
Admission routes1
Has abstractyes

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