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Record W2974385610 · doi:10.1515/ijnes-2019-0014

Nursing Students’ Perceived Self-Efficacy and the Generation of Medication Errors with the Use of an Electronic Medication Administration Record (eMAR) in Clinical Simulation

2019· article· en· W2974385610 on OpenAlexaff
Ryan Chan, Richard Booth, Gillian Strudwick, Barbara Sinclair

Bibliographic record

VenueInternational Journal of Nursing Education Scholarship · 2019
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsCentre for Addiction and Mental HealthWestern University
Fundersnot available
KeywordsNursingMedicineSelf-efficacyNurse educationConfidence intervalFamily medicinePsychologySocial psychologyInternal medicine

Abstract

fetched live from OpenAlex

Medication errors continue to be a significant issue, posing substantial threats to the safety and well-being of patients. Through Bandura's theory of self-efficacy, nursing students' self-efficacy (confidence) related to medication administration was examined to investigate its influence on the generation of medication errors with the use of an Electronic Medication Administration Record (eMAR) in clinical simulation. This study examined the generation of medication errors and the differences that may exist based on nursing students' perceived confidence. The findings of this study demonstrated that nursing students continue to generate medication errors within clinical simulation. No differences in the generation of medication errors were found between nursing students with perceived high levels of confidence and those with perceived low levels of confidence (one exception noted). Further examination of the variables and contextual factors related to safe medication administration practices is required to inform nursing education and practice.

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)0.002
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 score0.329
Threshold uncertainty score0.388

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.205
GPT teacher head0.535
Teacher spread0.330 · 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

Citations20
Published2019
Admission routes1
Has abstractyes

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