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Record W3007891275 · doi:10.1177/0193945920907395

High-Fidelity Simulation and Clinical Judgment of Nursing Students in a Maternal–Newborn Course

2020· article· en· W3007891275 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWestern Journal of Nursing Research · 2020
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsUniversity of Windsor
FundersSigma Theta Tau InternationalNational Science Foundation
KeywordsRubricClinical judgmentFidelityPsychologyNurse educationPediatric nursingNursingMedical educationMedicineMedical physicsPedagogyComputer science

Abstract

fetched live from OpenAlex

Clinical judgment, one’s ability to think like a nurse, is an essential skill for safe nursing practice. With the rise of simulation to replace clinical experiences, there is limited evidence regarding the effectiveness of simulation on the development of clinical judgment. This study explored differences in clinical judgment in maternal–newborn courses between undergraduate nursing students participating exclusively in simulation and those participating in hospital-based clinical experiences. Following completion of the clinical rotation, students participated in an evaluative maternal–newborn high-fidelity simulation experience that was recorded and evaluated using the Lasater’s Clinical Judgment Rubric (2007). Lasater’s Clinical Judgment Rubric scores between the simulation and clinical practice groups were compared using an independent sample t-test. There was no statistical difference in clinical judgment scores between the simulation and hospital-based clinical groups ( t = −1.056, P = .295). Our findings suggest that simulation may be a comparable alternative to clinical experience in nursing education.

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.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.040
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.340
GPT teacher head0.609
Teacher spread0.269 · 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