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Record W2588669690 · doi:10.5430/jnep.v7n7p30

Simulation is more than working with a mannequin: Student’s perceptions of their learning experience in a clinical simulation environment

2017· article· en· W2588669690 on OpenAlexvenueno aff
Karla Rodriguez, Noreen Nelson, Mattia J. Gilmartin, Lloyd A. Goldsamt, Hila Richardson

Bibliographic record

VenueJournal of Nursing Education and Practice · 2017
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsnot available
FundersRobert Wood Johnson Foundation
KeywordsPerceptionMedical educationFidelityPoint (geometry)PsychologyClinical PracticeNursingMedicineComputer science

Abstract

fetched live from OpenAlex

Purpose: This paper describes undergraduate nursing students’ assessment of learning in a clinical teaching model that replaces 50% of the traditional clinical hours with high-fidelity simulation. We assessed students’ perceptions of the use of best practices in simulation teaching, and the importance assigned to each teaching practice to support learning.Methods: Longitudinal program evaluation design. We surveyed undergraduate nursing students with the Educational Practices Questionnaire (EPQ) at the mid-point (semester 2) and end of the program (semester 4). We used paired t-tests to assess changes in student EPQ scores between mid- and end-program.Results: Results showed that students’ reported greater exposure over time to clinical simulation activities that fostered active learning and high expectations; the degree to which they rated collaborative learning as important also increased.Conclusions: Students’ perceptions of the use of educational best practices and the importance of simulation in nursing education from program mid-point to end-point lends support for a clinical teaching model that uses a simulation to substitute for traditional clinical hours.

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)0.001
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 score0.067
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.278
GPT teacher head0.564
Teacher spread0.286 · 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

Citations18
Published2017
Admission routes1
Has abstractyes

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