MétaCan
Menu
Back to cohort
Record W4392942950 · doi:10.1097/nne.0000000000001635

Virtual Reality Simulation in a Health Assessment Laboratory Course

2024· article· en· W4392942950 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

VenueNurse Educator · 2024
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsCompetence (human resources)PsychologySelf-confidenceDistressVirtual realityConfidence intervalFidelityMedical educationApplied psychologyComputer scienceMedicineClinical psychologySocial psychologyArtificial intelligence

Abstract

fetched live from OpenAlex

BACKGROUND: The purpose of this mixed-methods study was to examine the relationship between virtual reality simulation (VRS) and student satisfaction and self-confidence in a health assessment laboratory course. METHODS: Second-year students (n = 37) completed a postoperative respiratory distress scenario using Elsevier's Simulation Learning System with Virtual Reality. All participants completed the Satisfaction and Self-Confidence in Learning Scale; a subset participated in 1:1 semistructured interviews. RESULTS: Satisfaction and self-confidence scores were strongly correlated. VRS experiences of fidelity, communication confidence and competence, learning with peers, integrated learning and critical thinking, and a safe space to learn were related to students' satisfaction and self-confidence. CONCLUSIONS: VRS experiences are correlated with high student satisfaction and self-confidence.

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)0.001
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 score0.071
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.041
GPT teacher head0.477
Teacher spread0.436 · 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