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Record W4391036357 · doi:10.1097/sih.0000000000000762

Effects of Simulation Fidelity on Health Care Providers on Team Training—A Systematic Review

2024· article· en· W4391036357 on OpenAlexaff
Sally A. Mitchell, Erin E Blanchard, Vernon Curran, Theresa Hoadley, Aaron Donoghue, Andrew Lockey

Bibliographic record

VenueSimulation in Healthcare The Journal of the Society for Simulation in Healthcare · 2024
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsFidelityInclusion (mineral)TeamworkHealth careRandomized controlled trialPsychologyEvidence-based practiceApplied psychologyMedical educationMedicineComputer scienceAlternative medicineSocial psychology

Abstract

fetched live from OpenAlex

ABSTRACT: This systematic review, following PRISMA standards, aimed to assess the effectiveness of higher versus lower fidelity simulation on health care providers engaged in team training. A comprehensive search from January 1, 2011 to January 24, 2023 identified 1390 studies of which 14 randomized (n = 1530) and 5 case controlled (n = 257) studies met the inclusion criteria. The certainty of evidence was very low due to a high risk of bias and inconsistency. Heterogeneity prevented any metaanalysis. Limited evidence showed benefit for confidence, technical skills, and nontechnical skills. No significant difference was found in knowledge outcomes and teamwork abilities between lower and higher fidelity simulation. Participants reported higher satisfaction but also higher stress with higher fidelity materials. Both higher and lower fidelity simulation can be beneficial for team training, with higher fidelity simulation preferred by participants if resources allow. Standardizing definitions and outcomes, as well as conducting robust cost-comparative analyses, are important for future research.

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)0.006
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.673
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.050
GPT teacher head0.424
Teacher spread0.373 · 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.

Study designSimulation or modeling
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

Citations12
Published2024
Admission routes1
Has abstractyes

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