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Record W4399442979 · doi:10.1080/10872981.2024.2363006

The 2024 French guidelines for scenario design in simulation-based education: manikin-based immersive simulation, simulated participant-based immersive simulation and procedural simulation

2024· article· en· W4399442979 on OpenAlexaff
Guillaume Der Sahakian, Maxime de Varenne, Clément Buléon, Guillaume Alinier, Christian Balmer, Antonia Blanié, Bertrand Bech, A. Bellot, Hamdi Boubaker, Nadège Dubois, Francisco Guevara, Erwan Guillouët, Jean-Claude Granry, Morgan Jaffrelot, François Lecomte, Fernande Lois, Mohammed Mouhaoui, Ollivier Ortolé, Méryl Paquay, Justine Piazza, Marie Pittaco, Patrick Plaisance, Dan Benhamou, Gilles Chiniara, Étienne Rivière

Bibliographic record

VenueMedical Education Online · 2024
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsModalitiesComputer scienceSimulation trainingMedical simulationHuman–computer interactionHealth careSimulation3d simulationMultimedia

Abstract

fetched live from OpenAlex

BACKGROUND: Simulation-based education in healthcare encompasses a wide array of modalities aimed at providing realistic clinical experiences supported by meticulously designed scenarios. The French-speaking Society for Simulation in Healthcare (SoFraSimS) has developed guidelines to assist educators in the design of scenarios for manikin- or simulated participant- based immersive simulation and procedural simulation, the three mainly used modalities. METHODS: After establishing a French-speaking group of experts within the SoFraSimS network, we performed an extensive literature review with theory-informed practices and personal experiences. We used this approach identify the essential criteria for practice-based scenario design within the three simulation modalities. RESULTS: We present three comprehensive templates for creating innovative scenarios and simulation sessions, each tailored to the specific characteristics of a simulation modality. The SoFraSimS templates include five sections distributed between the three modalities. The first section contextualizes the scenario by describing the practicalities of the setting, the instructors and learners, and its connection to the educational program. The second section outlines the learning objectives. The third lists all the elements necessary during the preparation phase, describing the educational method used for procedural simulation (such as demonstration, discovery, mastery learning, and deliberate practice). The fourth section addresses the simulation phase, detailing the behaviors the instructor aims to analyze, the embedded triggers, and the anticipated impact on simulation proceedings (natural feedback). This ensures maximum control over the learning experience. Finally, the fifth section compiles elements for post-simulation modifications to enhance future iterations. CONCLUSION: We trust that these guidelines will prove valuable to educators seeking to implement simulation-based education and contribute to the standardization of scenarios for healthcare students and professionals. This standardization aims to facilitate communication, comparison of practices and collaboration across different learning and healthcare institutions.

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)0.002
metaresearch head score (Gemma)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-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 score0.705
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.138
GPT teacher head0.477
Teacher spread0.339 · 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

Citations8
Published2024
Admission routes1
Has abstractyes

Explore more

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