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Record W4404963907 · doi:10.1097/sih.0000000000000838

Tabletop Simulations in Medical Emergencies

2024· article· en· W4404963907 on OpenAlexaff
Amélie Frégeau, Billy Vinette, Alexandra Lapierre, Marc‐André Maheu‐Cadotte, Guillaume Fontaine, Véronique Castonguay, Rodrigo Flores-Soto, Zoé Garceau-Tremblay, Samuel Blais, Delphine Hansen-Jaumard, François Laramée, Massimiliano Iseppon, Raoul Daoust, Sylvie Cossette, Michael Buyck, Richard Fleet, Alexis Cournoyer

Bibliographic record

VenueSimulation in Healthcare The Journal of the Society for Simulation in Healthcare · 2024
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal
Fundersnot available
KeywordsModality (human–computer interaction)Medical educationPsychologyMedicineRandomized controlled trialStatement (logic)Computer scienceSurgeryHuman–computer interaction

Abstract

fetched live from OpenAlex

SUMMARY STATEMENT: Tabletop simulations (TTS) are a novel educational modality used in health care education. The objective of this scoping review was to describe the use of TTS in medical emergencies, specifically settings, specialties, participants, formats, and outcomes.We included 70 studies (33 descriptive studies [47%], 33 cohort studies [47%], and 2 randomized controlled trials [3%]), of which 65 reported positive results regarding reaction and learning educational outcomes (reaction: n = 37, 53%; learning: n = 25, 36%; behavior: n = 7, 10%; result: n = 1, 1%). The scenario for most TTS was a disaster (n = 56; 80%). Most TTS involved participants from several professions (n = 45; 64%). A board game was used in 26 studies (37%).Most studies on TTS in medical emergencies involved participants from multiple professions addressing disaster scenarios and showed positive results pertaining to reaction or learning educational outcomes.

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)0.002
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: Empirical
Teacher disagreement score0.234
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.002
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 head0.062
GPT teacher head0.436
Teacher spread0.374 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueSimulation in Healthcare The Journal of the Society for Simulation in HealthcareSame topicSimulation-Based Education in HealthcareFrench-language works237,207