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Record W2572086525 · doi:10.1186/s12909-016-0838-3

Design of simulation-based medical education and advantages and disadvantages of in situ simulation versus off-site simulation

2017· editorial· en· W2572086525 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

VenueBMC Medical Education · 2017
Typeeditorial
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsUniversity of Ottawa Skills and Simulation CentreOttawa HospitalUniversity of Ottawa
FundersLaerdal Foundation for Acute MedicineAase og Ejnar Danielsens Fond
KeywordsComputer scienceMedical educationIn situMedical physicsMedical simulationMedicineSimulationChemistry

Abstract

fetched live from OpenAlex

BACKGROUND: Simulation-based medical education (SBME) has traditionally been conducted as off-site simulation in simulation centres. Some hospital departments also provide off-site simulation using in-house training room(s) set up for simulation away from the clinical setting, and these activities are called in-house training. In-house training facilities can be part of hospital departments and resemble to some extent simulation centres but often have less technical equipment. In situ simulation, introduced over the past decade, mainly comprises of team-based activities and occurs in patient care units with healthcare professionals in their own working environment. Thus, this intentional blend of simulation and real working environments means that in situ simulation brings simulation to the real working environment and provides training where people work. In situ simulation can be either announced or unannounced, the latter also known as a drill. This article presents and discusses the design of SBME and the advantage and disadvantage of the different simulation settings, such as training in simulation-centres, in-house simulations in hospital departments, announced or unannounced in situ simulations. DISCUSSION: Non-randomised studies argue that in situ simulation is more effective for educational purposes than other types of simulation settings. Conversely, the few comparison studies that exist, either randomised or retrospective, show that choice of setting does not seem to influence individual or team learning. However, hospital department-based simulations, such as in-house simulation and in situ simulation, lead to a gain in organisational learning. To our knowledge no studies have compared announced and unannounced in situ simulation. The literature suggests some improved organisational learning from unannounced in situ simulation; however, unannounced in situ simulation was also found to be challenging to plan and conduct, and more stressful among participants. The importance of setting, context and fidelity are discussed. Based on the current limited research we suggest that choice of setting for simulations does not seem to influence individual and team learning. Department-based local simulation, such as simulation in-house and especially in situ simulation, leads to gains in organisational learning. The overall objectives of simulation-based education and factors such as feasibility can help determine choice of simulation setting.

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)0.002
metaresearch head score (Gemma)0.081
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
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 score0.544
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.081
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0020.001
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 head0.037
GPT teacher head0.441
Teacher spread0.404 · 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