Design of simulation-based medical education and advantages and disadvantages of in situ simulation versus off-site simulation
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".