The 2024 French guidelines for scenario design in simulation-based education: manikin-based immersive simulation, simulated participant-based immersive simulation and procedural simulation
Bibliographic record
Abstract
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.
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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 | 0.029 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.013 |
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".