Using High-Fidelity Simulation to Teach Ethics Related Non-Technical Skills: Description of an Innovative Model
Bibliographic record
Abstract
Issam Tanoubi, L Mihai Georgescu, Arnaud Robitaille, Pierre Drolet, Roger Perron Department of Anesthesiology, Centre d’apprentissage des Attitudes et Habiletés Cliniques (CAAHC), Université de Montréal, Faculty of Medicine, Montreal, Quebec, CanadaCorrespondence: Issam TanoubiMD, MA(Ed), DESAR, Department of Anesthesiology, Centre d’apprentissage des Attitudes et Habiletés Cliniques (CAAHC), Université de Montréal, Faculty of Medicine, Montreal, Quebec, CanadaEmail i.tanoubi@umontreal.caAbstract: This article describes a high-fidelity (Hi-Fi) simulation-based innovative educational strategy intended to introduce anesthesiology residents to key ethical considerations and how they apply to their practice. Three Hi-Fi simulation scenarios involving situations with various ethical issues are described with their debriefing objectives and the trainees’ subjective feedback. Three high-fidelity simulation scenarios are described: (a) teaching critical incident disclosure, (b) disclosing and discussing patient awareness during general anesthesia, and (c) would physicians override a do-not-resuscitate (DNR) order if the cause of a cardiac arrest is iatrogenic? We used Hi-Fi simulation in an innovative way to teach these principles of ethics. Simulation, through carefully crafted debriefing, can contribute to the acquisition of essential non-technical ethical skills. How best to integrate simulation in an existent ethics curriculum and how it compares with more traditional teaching methods are questions that need to be addressed.Keywords: high-fidelity simulation, ethics principles education, ethic scenario, debriefing
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".