<p>Using High-Fidelity Simulation to Teach Ethics Related Non-Technical Skills: Description of an Innovative Model</p>
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".