Stress to Success: Leveraging Crisis Resource Management to Enhance Non-Technical Skills in Anesthesia Training
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.
Bibliographic record
Abstract
Simulation has become a fundamental educational tool in medical training since the creation of the first simulator in the mid-20 th century.Initially focused on technical skills, the introduction of Anesthesia Crisis Resource Management (ACRM) in the 1980s shifted the focus to nontechnical skills responsible for much of the medical error, emphasizing crisis management, decision making, and team communication.The Anesthesiology Residency Program at the University of Toronto incorporates these principles to enhance the non-technical skills of anesthesia residents.The program features three main pillars: high-fidelity simulation, focus on anesthesia non-technical skills (ANTS), and debriefing.High-fidelity simulations expose residents to critical scenarios with a focus on improving clinical performance and reducing errors.ANTS training addresses situational awareness, teamwork, task management and decision making, all factors that play a critical role in patient safety and error reduction.Debriefing sessions, based on the PEARLS framework and the "debriefing with good judgment" model, promote reflective learning and cognitive reframing, allowing residents to hone their decision-making and emotional management in high-pressure situations.The program's consistency across the four years of residency ensures continued development of non-technical skills, enhancing competency and readiness for national assessments such as the Canadian National Anesthesia Simulation Curriculum (CanNASC).This structured, longitudinal, crisis-focused approach can serve as a global benchmark.By aligning with the CanMEDS framework and national standards, the program exemplifies the effectiveness of simulation-based education in preparing the next generation of anesthesiologists.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it