Lessons Learned: A Decade of Implementing an Interdisciplinary Airway Training Simulation Module
Bibliographic record
Abstract
Importance Nontechnical skills are crucial in delivering critical and urgent patient care. Through our simulation module, we gear residents and interprofessional personnel with the knowledge and skills necessary to tackle complex airway emergencies and limit human error. Objective Develop, implement, adapt, and evaluate a novel interdisciplinary and interprofessional crisis resource management (CRM) simulation module for the management of complex airways. Design Simulation-Based Quality Improvement Project. Setting McGill University’s Arnold and Blema Steinberg Medical Simulation Center and a variety of hospital environments at the McGill University Health Centre in Montreal, Quebec, Canada. Participants 138 residents (otolaryngology, anesthesia, pediatric emergency medicine) and allied healthcare professionals (nurses and respiratory therapists) participated in 20 unique scenarios. Intervention or Exposures From 2012 to 2022, modules occurred from 4 to 6 half days per year, structured as 3 to 4 simulation scenarios, each followed by debriefing sessions. Main Outcome Measures Participants completed self-assessment forms evaluating module satisfaction, CRM skill development, and narrative commentary. Quantitative and qualitative data were obtained and analyzed. Results Participants reported a significant perceived increase ( P < .05) in all nontechnical CRM skills. Participants without previous CRM training reached comparable levels in CRM skills to those with such training. Increasing involvement of allied healthcare professionals, formal debriefing focused on role clarity, and increasing complexity of scenarios are identified as key elements for stressing CRM skills and consolidating lessons learned. Conclusions This module is among the first of its kind in otolaryngology given its interprofessional, longitudinal, and evolving nature, while providing an opportunity for residents to develop nontechnical skills through simulation. Its interdisciplinary and interprofessional nature is a key element to its success. Relevance This module aims to translate into positive results in patient safety and patient outcomes in challenging airway management scenarios. Implementing modules as continued medical education may help maintain proficiency overtime.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".