Achieving Reliable Mastery of Emergency Airway Management Skills Through 4-Component Instructional Design
Bibliographic record
Abstract
INTRODUCTION: We used cognitive load theory to design the Queen's University Airway Mastery (QUMAC) pilot course to work toward reliable mastery of Emergency Airways Management elements in all participants. METHODS: We describe the process of designing QUMAC using 4-Component Instructional Design to harness the cognitive load theory as a learning tool. We evaluated the effectiveness of QUMAC using an outcome-based mixed-methods approach including Objective Structured Assessment of Technical Skills (OSATS) and 2 Objective Structured Clinical Examinations (OSCEs) at course completion using blinded expert video review. We also conducted semistructured interviews at course completion and after 6 months of independent practice. Interviews were analyzed thematically. RESULTS: Mean OSCE Global Performance Scores were 4.1 (±0.56) of 5 for both OSCE scores; and 4.0-4.4 (±0.48-0.89) on OSATS. At course completion, 4 themes were identified: Overall Experience with the Course, Facilitators of Performance, Recommendations, and Transfer to Practice. At 6 months of independent practice 5 themes emerged: Level of Confidence, Management of Cognitive Load, Persistence, Barriers to Application, and Recommendations. CONCLUSIONS: All participants demonstrated a high degree of competence when assessed by OSCEs and majority did so with the OSATS. All noticed an increase in confidence and reduced cognitive load while managing airways. These persisted over 6 months of independent practice where the participants were actively managing airways as staff physicians in new workplaces. High performance expectations, automation, schemas, spaced repetition, and homework were the elements most associated with better performance and more confidence. Decreased cognitive load freed up resources for higher order thinking, while the overall sense of competence reduced the anxiety of going to work as a new emergency department staff.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".