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 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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".