Teaching airway teachers: a post-course quantitative and qualitative survey
Bibliographic record
Abstract
BACKGROUND: Airway management is a crucial skill for many clinicians. Besides mastering the technical skills of establishing a patent airway, human factors including leadership and team collaboration are essential. Teaching these human factors is often challenging for instructors who lack dedicated training. Therefore, the European Airway Management Society (EAMS) developed the Teach-the-Airway-Teacher (TAT) course. METHODS: This online post-course survey of TAT-course participants 2013-2021 investigated the impact of the TAT-course and the status of airway management teaching in Europe. Twenty-eight questions e-mailed to participants (using SurveyMonkey) assessed the courses' strengths and possible improvements. It covered participants' and workplace details; after TAT-course considerations; and specifics of local airway teaching. Data were assessed using Excel and R. RESULTS: Fifty-six percent (119/213) of TAT-participants answered the survey. Most were anaesthetists (84%), working in university level hospitals (76%). Seventy-five percent changed their airway teaching in some way, but 20% changed it entirely. The major identified limitation to airway teaching in their departments was "lack of dedicated resources" (63%), and the most important educational topic was "Teaching non-technical skills" (70%). "Lecturing " was considered less important (37%). Most surveyed anaesthesia departments lack a standardized airway teaching rotation. Twenty-one percent of TAT-participants rated their departmental level of airway teaching overall as inadequate. CONCLUSIONS: This survey shows that the TAT-course purpose was successfully fulfilled, as most TAT-course participants changed their airway teaching approach and did obtain the EAMS-certificate. The feedback provided will guide future TAT-course improvements to advance and promote a comprehensive approach to teaching airway management.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".