Interdisciplinary Crisis Resource Management Training: How Do Otolaryngology Residents Compare? A Survey Study
Bibliographic record
Abstract
OBJECTIVE: Emergent medical crises, such as acute airway obstruction, are often managed by interdisciplinary teams. However, resident training in crisis resource management traditionally occurs in silos. Our objective was to compare the current state of interdisciplinary crisis resource management (IDCRM) training of otolaryngology residents with other disciplines. METHODS: A survey study examining (1) the frequency with which residents are involved in interdisciplinary crises, (2) the current state of interdisciplinary training, and (3) the desired training was conducted targeting Canadian residents in the following disciplines: otolaryngology, anesthesiology, emergency medicine, general surgery, obstetrics and gynecology, internal medicine, pediatric emergency medicine, and pediatric/neonatal intensive care. RESULTS: A total of 474 surveys were completed (response rate, 12%). On average, residents were involved in 13 interdisciplinary crises per year. Only 8% of otolaryngology residents had access to IDCRM training, as opposed to 66% of anesthesiology residents. Otolaryngology residents reported receiving an average of 0.3 hours per year of interdisciplinary training, as compared with 5.4 hours per year for pediatric emergency medicine residents. Ninety-six percent of residents desired more IDCRM training, with 95% reporting a preference for simulation-based training. DISCUSSION: Residents reported participating in crises managed by interdisciplinary teams. There is strong interest in IDCRM and crisis resource management training; however, it is not uniformly available across Canadian residency programs. Despite their pivotal role in managing critical emergencies such as acute airway obstruction, otolaryngology residents received the least training. IMPLICATION: IDCRM should be explicitly taught since it reflects reality and may positively affect patient outcomes.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".