Multidisciplinary, multisite trauma team training during COVID-19: lessons from the first virtual E-S.T.A.R.T.T. course
Bibliographic record
Abstract
Trauma care delivery is a complex team-based task that requires deliberate practice. The COVID-19 pandemic has not diminished the importance of excellent trauma team dynamics. However, the pandemic hampers our ability to gather safely and train together. A mitigating solution is the provision of high-fidelity simulation training in a virtual setting. The Simulated Trauma and Resuscitation Team Training (S.T.A.R.T.T.) course has provided multidisciplinary trauma team members with skills in crisis resource management (CRM) for nearly 10 years. It has promoted collaborative learning from coast to coast, as the course typically runs at our national surgical and trauma meetings. In response to COVID-19 challenges, the course content has been modified to virtually connect 2 centres in different provinces simultaneously. High participant satisfaction suggests that the new virtual E-S.T.A.R.T.T course is able to continue to help providers develop important CRM skills in a multidisciplinary setting while remaining compliant with COVID-19 safety precautions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".