“Building the plane as you fly”: Simulation during the COVID-19 pandemic
Bibliographic record
Abstract
The 2019 coronavirus disease (COVID-19) pandemic is challenging our Canadian emergency departments (EDs) in unparalleled ways. As part of the frontline response, EDs have had to adapt to the unique clinical difficulties associated with the constant threat of COVID-19, developing protocols and pathways in the setting of limited and evolving information. In addition to the disruption of routine clinical care practices, an underlying perception of danger has resulted in a challenging clinical environment in which to make time-sensitive, high-stakes decisions. This has created an urgent need for targeted and adaptive training for all members of the emergency medicine healthcare team. The following commentary reflects the perspective of four emergency medicine simulation educators during the Canadian response to COVID-19. Recognizing that local needs and resources will vary, we highlight three key roles that simulation can play in the adaptive response to COVID-19: protocol development and system testing, provider education, and team-based training. The disruption to our practice as a result of COVID-19 has required us to "build the plane as we fly," and we believe simulation to be a key tool in this process.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".