The Use of In Situ Simulation to Enhance COVID-19 Pandemic Preparedness in Obstetrics
Bibliographic record
Abstract
Simulation's benefits in medical education are well established. However, its use for pandemic preparedness in obstetrics is lacking. Management of obstetrical patients with suspected COVID-19 infection is a complex task with safety considerations for mother, fetus and healthcare workers. Implementation of new workflow algorithms to ensure safety is critical but is challenging to implement in real-time. We sought to improve pandemic preparedness by designing and deploying a high-fidelity simulation exercise involving the admission of a labouring obstetrical patient with suspected COVID-19 into a labour room, urgent transfer to the operating room and neonatal resuscitation. The creation of the simulation scenario was a multi-disciplinary effort with input from a focus group of key clinical stakeholders from within and outside of our centre to ensure clinical validity. Simulations were performed on the clinical unit during regular work hours so workflow could be observed in real-time with access to the equipment and personnel in which this clinical scenario would occur. We completed a total of 11 simulation sessions involving 42 participants. Feedback, obtained from debrief sessions and anonymous surveys, was categorized based on the human factors framework, and used as part of an iterative process to adapt, revise and improve the simulation scenario. The result of this iterative process was the creation of validated departmental infection control protocols that continue to be implemented through the second wave of the COVID-19 pandemic.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".