Disaster Management Simulation–A Novel Virtual Exercise
Bibliographic record
Abstract
Introduction: Disaster management and emergency preparedness relies on the collaboration, communication, and expertise of a multidisciplinary team. Skills in preparation, communication, and management of disasters are core competencies of an emergency physician. To learn the principles of disaster management, simulations are critical as mass casualty/rapid surge events seldom occur. The COVID-19 pandemic resulted in the cancellation of in-person events. In response to these restrictions, the University of Toronto, EM Program developed a successful virtual interprofessional mass casualty simulation. Method: The novel online simulation event was piloted in 2021 and ran for three-hours. The exercise focused on developing soft skills (e.g., communication, team-work, and debriefing) and hard skills (e.g., triage, casualty distribution, and activation of plans). Groups were composed of members of each post-graduate year to facilitate near-peer learning. A total of six groups were formed: Adult, Children, Community Hospitals, EMS, Government, and Media. Each Team used multiple communication tools (i.e., Whatsapp groups, Zoom breakout rooms, Shared Google Documents) to swiftly pivot and manage a mass casualty event. Post-exercise debriefing and anonymous evaluations were gathered. Results: A total of 28-residents (nine PGY1, ten PGY2, and eight PGY3 learners) and 11-staff observers participated (25-respondents). Nineteen participants rated the simulation exercise as excellent and six as “very good”. Twenty participants rated the workshop as “very useful” and five as “useful”. Positive feedback centered around content applicability, exercise creativity, level of engagement, and learning value. Constructive feedback included the need for more pre-exercise orientation time, increasing disaster management time, and inviting allied-health staff. Conclusion: There is a clear need for EM residents to learn and develop skills related to disaster management and emergency preparedness. This exercise showed that disaster management and emergency preparedness competencies can be learned in a virtual format. This virtual format has encouraged its continuation and further inspired the curation of a four-year program.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".