Practical insights for regional multi-sectoral exercise planning: The Greater Toronto experience
Bibliographic record
Abstract
Exercise GTA Unified was a functional, multi-agency, cross-jurisdictional, health-sector focused mass casualty preparedness exercise conducted in the Greater Toronto Area (GTA) on 28th November, 2019. With over 1,000 unique paper-based and electronic injects and 34 participating agencies, including 22 separate hospital sites, Exercise GTA Unified is likely the largest health-sector focused mass casualty preparedness exercise ever conducted in Canada. The exercise design approach supported a successful, objective-based functional exercise, with elements of marked realism for participants. The exercise offered a unique opportunity to collect data for future analysis and the insights gained will have a transformative impact on interagency engagement and cooperation for emergency response planning. Furthermore, the approach adopted for the exercise is affordable, reproducible, scalable and transferrable to sectors beyond the health system. This paper provides a detailed review of the key planning and design components adopted in the development and implementation of the exercise, as well as practical insights for the design and conduct of multi-agency, cross-jurisdictional functional exercises.
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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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".