Preparing for resilience — Si Vis Pacem, Para Bellum
Bibliographic record
Abstract
Canada has faced numerous public health challenges but remains inadequately prepared for future crises. For example, despite extensive reports and plans following the 2003 SARS-CoV-1 outbreaks, the country was unprepared for COVID-19 and lessons learned from the pandemic emphasized the need for immediate action to enhance preparedness. In the current era of poly-crisis, Canada must be ready for diverse challenges, including potential conflicts and their impacts on public health and health systems. The conflict in Ukraine highlights the need for extensive medical resources for returnees, which could strain public health and health systems alongside other concurrent threats. Exercise Trillium Cura (ETC) in 2024 simulated Ontario's health system response to a prolonged conventional war, revealing both successes and challenges. Key issues included leadership and resource needs, with recommendations for specific actions like creating a repatriation hub and a trauma registry. ETC emphasized a "whole of society" approach, engaging civil society in planning and highlighting the importance of integrated preparedness. Tabletop exercises like ETC are crucial for building relationships, shared learning, and innovative solutions. They help prepare for complex crises by fostering collaboration and readiness. Regular exercises are recommended to enhance preparedness and resilience, ensuring effective responses to future health emergencies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.004 |
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".