The federal government and Canada's COVID-19 responses: from ‘we're ready, we're prepared’ to ‘fires are burning’
Bibliographic record
Abstract
Canada's experience with the coronavirus disease-2019 (COVID-19) pandemic has been characterized by considerable regional variation, as would be expected in a highly decentralized federation. Yet, the country has been beset by challenges, similar to many of those documented in the severe acute respiratory syndrome outbreak of 2003. Despite a high degree of pandemic preparedness, the relative success with flattening the curve during the first wave of the pandemic was not matched in much of Canada during the second wave. This paper critically reviews Canada's response to the COVID-19 pandemic with a focus on the role of the federal government in this public health emergency, considering areas within its jurisdiction (international borders), areas where an increased federal role may be warranted (long-term care), as well as its technical role in terms of generating evidence and supporting public health surveillance, and its convening role to support collaboration across the country. This accounting of the first 12 months of the pandemic highlights opportunities for a strengthened federal role in the short term, and some important lessons to be applied in preparing for future pandemics.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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 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".