Mobilization of science advice by the Canadian federal government to support the COVID-19 pandemic response
Bibliographic record
Abstract
The procurement and provision of expert-driven, evidence-informed, and independent science advice is integral to timely decision-making during public health emergencies. The 2019 coronavirus disease (COVID-19) pandemic has underscored the need for sound evidence in public health policy and exposed the challenges facing government science advisory mechanisms. This paper is a jurisdictional case study describing (i) the federal science advice bodies and mechanisms for public health in Canada (i.e., the federal science advice "ecosystem"); and (ii) how these bodies and mechanisms have mobilized and evolved to procure expertise and evidence to inform decisions during the first two years of the COVID-19 pandemic. We reviewed publicly accessible Government of Canada documents, technical reports, and peer-reviewed articles available up to December 2021. Canada's federal landscape of science advisory bodies for public health within the Health Portfolio was largely shaped by Canada's experiences with the 2003 severe acute respiratory syndrome and 2009 H1N1 outbreaks. In parallel, Canada has a designated science advisory apparatus that has seen frequent reforms since the early 2000s, with the current Office of the Chief Science Advisor created within the Science Portfolio in 2018. The COVID-19 pandemic has further complicated Canada's science advice ecosystem, with involvement from departments, expert advisory groups, and partnerships within both the federal Health and Science Portfolios. Although the engagement of federal departments outside the health sector is promising, the COVID-19 experience in Canada supports the need to institutionalize science advisory bodies for public health to improve pandemic preparedness and ensure rapid mobilization of well-coordinated and independent advice in future emergencies. This review also identified pressing areas for further inquiry to strengthen science advice for public health in Canada, including to assess the independence of science advisory actors and the interaction between federal and subnational authorities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.037 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.038 | 0.013 |
| Scholarly communication | 0.022 | 0.005 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".