Nimble, efficient and evolving: the rapid response of the National Collaborating Centres to COVID-19 in Canada
Bibliographic record
Abstract
Since December 2019, there has been a global explosion of research on COVID-19. In Canada, the six National Collaborating Centres (NCCs) for Public Health form one of the central pillars supporting evidence-informed decision making by gathering, synthesizing and translating emerging findings. Funded by the Public Health Agency of Canada and located across Canada, the six NCCs promote and support the use of scientific research and other knowledges to strengthen public health practice, programs and policies. This paper offers an overview of the NCCs as an example of public health knowledge mobilization in Canada and showcases the NCCs' contribution to the COVID-19 response while reflecting on the numerous challenges encountered.
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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 | 0.033 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.031 | 0.015 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 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".