Agiles, efficients et en évolution : la réponse rapide des Centres de collaboration nationale à la COVID-19 au Canada
Bibliographic record
Abstract
Résumé Depuis décembre 2019, on assiste à une explosion mondiale de la recherche sur la COVID-19. Au Canada, les six Centres de collaboration nationale (CCN) en santé publique constituent l’un des piliers de la prise de décisions informées par les données probantes, car ils recueillent, résument et traduisent les connaissances émergentes. Financés par l’Agence de la santé publique du Canada et répartis sur le territoire, ils favorisent et soutiennent l’utilisation des résultats de la recherche scientifique et d’autres connaissances pour renforcer les pratiques, les programmes et les politiques en santé publique. Cet article fournit un aperçu de la manière dont les CCN participent à la mobilisation des connaissances en santé publique au Canada, met en évidence leur contribution à la lutte contre la COVID-19 et décrit les nombreuses difficultés rencontrées.
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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 | 0.050 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".