Canada’s National Collaborating Centres: Facilitating evidence-informed decision-making in public health
Bibliographic record
Abstract
Although evidence-informed decision-making is fundamental to public health, it is challenging in practice as there is a continual burgeoning of both evidence and emerging issues, which public health professionals need to address at local, regional and national levels. One way that Canada has addressed this perennial challenge is through its six National Collaborating Centres (NCCs). The NCCs for Public Health were created to promote and support the use of scientific research and other knowledge to strengthen public health practice, programs and policies in Canada. The NCCs identify knowledge gaps, foster networks across sectors and jurisdictions and provide the public health system with an array of evidence-informed resources and knowledge translation services. Each centre is hosted in academic or government organizations across Canada and focuses on a specific public health priority: Determinants of Health; Environmental Health; Healthy Public Policy; Indigenous Health; Infectious Diseases; and Knowledge Translation Methods and Tools. Since their launch in 2005, the NCCs have undergone two federal evaluations, the results of which clearly demonstrate their significant contribution to evidence-informed decision-making in public health in Canada, while identifying some opportunities for future growth. The NCCs successfully help to bridge the gaps between evidence, policy and practice and facilitate the implementation of evidence in multiple, often complex, settings.
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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 | 0.143 | 0.222 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.020 | 0.008 |
| Open science | 0.007 | 0.033 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.027 | 0.008 |
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".