The National Collaborating Centre for Healthy Public Policy in times of COVID-19: Building skills to “Build Back Better”
Bibliographic record
Abstract
This article, the second in a series on the six National Collaborating Centres for Public Health, focuses on the National Collaborating Centre for Healthy Public Policy (NCCHPP), a centre of expertise, and knowledge synthesis and sharing that supports public health actors in Canada in their efforts to develop and promote healthy public policy. The article briefly describes the NCCHPP's mandate and programming, noting some of the resources that are particularly relevant in the current coronavirus disease 2019 (COVID-19) context. It then discusses how the NCCHPP's programming has been adapted to meet the changing needs of public health actors throughout the pandemic. These needs have been strongly tied to decisions aimed at containing the spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and mitigating its immediate impacts in various societal sectors since the beginning of the crisis. Needs have also gradually emerged related to how public health is expected to help inform the development of public policies that will allow us to "build back better" societies as we recover from the pandemic. The article concludes by discussing the orientation of the NCCHPP's future work as we emerge from the COVID-19 crisis.
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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 | 0.048 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.028 | 0.025 |
| Scholarly communication | 0.023 | 0.010 |
| Open science | 0.007 | 0.025 |
| Research integrity | 0.015 | 0.026 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".