Knowledge exchange strategies for interventions and policy in public health
Bibliographic record
Abstract
Promoting the use of research-based knowledge in public health becomes more complex when public health includes interventions on health determinants. This article examines strategies for knowledge synthesis, translation and exchange (KSTE) in the context of public health in Canada, making reference to the work of the recently established National Collaborating Centres for Public Health (NCCs). NCCs simultaneously pursue KSTE and study how KSTE strategies meet different needs. Because NCCs are focused on interventions and policies, they must address the relationship between knowledge and policy, and how amenable it is to change. KSTE can seek to respond to and inform an existing policy agenda, but it can also seek to shape, frame and change that agenda. The two paths might call for different approaches, and for expanding the boundaries of KSTE in health.
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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 | 0.163 | 0.163 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.011 | 0.039 |
| Scholarly communication | 0.023 | 0.034 |
| Open science | 0.005 | 0.027 |
| Research integrity | 0.013 | 0.008 |
| Insufficient payload (model declined to judge) | 0.021 | 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".