The value of program science to optimize knowledge brokering on infectious diseases for public health
Bibliographic record
Abstract
BACKGROUND: Knowledge translation (KT) and related terms have variously been defined as process and as products. In this paper we contribute to debates on effective KT, specifically knowledge brokering, by describing an adaptation of Program Science that aligns with the real-world of public health activities. We describe an adaptation of the Program Science framework to our knowledge translation and brokering planning and projects at the National Collaborating Centre for Infectious Diseases. The systematic approach allows for layering of knowledge year to year and translating knowledge from one infectious disease content area to another. Using a recent forum on syphilis outbreaks as an example, we also demonstrate the value of using Program Science to shape the design and delivery of the knowledge brokering event. CONCLUSION: The use of scientific knowledge to improve public health program design, implementation and evaluation forms the basis for the program science framework. Providing the right public health information to the right audience at the right time can foster long-term outcomes of networks and new partnerships which can potentially improve delivery of public health services.
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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 | 0.132 | 0.200 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".