Accelerating knowledge translation to improve cardiovascular outcomes and health services: opportunities for bridging science and clinical practice
Bibliographic record
Abstract
Knowledge translation (KT) is the exchange between knowledge producers and users to understand, synthesize, share, and apply evidence to accelerate the benefits of research to improve health and health systems. Knowledge translation practice (activities/strategies to move evidence into practice) and KT science (study of the methodology and approaches to promote the uptake of research) benefit from the use of conceptual thinking, the meaningful inclusion of patients, and the application of intersectionality. In spite of multiple barriers, there are opportunities to develop strong partnerships and evidence to drive an impactful research agenda and increase the uptake of cardiovascular research.
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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 | 0.263 | 0.354 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.023 | 0.030 |
| Open science | 0.005 | 0.037 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.020 | 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".