Funding change: An environmental scan of research funders’ knowledge translation strategic plans and initiatives across 10 high-income countries/regions
Bibliographic record
Abstract
Knowledge translation (KT) is the science and practice of dissemination and implementation of evidence. We describe how research funders operationalize and evaluate KT initiatives, identify challenges and opportunities, and suggest strategic considerations for KT support. We conducted an environmental scan, which included a systematic search of published and grey literature and a focus group with Canadian funders. A total of 130 published articles and 2415 grey literature sources were screened; 212 unique data sources were included. Published literature commonly described KT initiatives related to “KT practice and science funding.” These initiatives commonly provided funds for infrastructure development (e.g., clinical technologies, database subscriptions) to facilitate translational or applied research to address regional health priorities. Of the articles, 44% outlined an evaluation plan; few provided validated KT metrics. In the grey literature, 364 initiatives were described; the most commonly described initiatives related to “exchange and integrated KT.” Focus group participants hoped to see increased resources to support KT, exchange opportunities with policy/decision-makers, and evaluate KT initiatives. Funders completed various KT initiatives, which tended to engage stakeholders to set research priorities, collaborate with a range of stakeholders, build KT capacity, and mandate KT requirements. We provide six considerations for funders to support KT.
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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 | 0.128 | 0.221 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.070 | 0.139 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".