Health Research Funding Agencies' Support and Promotion of Knowledge Translation: An International Study
Bibliographic record
Abstract
CONTEXT: The process of knowledge translation (KT) in health research depends on the activities of a wide range of actors, including health professionals, researchers, the public, policymakers, and research funders. Little is known, however, about health research funding agencies' support and promotion of KT. Our team asked thirty-three agencies from Australia, Canada, France, the Netherlands, Scandinavia, the United Kingdom, and the United States about their role in promoting the results of the research they fund. METHODS: Semistructured interviews were conducted with a sample of key informants from applied health funding agencies identified by the investigators. The interviews were supplemented with information from the agencies' websites. The final coding was derived from an iterative thematic analysis. FINDINGS: There was a lack of clarity between agencies as to what is meant by KT and how it is operationalized. Agencies also varied in their degree of engagement in this process. The agencies' abilities to create a pull for research findings; to engage in linkage and exchange between agencies, researchers, and decision makers; and to push results to various audiences differed as well. Finally, the evaluation of the effectiveness of KT strategies remains a methodological challenge. CONCLUSIONS: Funding agencies need to think about both their conceptual framework and their operational definition of KT, so that it is clear what is and what is not considered to be KT, and adjust their funding opportunities and activities accordingly. While we have cataloged the range of knowledge translation activities conducted across these agencies, little is known about their effectiveness and so a greater emphasis on evaluation is needed. It would appear that "best practice" for funding agencies is an elusive concept depending on the particular agency's size, context, mandate, financial considerations, and governance structure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Incentives · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Metaresearch Domain: Incentives · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.291 | 0.351 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.017 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.022 | 0.015 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".