How research funding agencies support science integration into policy and practice: An international overview
Bibliographic record
Abstract
BACKGROUND: Funding agencies constitute one essential pillar for policy makers, researchers and health service delivery institutions. Such agencies are increasingly providing support for science implementation. In this paper, we investigate health research funding agencies and how they support the integration of science into policy, and of science into practice, and vice versa. METHODS: We selected six countries: Australia, The Netherlands, France, Canada, England and the United States. For 13 funding agencies, we compared their intentions to support, their actions related to science integration into policy and practice, and the reported benefits of this integration. We did a qualitative content analysis of the reports and information provided on the funding agencies' websites. RESULTS: Most funding agencies emphasized the importance of science integration into policy and practice in their strategic orientation, and stated how this integration was structured. Their funding activities were embedded in the push, pull, or linkage/exchange knowledge transfer model. However, few program funding efforts were based on all three models. The agencies reported more often on the benefits of integration on practice, rather than on policy. External programs that were funded largely covered science integration into policy and practice at the end of grant stage, while overlooking the initial stages. Finally, external funding actions were more prominent than internally initiated bridging activities and training activities on such integration. CONCLUSIONS: This paper contributes to research on science implementation because it goes beyond the two community model of researchers versus end users, to include funding agencies. Users of knowledge may be end users in health organizations like hospitals; civil servants assigned to decision making positions within funding agencies; civil servants outside of the Ministry of Health, such as the Ministry of the Environment; politicians deciding on health-related legislation; or even university researchers whose work builds on previous research. This heterogeneous sample of users may require different user-specific mechanisms for research initiation, development and dissemination. This paper builds the foundation for further discussion on science implementation from the perspective of funding agencies in the health field. In general, case studies can help in identifying best practices for evidence-informed decision making.
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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 | 0.118 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.020 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.025 | 0.016 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".