MétaCan
Menu
Back to cohort
Record W2098945042 · doi:10.1186/1748-5908-9-28

How research funding agencies support science integration into policy and practice: An international overview

2014· review· en· W2098945042 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueImplementation Science · 2014
Typereview
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversité de MontréalÉcole Nationale d'Administration Publique
Fundersnot available
KeywordsHealth services researchPublic relationsScience policyHealth policyHealth administrationHealth informaticsPolitical sciencePublic administrationPublic healthMedicineNursing

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.083
metaresearch head score (Gemma)0.058
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesMetaresearch, Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.962
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0830.058
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.010
Science and technology studies0.0080.004
Scholarly communication0.0020.012
Open science0.0030.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.946
GPT teacher head0.851
Teacher spread0.095 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it