Knowledge Translation Platform Increasing Use of Research Evidence in Physical Activity Policy Making - A Case Study in Finland
Bibliographic record
Abstract
BACKGROUND: Knowledge Translation Platform (KTP) in partnerships between policymakers, stakeholders, and researchers was established in order to enhance evidence-informed policymaking on physical activity. The article aims to give answers to specific questions, such as what were the main knowledge translation tools to improve access to research evidence in physical activity policy in Finland; which factors facilitated the improvements in use of research evidence, and what kind of procedures were implemented to improve the use of research evidence in policy making.METHODS: The study triangulated qualitative data from documents, reviews and observations of meetings between 2012 and 2013. Purposive sampling of meeting documents was used and data was analysed using a thematic content analysis of documents.RESULTS: KTP contributed to an increased awareness of the importance of the use of research evidence in physical activity policymaking, and strengthened relationships between policymakers, stakeholders and researchers. Support from policymakers and professionals as well as a window of opportunity facilitated KTP activities. Based on the KTP experience, institutionalization within the government could help to keep the use of research evidence high on the agenda.CONCLUSIONS: The case study provided unique insights into what counts for developing use of research evidence in policymaking. The expectations of the public policy were to give a larger role to evidence-informed policymaking, but expectations conflicted between the interests of various stakeholders. The establishment of KTP was a promising development in supporting the use of research evidence in physical activity policymaking. Real-time lesson drawing from the experiences of KTP can support improvements in the functioning of KTP in the short term, while making the case for sustaining their work in the long term.
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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 | 0.092 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.018 | 0.012 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.008 | 0.005 |
| 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".