Integrating research evidence and physical activity policy making—REPOPA project
Bibliographic record
Abstract
Evidence shows that regular physical activity is enhanced by supporting environment. Studies are needed to integrate research evidence into health enhancing, cross-sector physical activity (HEPA) policy making. This article presents the rationale, study design, measurement procedures and the initial results of the first phase of six European countries in a five-year research project (2011-2016), REsearch into POlicy to enhance Physical Activity (REPOPA). REPOPA is programmatic research; it consists of linked studies; the first phase studied the use of evidence in 21 policies in implementation to learn more in depth from the policy making process and carried out 86 qualitative stakeholder interviews. The second, ongoing phase builds on the central findings of the first phase in each country; it consists of two sets of interventions: game simulations to study cross-sector collaboration and organizational change processes in the use of evidence and locally tailored interventions to increase knowledge integration. The results of the first two study phases will be tested and validated among policy makers and other stakeholders in the third phase using a Delphi process. Initial results from the first project phase showed the lack of explicit evidence use in HEPA policy making. Facilitators and barriers of the evidence use were the availability of institutional resources and support but also networking between researchers and policy makers. REPOPA will increase understanding use of research evidence in different contexts; develop guidance and tools and establish sustainable structures such as networks and platforms between academics and policy makers across relevant sectors.
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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 | 0.190 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.006 | 0.028 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".