Translating active living research into policy and practice: One important pathway to chronic disease prevention
Bibliographic record
Abstract
Global concerns about rising levels of chronic disease make timely translation of research into policy and practice a priority. There is a need to tackle common risk factors: tobacco use, unhealthy diets, physical inactivity, and harmful alcohol use. Using evidence to inform policy and practice is challenging, often hampered by a poor fit between academic research and the needs of policymakers and practitioners--notably for active living researchers whose objective is to increase population physical activity by changing the ways cities are designed and built. We propose 10 strategies that may facilitate translation of research into health-enhancing urban planning policy. Strategies include interdisciplinary research teams of policymakers and practitioners; undertaking explicitly policy-relevant research; adopting appropriate study designs and methodologies (evaluation of policy initiatives as 'natural experiments'); and adopting dissemination strategies that include knowledge brokers, advocates, and lobbyists. Conducting more policy-relevant research will require training for researchers as well as different rewards in academia.
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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 | 0.277 | 0.395 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.035 | 0.034 |
| Open science | 0.009 | 0.034 |
| Research integrity | 0.023 | 0.026 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
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".