Promoting Physical Activity Policy: The Development of the MOVING Framework
Bibliographic record
Abstract
BACKGROUND: Considering the large health burden of physical inactivity, effective physical activity promotion is a "best buy" for noncommunicable disease and obesity prevention. The MOVING policy framework was developed to promote and monitor government policy actions to increase physical activity as part of the EU Horizon 2020 project "Confronting Obesity: Co-creating policy with youth (CO-CREATE)." METHOD: A scanning exercise, documentary review of key international policy documents, and thematic analysis of main recommendations were conducted. Themes were reviewed as part of a consultation with physical activity experts. RESULTS: There were 6 overarching policy framework areas: M-make opportunities and initiatives that promote physical activity in schools, the community, and sport and recreation; O-offer physical activity opportunities in the workplace and training in physical activity promotion across multiple professions; V-visualize and enact structures and surroundings that promote physical activity; I-implement transport infrastructure and opportunities that support active societies; N-normalize and increase physical activity through public communication that motivates and builds behavior change skills; and G-give physical activity training, assessment, and counseling in health care settings. CONCLUSIONS: The MOVING framework can identify policy actions needed, tailor options suitable for populations, and assess whether approaches are sufficiently comprehensive.
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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 | 0.079 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".