Three types of scientific evidence to inform physical activity policy: results from a comparative scoping review
Bibliographic record
Abstract
OBJECTIVES: This paper presents a typology of available evidence to inform physical activity policy. It aims to refine the distinction between three types of evidence relating to physical activity and to compare these types for the purpose of clarifying potential research gaps. METHODS: A scoping review explored the extent, range and nature of three types of physical activity-related evidence available in reviews: (I) health outcomes/risk factors, (II) interventions and (III) policy-making. A six-step qualitative, iterative process with expert consultation guided data coding and analysis in EPPI Reviewer 4. RESULTS: 856 Type I reviews, 350 Type II reviews and 40 Type III reviews were identified. Type I reviews heavily focused on obesity issues (18 %). Reviews of a systematic nature were more prominent in the Type II (>50 %). Type III reviews tended to conflate research about policy intervention effectiveness and research about policymaking processes. The majority of reviews came from the United States, United Kingdom, Australia and Canada. CONCLUSIONS: Although evidence gaps exist regarding evidence Types I and II, the most prominent gap regards Type III, i.e. research pertaining to physical activity policymaking. The findings presented herein will be used to inform physical activity policy development and future research.
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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 | 0.201 | 0.493 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.014 |
| Bibliometrics | 0.056 | 0.054 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".