Development of the Physical Activity Research Opportunities (PARO) framework
Bibliographic record
Abstract
PURPOSE: Physical activity is beneficial across the lifespan, but most Americans do not meet physical activity guidelines. Multiple sources exist that identify opportunities to address gaps in physical activity research knowledge and implementation. Several of these opportunities have important considerations for populations experiencing health inequities. The goal of this study was to identify, synthesize, and categorize opportunities for research (i.e., systematic investigations to develop generalizable knowledge) into a conceptual framework to advance physical activity research in a cohesive and efficient manner. METHODS: The National Collaborative on Childhood Obesity Research convened subject matter experts to conduct five qualitative document analysis steps: (1) identify sources published by United States government, intergovernmental, or national non-profit organizations related to physical activity, (2) review sources to extract research opportunities, (3) code the opportunities by variables (translational research phase, social ecological level, setting, and priority population) determined by the expert group, (4) synthesize data on similar opportunities, and (5) review crosstabulation data to examine coding patterns and develop a framework. RESULTS: Opportunities (n = 385) were extracted from sources (n = 11) and combined into condensed opportunity statements (n = 87). Most called for effectiveness research (n = 44, 51%) or dissemination and implementation science research (n = 14, 16%). 38% were related to policy, systems, and environmental interventions (n = 33), and 70% mentioned community settings (n = 61). Additionally, 76% did not include health equity considerations (n = 66), and 75% mentioned no specific population or populations across the lifespan (n = 65). The resultant Physical Activity Research Opportunities (PARO) framework details opportunities by translational research phase (methods/measures development, etiology, efficacy, effectiveness, dissemination and implementation, and surveillance) and social ecological level (individual or interpersonal, policy/systems/environmental, and crosscutting), including health equity considerations. CONCLUSIONS: The resultant PARO framework highlights gaps in current evidence and reveals opportunities for physical activity funders, researchers, policymakers, and practitioners to strategically advance their work. There are prospects for designing efficacy and effectiveness trials with an eye toward dissemination and implementation, developing strategies for improving dissemination and implementation, and using community- and practitioner-engaged approaches across translational research phases to advance health equity. Health equity can also be addressed by tailoring interventions, enhancing reach to priority populations, and improving social determinants of health.
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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 | 0.158 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.019 | 0.012 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".