The Secret Sauce? Taking the Mystery Out of Scaling-Up School-Based Physical Activity Interventions
Bibliographic record
Abstract
Over the last 4 decades, physical activity researchers have invested heavily in determining "what works" to promote healthy behaviors in schools. Single and multicomponent school-based interventions that target physical education, active transportation, and/or classroom activity breaks effectively increased physical activity among children and youth. Yet, few of these interventions are ever scaled-up and implemented under real-world conditions and in diverse populations. To achieve population-level health benefits, there is a need to design school-based health-promoting interventions for scalability and to consider key aspects of the scale-up process. In this opinion piece, we aim to identify challenges and advance knowledge and action toward scaling-up school-based physical activity interventions. We highlight the key roles of planning for scale-up at the outset, scale-up pathways, trust among partners and program support, program adaptation, evaluation of scale-up, and barriers and facilitators to scaling-up. We draw upon our experience scaling-up effective school-based interventions and provide a solid foundation from which others can work toward bridging the implementation-to-scale-up gap.
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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 | 0.234 | 0.340 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.009 | 0.021 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 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".