Implementation models and frameworks used to guide community-based physical activity programs for children: a scoping review
Bibliographic record
Abstract
Abstract Background The implementation of community-based programs is key to effective, sustainable initiatives that can support population-level changes in children’s physical activity. The purpose of this scoping review was to explore the implementation models and frameworks used to develop (process models), explore (determinant frameworks), and/or evaluate (evaluation frameworks) community-based physical activity programs for children. Also, the foundational components of the implementation models and frameworks and practical application in real-world settings were described. Methods The methodological framework developed by Arksey and O’Malley (2005) and the updated recommendations from Levac, Colquhoun and O’Brien (2010) were used to search, identify, and summarize applicable studies. This review also met the requirements in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Scoping Reviews Checklist (PRISMA-ScR). A detailed search of six databases and three academic journals was conducted. Information about the article, the program, and the implementation model/framework were extracted and summarized. Results The search retrieved 42,202 articles, of which 27 met the inclusion criteria. Eleven process models, one determinant framework, and two evaluation frameworks were identified. Nineteen components were developed from the models and frameworks. Tailoring, situational analysis, and element identification were common components among the identified models and frameworks. Conclusions Since the execution of interventions is vital for creating successful health-promoting initiatives, researchers and program developers should consider using implementation models and frameworks to guide their community-based physical activity programs. Further research examining the application of new and existing implementation models and frameworks in developing, exploring, and evaluating community-level programs is warranted.
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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 | 0.130 | 0.253 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.013 |
| Bibliometrics | 0.033 | 0.028 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| 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".