Implementation Strategies to Support Built Environment Approaches in Community Settings
Bibliographic record
Abstract
BackgroundBuilt environment approaches are recommended to improve population physical activity levels. Implementation strategies are needed to improve uptake, but little is known about effective strategies to translate research to practice in community settings.PurposeInform implementation strategies through understanding delivery agents’ perceptions of (1) built environment approaches, (2) a toolkit developed to support implementation, and (3) other required implementation strategies.MethodA toolkit was developed to detail the process of partnering to change the built environment and provide examples of built environment approaches (e.g., walking paths, traffic calming). Data were collected through focus groups (N = 3) with Extension Agents (n = 46) in 2020. The semi-structured focus group script was based on the Consolidated Framework for Implementation Research and the Technology Acceptance model. Rapid content analysis techniques and a deductive, grounded theory approach were used to interpret the data. Results. Focus groups generated meaning units coded into themes of perceptions of the intervention (subthemes: barriers, resources needed, and facilitators) and perceptions of the toolkit (subthemes: components to add, positive perceptions, and helpful components). The most common resources needed were coalition guidance and funding.ConclusionAgents experience barriers and facilitators to implementing built environment approaches and have specific needs for support. Based on the results, we created implementation strategies: (1) Places for Physical Activity toolkit, (2) Coalition Coaching, and (3) Mini-Grants. Future work is needed to investigate the effectiveness of these implementation strategies.
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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 | 0.030 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".