Propagating Change: Using RE-FRAME to Scale and Sustain A Community-Based Childhood Obesity Prevention Initiative
Bibliographic record
Abstract
Sustainable Childhood Obesity Prevention through Community Engagement (SCOPE) has developed Live 5-2-1-0, a multi-sectoral, multi-component community-based childhood obesity prevention initiative grounded in systems thinking and participatory research principles. Building on previous work, this study continued implementation of an innovative knowledge exchange model, RE-FRAME, in two 'new' and two 'existing' Live 5-2-1-0 communities. This mixed-methods study examined follow-up data to determine the nature and extent of the model's ability to build and sustain community capacity and facilitate the scale-up and sustainability of systems- and community-level change. Qualitative and quantitative data were collected using stakeholder interviews, and quantitative process tracking (PTT) and capacity building tools (CCBT), and were analyzed using thematic analysis and descriptive statistics, respectively. Results from three communities with baseline and follow-up CCBT data showed capacity domain scores increased (15/27; 56%) or remained constant (10/27; 37%) over the study period. PTT data showed over 50 multi-sectoral community partnerships formed in Community D (new) and 108 actions implemented. Stakeholder interviews identified having a common cause, co-ownership, champion networks and consistency of the Live 5-2-1-0 message as essential to sustainability of the initiative. RE-FRAME supports knowledge exchange and community capacity-building that is integral to initiating and sustaining a community-based childhood obesity prevention initiative.
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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 | 0.047 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".