Wayfinding the Live 5-2-1-0 Initiative—At the Intersection between Systems Thinking and Community-Based Childhood Obesity Prevention
Bibliographic record
Abstract
Childhood obesity is complex and requires a 'systems approach' that collectively engages across multiple community settings. Sustainable Childhood Obesity Prevention through Community Engagement (SCOPE) has implemented Live 5-2-1-0-a multi-sector, multi-component childhood obesity prevention initiative informed by systems thinking and participatory research via an innovative knowledge translation (KT) model (RE-FRAME). This paper describes the protocol for implementing and evaluating RE-FRAME in two 'existing' (>2 years of implementation) and two 'new' Live 5-2-1-0 communities to understand how to facilitate and sustain systems/community-level change. In this mixed-methods study, RE-FRAME was implemented via online resources, webinars, a backbone organization (SCOPE) coordinating the initiative, and a linking system supporting KT. Qualitative and quantitative data were collected using surveys and stakeholder interviews, analyzed using thematic analysis and descriptive statistics, respectively. Existing communities described the consistency of Live 5-2-1-0 and extensive local partnerships/champions as catalysts for synergistic community-wide action; new communities felt that the simplicity of the message combined with the transfer of experiential learning would inform their own strategies and policies/programs to broadly disseminate Live 5-2-1-0. RE-FRAME effectively guided the refinement of the initiative and provided a framework upon which evaluation results described how to implement a community-based systems approach to childhood obesity prevention.
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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 | 0.028 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".