Scaling up a community-led health promotion initiative: Lessons learned and promising practices from the Healthy Weights for Children Project
Bibliographic record
Abstract
The increase in overweight and obesity among children has emerged as an important public health issue. This trend has highlighted the need for accessible and novel approaches to support healthy weights for children and their families to prevent childhood obesity. The purpose of this article is to describe the iterative development and scale-up of a community-led, national-level project to promote healthy weights among Canadian children and families who may be experiencing vulnerabilities. In this project, the Healthy Together program was designed to engage families in an interactive program to support healthy lifestyles. The program also provides a platform for creating supportive environments for healthful lifestyles through practice and policy change. Based on a process evaluation, we describe the iterative development of Healthy Together from Phase 1 through 3 to shed light on processes shaping implementation and scale-up of the program. Lessons learned during each phase were used to refine the program and further expansion. Indicators of successful scale-up include the Healthy Together program's cross-jurisdictional reach and promising evaluation results in real-world conditions. The practice-based program scaling approach provides practical guidance for planning and implementing similar health promotion programs in diverse communities.
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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 | 0.041 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| 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".