From Start-Up to Scale-Up of a Health-Promoting Intervention for Older Adults: The Choose to Move Story
Bibliographic record
Abstract
Interventions that are effective in research (efficacy or effectiveness) trials cannot improve health at a population level unless they are successfully delivered more broadly (scaled up) outside of the research setting. However, scale-up is often relegated to the too hard basket. Factors such as the need to adapt interventions prior to implementing them in diverse settings at scale, retaining fidelity to the intervention, and cultivating the necessary community and funding partnerships can all present a challenge. In the present review article, we present a scale-up case study—Choose to Move—an effective health-promoting intervention for older adults. The objectives of this review were to (a) describe the frameworks and processes adopted to implement, adapt, and scale up Choose to Move across British Columbia, Canada; (b) provide an overview of the phased approach to scale-up; and (c) share key lessons learned while implementing and scaling up health-promoting interventions with community partners across more than 2 decades.
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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 | 0.037 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".