Implementing Appetite to Play at scale in British Columbia: Evaluation of a Capacity-Building Intervention to Promote Physical Activity in the Early Years
Bibliographic record
Abstract
Childcare is a critical target for promoting children's physical activity (PA) and physical literacy (PL). With emerging evidence about the efficacy of policy and capacity-building strategies, more information about how to bring these strategies to scale is needed. This paper describes implementation at scale of Appetite to Play (ATP), a capacity-building intervention for childcare providers, and examines the implementation and impact on early years providers' capacity to address PA. The ATP implementation evaluation was a natural experiment that utilized a mixed methods concurrent parallel design framed within the Reach, Effectiveness, Adoption, Implementation, Maintenance framework (RE-AIM). Workshop and website tracking assessed reach and adoption. Surveys and interviews with workshop participants and stakeholders assessed satisfaction, implementation, and maintenance. Training reached 60% of British Columbia municipalities and 2700 early years providers. Significant changes in participants' knowledge and confidence to promote PA and PL were achieved (p >0.01-0.001). Childcare level implementation facilitators as reported by early years providers included appropriate resources, planning, indoor space, and equipment, whereas weather and space were reported barriers. The stakeholder advisory group viewed the stakeholder network and Active Play policy as facilitators and adjustments to recent shifts in childcare funding and previous initiatives as barriers to implementation. ATP was scalable and impacted provider knowledge, confidence, and intentions. The impact on actual policies and practices, and children's PA needs to be assessed along with sustainability.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".