Scaling up healthy eating in early childhood education and care: evaluation of the Appetite to Play capacity-building intervention
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to examine the dissemination of the healthy eating component of Appetite to Play at scale using the Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) framework. DESIGN: The Appetite to Play capacity-building intervention is a set of evidence-informed implementation strategies aimed at enhancing the adoption of recommended practices for promoting healthy eating and active play in early years settings. The evaluation was pragmatic, employing both quantitative (surveys) and qualitative (interviews) data collection. SETTING: The Appetite to Play intervention was delivered through in-person community-based workshops, virtual workshops, asynchronous e-learning and online resources. PARTICIPANTS: We received completed surveys from 1670 in-person workshop participants (96 % female), and twenty-three (all female) survey respondents also participated in a telephone interview. Approximately two-thirds of all participant groups were certified early childhood educators. RESULTS: < 0·05) and high post-intervention intention to implement), adoption (11 % of educators in BC trained) and implementation (good alignment with implementation strategies and current practices), with a significant maintenance plan to support the intervention's future success. CONCLUSIONS: An evidence-based capacity-building intervention with an emphasis on training and provision of practical online resources can improve early years providers' knowledge, confidence and intention to implement recommended practices that promote healthy eating. Further research is needed to determine the impact on child-level outcomes and how parents can be supported in contributing to positive food environments.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".