Facility-level implementation strategies in early childhood education and care to enhance adherence to a provincial physical activity standard: Protocol for the Good Start Matters ATP+ randomised controlled trial
Bibliographic record
Abstract
Early childhood education and care (ECEC) facilities represent an important setting to support the healthy development during early childhood through the promotion of active play (AP) and fundamental movement skills (FMS). A mandatory AP standard was enforced in 2017 for licensed ECEC centres in the province of British Columbia, Canada. In conjunction with the AP standard, a suite of capacity building resources were administered (Appetite to Play), which improved the knowledge and confidence of early childhood educators in implementing AP and FMS development activities. Child outcomes were not measured. With implementation of AP practices degrading over time, we set out to enhance existing ATP resources and developed an advanced mobile app-based professional development program title “Appetite to Play Plus (ATP+)”. ATP+ focuses on implementation strategies that integrate AP and FMS into scheduling and curriculum planning and on creating a supportive environment for AP in ECEC facilities. The purpose of the research is to investigate whether advanced mobile app-based training focused on implementation strategies and practical resources for early childhood educators and managers: 1) enhances adherence to the British Columbia Director of Licensing Standard of Practice for AP, and 2) improves 2.5- to 5.9-year-old children’s AP and FMS over a 12-week period. We hypothesise that centres randomised to the ATP+ intervention will: 1) demonstrate greater adherence to the AP standards in comparison to those assigned to the waitlist control group, and 2) children attending intervention centres will spend more time in AP while in care and have greater increases in their FMS scores. A hierarchical mixed effect model incorporating both fixed and random effects at the ECEC centre and child levels will test the impact of ATP + , while considering that children, educators and managers are clustered within ECEC centres. Trial registration ClinicalTrials.gov NCT 05669378
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.037 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.092 | 0.014 |
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".