Scalability and scaling-up strategy of a physical activity policy intervention in Australian childcare centres
Bibliographic record
Abstract
There is an urgent need for scalable interventions to promote physical activity in early childhood. An early childhood education and care (ECEC) physical activity policy intervention with implementation support strategies (Play Active) has been proposed for scale-up in Australia. This study sought to assess the scalability of Play Active and describe the Play Active scaling-up strategy. The Intervention Scalability Assessment Tool was used to assess scalability. The PRACTical planning for Implementation and Scale-up (PRACTIS) guided the scaling-up strategy and involved: (i) characterizing the implementation setting; (ii) identifying existing/new partnerships; (iii) identifying barriers and facilitators to implementation; (iv) addressing barriers through adaptations. The Play Active scalability assessment domains with the highest scores (>2.5/3) were for the problem, intervention, reach and acceptability. Four additional domains scored highly (>2/3): fidelity and adaptation, delivery settings and workforce, implementation infrastructure, and strategic/political context. The lowest scores (<2/3) were the evidence of effectiveness, intervention costs and benefits, and sustainability domains. The PRACTIS guide showed that the implementation setting and existing and new partnerships were appropriate for scaling-up Play Active. The PRACTIS guide also identified key barriers (e.g. staff time) and enablers (e.g. staff professional development) to implementation at scale. Adaptations were identified to address these barriers (e.g. intervention delivery via a customised website). Overall, the scalability assessment revealed gaps in some scalability domains to be addressed through further research and adaptation of Play Active. The proposed scale-up trial evaluation is crucial to support decision-makers to fund, scale and institutionalize Play Active in the real world.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".