Factors influencing implementation of a preschool-based physical activity intervention
Bibliographic record
Abstract
Examining factors that influence implementation of key program components that underlie an intervention’s success provides important information to inform the development of effective dissemination strategies. We examined direct and indirect effects of preschool capacity, quality of prevention support system and teacher characteristics on implementation levels of a component, called Move Outside (i.e., preschool classroom teachers to provide at least 40 min of outdoor recess per day), that was fundamental to the success of a preschool-based physical activity intervention. Level of implementation, defined as the percent of daily goal met for the Move Outside component, was assessed via direct observation. Items assessing preschool capacity, quality of prevention support system and teacher characteristics were selected from surveys and an environmental checklist completed by preschool directors and teachers. Preschool classroom was used as the unit of analysis (Year 1: n = 19; Year 2: n = 17). Results from Bayesian path analyses showed that the three factors were not significantly associated with level of implementation in Year 1, but preschool capacity was directly associated with level of implementation in Year 2 (β= 0.528, 95% CI: 0.134, 0.827). The current findings suggest that factors that influence level of implementation appear to differ as an intervention evolved over time.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".