Identifying essential implementation strategies: a mixed methods process evaluation of a multi-strategy policy implementation intervention for schools
Bibliographic record
Abstract
BACKGROUND: Physically Active Children in Education (PACE) is composed of eight implementation strategies that improves schools' implementation of a government physical activity policy. A greater understanding of each discrete implementation strategy could inform improvements to PACE for delivery at-scale. This study aimed to: (A) measure the dose delivered, fidelity, adoption and acceptability of each strategy using quantitative data; (B) identify implementation barriers and facilitators using qualitative data; and (C) explore the importance of each strategy by integrating both data sets (mixed methods). METHODS: This study used data from a cluster randomised noninferiority trial comparing PACE with an adapted version (Adapted PACE) that was delivered with reduced in-person external support to reduce costs and increase scalability. Data were collected from both trials arms for between-group comparison. Descriptive statistics were produced using surveys of principals, in-school champions and teachers; and project records maintained by PACE project officers (objective A). Thematic analysis was performed using in-school champion and project officer interviews (objective B). Both data sets were integrated via a triangulation protocol and findings synthesized in the form of meta-inferences (objective C). RESULTS: Eleven in-school champions and six project officers completed interviews; 33 principals, 51 in-school champions and 260 teachers completed surveys. Regardless of group allocation, implementation indicators were high for at least one component of each strategy: dose delivered =100%, fidelity ≥95%, adoption ≥83%, acceptability ≥50%; and several implementation barriers and facilitators were identified within three broad categories: external policy landscape, inner organizational structure/context of schools, and intervention characteristics and processes. All strategies were considered important as use varied by school, however support from a school executive and in-school champions' interest were suggested as especially important for optimal implementation. CONCLUSION: This study highlights the importance of both executive support and in-school champions for successful implementation of school physical activity policies. In particular, identifying and supporting an in-school champion to have high power and high interest is recommended for future implementation strategies. This may reduce the need for intensive external support, thus improving intervention scalability.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".