Do the implementation processes of a school-based daily physical activity (DPA) program vary according to the socioeconomic context of the schools? a realist evaluation of the Active at school program
Bibliographic record
Abstract
BACKGROUND: Less than half of Canadian children meet the Canadian Physical Activity (PA) Guidelines, and the proportion is even lower among children living in underprivileged neighbourhoods. Regular PA supports physical, cognitive, and psychological/social health among school-aged children. Successful implementation of school-based daily physical activity (DPA) programs is therefore important for all children and crucial for children who attend schools in lower socioeconomic settings. The purpose of this study is to uncover what worked, for whom, how, and why during the three-year implementation period of a new "flexible" DPA program, while paying particular attention to the socioeconomic setting of the participating schools. METHODS: This study is a realist evaluation using mixed methods for data generation. Longitudinal data were collected in 415 schools once a year during the three-year implementation period of the program using questionnaires. Data analysis was completed in three steps and included qualitative thematic analysis using a mixed inductive and deductive method and chi-square tests to test and refine context-mechanism-outcome (CMO) configurations. RESULTS: Giving the school teams autonomy in the choice of strategies appropriate to their context have allowed schools to take ownership of program implementation by activating a community empowerment process, which resulted in a cultural shift towards a sustainable DPA provision in most settings. In rural underprivileged settings, the mobilization of local resources seems to have successfully created the conditions necessary for implementing and maintaining changes in practice. In disadvantaged urban settings, implementing local leadership structures (leader, committee, and meetings) provided pivotal assistance to members of the school teams in providing new DPA opportunities. However, without continued external funding, those schools seem unable to support local leadership structures on their own, jeopardizing the sustainability of the program for children living in disadvantaged urban areas. CONCLUSION: By exploring CMO configurations, we have been able to better understand what worked, for whom, how and why during the three-year implementation period of the Active at School! PROGRAM: When implementing DPA policies, decision makers should consider adjusting resource allocations to meet the actual needs of schools from different backgrounds to promote equal PA opportunities for all children.
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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 | 0.044 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".