Reducing Key Barriers to Classroom Physical Activity Improves Implementation Fidelity in Ontario Elementary Classrooms
Bibliographic record
Abstract
Background: Schools are the front lines of children’s physical activity. Students who participate in daily physical activity (DPA) within their classrooms show improved physical strength, physical fitness, academic success, and mental well-being. However, teachers face many barriers to DPA implementation. The current study evaluated whether providing a DPA toolkit that offers greater access to DPA resources and requires minimal training for teachers to implement could improve DPA implementation. Methods: A total of 343 teachers from Ontario, Canada, completed a pre-survey assessing DPA practices and were given access to a DPA toolkit containing easily accessible and usable DPA resources to implement between January and March 2023. A total of 142 participants completed the post-survey to assess for changes in DPA practice. Several repeated measures of ANOVA were conducted with a three-level factor of grade level (1–3, 4–6, and 7–8) and within the subject factor of time (pre- to post-survey) on key outcome variables. Results: Findings revealed that the DPA toolkit improved DPA fidelity (p = 0.007, η2p = 0.053) and promoted teacher confidence in implementing DPA (p < 0.001, η2p = 0.285) and enjoyment in implementing DPA (p < 0.001, η2p = 0.177). Many improvements were most prominent among teachers teaching grades 1–3 and 4–6. Conclusions: By providing teachers with adequate DPA resources that minimize teacher training, there can be significant improvements in DPA implementation with notable cognitive and psycho-emotional benefits for both teachers and students.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".