Parents, Teachers, and Community: A Team Approach to Developing Physical Competence in Children
Bibliographic record
Abstract
Increasing children’s physical activity engagement has short- and long-term health benefits. Developing physical competence is a key component of children’s engagement in physical activity. The purpose of our study was to assess if a 12-week home, school, and community-based physical literacy intervention improved the physical competence of children in kindergarten and grade one. Four schools were either assigned to receive the intervention (n = 2 schools) or continue with their usual practice (control sites) (n = 2 schools). Physical competence was evaluated pre- and post-intervention in 103 intervention (41 female) and 83 usual practice (36 female) children using PLAY Fun. PLAY Parent and PLAY Coach tools measured parent and teacher perspectives of children’s physical competence, respectively. The intervention effect was assessed with repeated measures MANOVA to evaluate change in physical competence, with alpha set at p < 0.05. Children in both groups improved their locomotor, object control, and overall physical competence (p < 0.05) over the 12-week intervention. There was a significant intervention effect for locomotor and overall physical competence (p < 0.05). Interestingly, parents did not perceive these changes in physical competence (p > 0.05). However, teachers perceived improved physical competence for children in the intervention. Our physical literacy intervention improved the physical competence of children in kindergarten and grade one.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".