Long Term Effects of a Physical Literacy Intervention Completed in Childhood
Bibliographic record
Abstract
Purpose: Physical literacy (PL) skills have been linked to the achievement of a healthy, and active lifestyle. PL programs can improve PL skills from pre to post intervention, but the long term improvements associated with a physical literacy intervention are unknown. Methods: Children from two schools who had previously participated in the Champions for Life (CFL) program were contacted. In total, 30 children completed the online questionnaires which included the knowledge and understanding and the motivation and confidence questionnaires from the Canadian Assessment of Physical Literacy-2 (CAPL-2), and the Physical Activity Questionnaire for Children (PAQ-C). The Child Focused Injury Risk Screening Tool (ChildFIRST) was used to assess movement competence on 45 children. Results: No difference was found in mean scores between the children who had participated and those who had not for the MC, the KU, the PAQ-C, and the ChildFIRST. A moderate correlation was determined between scores on the PAQ-C and MC but not between the PAQ-C and KU, nor the PAQ-C and the ChildFIRST. Conclusion: The results of this study suggests that a higher physical activity level in children is positively associated to their motivation and confidence they exhibit in their movements and physical activity. The results did not show that a physical literacy intervention had significant effects 4 years later on the children’s motivation and confidence, knowledge and understanding, movement competence, and physical activity levels. More research is needed to truly examine the long-term effects of a physical literacy intervention.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".