The Association Between PLAYfun and Physical Activity: A Convergent Validation Study
Bibliographic record
Abstract
Purpose: The purpose of this study was to examine the convergent validity of the PLAYfun tool, a physical literacy-based measure of movement competence, by examining its association with objectively measured physical activity in a sample of children and youth. Method: Participants included 110 children between the ages of seven to 14 years attending a stratified random sample of 27 afterschool programs across the province of Ontario, Canada. The PLAYfun tool was administered to the participants on one occasion at their afterschool program and then they were asked to wear a pedometer for seven consecutive days to measure their physical activity levels. A series of multiple linear regression models were used to examine the association between PLAYfun scores and physical activity, while controlling for age, sex, and time of year (season) in which the data were collected. Results: On its own, the PLAYfun average score accounted for close to 13% of the variance in physical activity, R = .36, R2 = .13, p < .001. The PLAYfun average score was also a significant independent predictor of physical activity, b (SE) = 145.98 (53.46), p < .01, when controlling for age, sex, and season in which the data were collected, R2 = .30, F (4, 105) = 11.04, p < .001. Conclusion: Results from the present study indicate that the PLAYfun tool is a significant predictor of objectively measured physical activity, supporting the convergent validity of the tool.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".