Measurement properties of the Physical Literacy Assessment for Youth (PLAY) Tools
Bibliographic record
Abstract
The Physical Literacy Assessment for Youth (PLAY) Tools are a suite of tools to assess an individual’s physical literacy. The purpose of this study is to examine the psychometric properties of the PLAY Tools, including inter-rater reliability, internal consistency, validity and the associations between the tools. In this study, 218 children and youth (aged 8.4 to 13.7 years) and a parent/guardian completed the appropriate physical literacy assessments (i.e., PLAYbasic, PLAYfun, PLAYparent and PLAYself) and the Bruiniks–Oseretsky Test of Motor Proficiency (BOT-2). Inter-rater reliability for PLAYfun was excellent (intraclass correlation coefficient = 0.94). The PLAYbasic, PLAYfun total, running and object control scores, and PLAYparent motor competence domain were higher in males than females, and PLAYfun locomotor skills were lower in males than females (p < 0.05). Age was positively correlated with PLAYbasic and PLAYfun (r = 0.14–0.32, p < 0.05). BOT-2 was positively correlated with PLAYfun and PLAYbasic (r = 0.19–0.59, p < 0.05). PLAYbasic is a significant predictor of PLAYfun (r2 = 0.742, p < 0.001). PLAYfun, PLAYparent and PLAYself were moderately correlated with one another. PLAYfun, PLAYparent and PLAYself demonstrated acceptable internal consistency (α = 0.74–0.87, ω = 0.73–0.87). The PLAY Tools demonstrated moderate associations between one another, strong inter-rater reliability and good construct and convergent validity. Continued evaluation of these tools with other populations, such as adolescents, is necessary. Novelty: In school-age children, the PLAY Tools demonstrated strong inter-rater reliability, moderate associations with one another, acceptable internal consistency and good construct and convergent validity. The results suggest that that PLAY Tools are an acceptable method of evaluation for physical literacy in school-age 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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".