The Preschool Physical Literacy Assessment Tool: Testing a New Physical Literacy Tool for the Early Years
Bibliographic record
Abstract
Background: Physical literacy is essential to physical activity across the lifespan. While there is an emerging body of research on physical literacy in school-aged children, the preschool years have largely been ignored. We tested the psychometric properties of new tool, the Preschool Physical Literacy Assessment Tool (Pre-PLAY) designed to address this gap. Methods: We recruted 78 children (aged 19 to 49 months) across 5 childcare centers in Hamilton, Ontario. Two Early Childhood Educators (ECE) completed the Pre-PLAY for each child at two points in time to assess inter-rater reliability and test-retest reliability. We assessed the agreement between the Pre-PLAY tool with gross motor skills and the ability of the PPLAy to predict physical activity. Results: Results indicated Pre-PLAY is related to gross motor skills and predictive of physical activity for females, but not males. Inter-rater and intra-rater reliability was at least adequate for all but the co-orindated movements items and scale for females, but ECEs showed poor agreement for males. Conclusions: These results suggest initial support for the Pre-PLAY tool as a measure of physical literacy during the early years. However, some modification to the items and training are required to address the gender-specific effects found in this sample.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 0.001 |
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".