Examining physical literacy in young adults: psychometric properties of the PLAYself
Bibliographic record
Abstract
The PLAYself is a commonly utilized tool to assess physical literacy in child and adolescent populations. Currently, there are no measurement tools designed to examine physical literacy among adults. The purpose of this cross-sectional study was to examine the psychometric properties of PLAYself subsections in a sample of young adults. Two hundred forty-five young adults (ages 18–25) from the United States completed the PLAYself questionnaire. Multiple principal component analyses using promax rotation were utilized to assess the current factor structure of the PLAYself subsections. Each subsection was analyzed independently to explore individual summary components. PLAYself subsections were assessed for reliability using Cronbach's α, inter-item correlations, and item-total correlations. A multi-factor structure was identified for each PLAYself subsection. A 2-factor structure was identified for the Environment subsection accounting for 55.2% of the variance. A 2-factor structure was identified for the Physical Literacy Self-Description subsection accounting for 57.1% of the variance. A 3-factor structure was identified for the Relative Ranking of Literacies subsection accounting for 70.3% of the variance. The Environment, Physical Literacy Self-Description, and Relative Ranking of Literacies subsections demonstrated poor ( α = 0.577), good ( α = 0.89), and acceptable ( α = 0.79) internal consistencies, respectively. The Physical Literacy Self-Description subsection demonstrated the best psychometric properties in our sample, and thus may be an appropriate tool to assess physical literacy in a young adult population until additional measurement tools are developed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".