Individual and environmental correlates of physical literacy sub-components in early childhood
Bibliographic record
Abstract
This study examined individual and environmental correlates of physical literacy sub-components among preschool-aged children (3-5 years). This cross-sectional study utilized baseline data from the PLAYshop intervention. Participants were 122 families from Alberta and British Columbia, Canada. Informed by Dynamical Systems Theory, individual (children's age, sex, race/ethnicity) and environmental (parental education, physical activity modelling, capability, opportunity, motivation) correlates were measured via a parental questionnaire. Five fundamental movement skills (FMS; horizontal jump, hop, overhand throw, underhand throw, balance) were assessed via recorded virtual meetings using the Test of Gross Motor Development (TGMD-3) and the Movement Assessment Battery for Children (MABC-2). Children's motivation/enjoyment/confidence of active play was parental-reported using the Preschool Physical Literacy Assessment Tool (Pre-PLAy) and children's enjoyment was self-reported via an adapted Five Degrees of Happiness Likert scale. Regression models were conducted. Children's age was a significant positive correlate of all FMS. Females had significantly lower scores for overhand throw, underhand throw, and parental-reported children's motivation, compared to males. Higher parental capability was associated with higher balance scores. Higher parental education was associated with lower children's self-reported enjoyment. Findings for race/ethnicity were mixed. Future research should explore additional correlates across settings and physical literacy sub-components to better inform physical literacy interventions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".