Movement for Life! A physical literacy resource for early childhood caregivers
Bibliographic record
Abstract
Early childhood education settings play a critical role in offering opportunities for children to develop physical literacy. The purpose of the present study was to investigate if Movement for Life! (M4L), a physical literacy education program for adult caregivers of children ages 0-6, translated to change in the provision, knowledge, and understanding of physical literacy by early childhood educators (ECEs). Using a pre and post-test design 84 ECES completed two measures: the Physical Literacy Environmental Assessment (PLEA; Caldwell et al., 2020), and a survey regarding personal behaviours for providing physical literacy development opportunities. In addition, six childcare centre directors completed the Physical Activity Self-Assessment for Childcare (Ward et al., 2008). The results demonstrate that the participation in the M4L program has a positive impact on the physical literacy environment of early childhood care centres. Additionally, ECEs believed it was important to provide physical literacy development activities. They reported increased confidence to provide effective physical literacy development activities from pre-test to post-test and reported significantly decreased difficulty providing effective physical literacy development activities from pre-test to post-test, although some barriers were suggested to still exist. The results indicate the M4L program was effective and successfully implemented with ECEs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".