Development of the Physical Literacy Environmental Assessment (PLEA) tool
Bibliographic record
Abstract
Physical literacy is becoming increasingly popular in sport, recreation, physical education and physical activity settings and programming. We developed an environmental assessment tool to evaluate the extent child and youth activity programs implement physical literacy across four domains: environment, programming, leaders and staff, and values and goals. The Physical Literacy Environmental Assessment (PLEA) tool was developed in 3 phases. First, the PLEA tool was created, content validity established, and physical literacy leaders were consulted. In the second phase, the PLEA tool was completed and tested by 83 child and youth programs and it was validated with individual physical literacy assessments completed on children in programs that scored in the top 10% and bottom 10% on the PLEA tool. Third, a National consultation was conducted, and program leaders provided feedback on the PLEA tool. In Phase 1, the PLEA tool was modified and shortened from 41 to 29 indicators, based on feedback from physical literacy content leaders. In Phase 2, participants in programs that scored in the top 10% had significantly higher scores on the upper body object control domain of PLAYfun (p = 0.018), and significantly higher PLAYself scores (p = 0.04) than participants in programs that scored in the bottom 10%. In Phase 3, over 80% of program leaders identified the PLEA tool was useful, and relevant to their areas of practice. The completed PLEA tool is a 20-item environmental assessment tool to evaluate to what degree child and youth programming implement physical literacy across four domains: environment, programming, leaders and staff, and values and goals. The application and validity of the PLEA tool beyond child and youth physical education, sport, dance and recreation sectors, such as in early years programs, should be investigated.
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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 | 0.019 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".