A tailored physical education program enhances elementary students' self‐efficacy, attitudes, and motivation to engage in physical activity
Bibliographic record
Abstract
Abstract Achieving adequate physical activity is an important health goal that many elementary school students are not meeting during the school day. The physical activity leadership (PAL) Program was designed to provide individually tailored supports to improve the quantity and quality of physical activity in schools, as well as promote improvements in self‐efficacy, attitudes, and motivation for physical activity. Self‐efficacy, attitudes, and motivation are three key factors in predicting increases in physical activity, and consequently serve as the cognitive‐affective variables examined in the present study. A sample of 117 fourth‐ and fifth‐grade students and 22 teachers completed several quantitative and open‐ended feedback measures before and after a district‐wide physical activity intervention. Results indicated the PAL Program was highly beneficial to all participants, and several key measures (negative attitudes, motivation to exercise, and perceptions of physical ability) were significantly impacted in the anticipated direction. Open‐ended feedback data collected from teachers involved also suggests that the program was well‐received and was perceived as beneficial. This study provides additional evidence in support of individually tailored physical education 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".