The role of the teacher in the implementation of a School-based intervention on the physical activity practice of children
Bibliographic record
Abstract
INTRODUCTION: Medium or long term intervention strategies on physical activity practice (PAP) need to be more effective in their implementation by practitioners. The aim of this study was to evaluate the role of a teacher to implement the Team Pentathlon (TP) in order to improve the PAP in children. METHODS: 203 children (age: 10-13years), grades 5 and 6 (intervention group [IG] N = 104, control group [CG] N = 99) were guided to increase their PAP during an 8-week period by 5 elementary school teachers (physical education or classroom) who received 4 training sessions. Levels of PAP (self-reported) were compared between groups (IG/CG), sex, socioeconomic status of the schools and between teachers: baseline and during TP. RESULTS: Several teachers noted significant increases in PAP in the IG for both boys and girls (p<.05 or p<.01), whereas others found only small improvements in PAP. One teacher even observed higher PAP in the CG. Training session records revealed that the teacher himself, how the TP is implemented, and proper resources were the 3 elements that explained the successful implementation of the TP program. CONCLUSION: The implementation of the TP significantly increased the PAP in children. Training sessions helped teachers to implement the TP program but personal engagement, motivation, respecting protocol and adequate environment are necessary in improving the PAP of children.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".