The Influence of Physical Education Class Content and Teachers’ Behaviour on Physical Activity Levels of Mexican Children
Bibliographic record
Abstract
Gharib H, Galaviz K, Lévesque L. According to the Mexican Report Card on Physical Activity for Children and Youth, Mexico is now a leader in childhood obesity with more than 4.5 million children aged 5 to 11 years being overweight or obese. Over 40% of Mexican children and youth are physically inactive, which is a major risk factor contributing to the childhood obesity epidemic in Mexico. Physical education (PE) class environment has been shown to influence physical activity (PA) levels in school-aged children; however, this relationship has not been documented in Mexican children. Thus, the purpose of this study is to assess the influence of PE class factors (class content and teacher behaviour) on children’s PA during class. PA was measured in a sample of 250 students in grades 3-5 in Mexico City during PE class. The SOFIT method was used to measure children’s PA (e.g. standing), class content (e.g. management), and teachers’ behaviour (e.g. instructing) during class. Multiple linear regressions will be conducted to assess the influence of class factors on children’s PA adjusting for age and gender. With the staggering rates of obesity and physical inactivity in Mexico, the identification of factors influencing PA is crucial. Results can be used to guide PE class interventions and to inform school policies.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".