Relationship Between Eating Behaviors and Physical Activity Among Primary and Secondary School Students: Results of a Cross‐Sectional Study
Bibliographic record
Abstract
BACKGROUND: With a view toward developing concerted efforts in fostering healthy eating habits and a physically active lifestyle among young people, a study was carried out to explore associations between eating behavior and physical activity (PA). METHODS: In the school district, questionnaires were completed at home by parents of primary school children (N = 8612) and by secondary school youth (N = 5250) during a break in the schedule. The rates of response were 79% and 83%, respectively. Inferential and descriptive analyses were performed. RESULTS: The results indicate significant differences between the eating behaviors of young people who engage in 60 minutes of daily PA and those who are sedentary. The physically active children were generally more likely to eat fruit, vegetables, and whole-grain products and to have breakfast (p < .05 among high-school students). The lack of self-confidence (55%) and not enjoying PA (46%) stood out as the greatest obstacles facing adolescents trying to lose weight. CONCLUSION: There should be particular actions targeting students in the last half of primary school aimed at developing individual accountability and autonomy with respect to healthy eating and PA. These actions should also consider sex differences and those who have more sedentary lifestyles.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".