Physical activity during recess among 13–14 year old Mexican girls
Bibliographic record
Abstract
Physical activity patterns during recess have been poorly described among adolescents. Physical activity levels could be captured the most accurately using a combination of instruments. The purposes of this study were to describe the physical activity patterns during school recess in a sample of 13-14 year old Mexican girls, to examine differences in these patterns as assessed using three physical activity measurement instruments and to understand the influence of body weight status on the observed associations.The study population included seventy-two female adolescents from a private school in Mexico City. Three different instruments were used to monitor physical activity patterns during the recess break including an accelerometer, direct observation (SOFIT instrument), and a physical activity recall. Descriptive analyses were used to characterize physical activity patterns, and one-way ANOVA was used to examine differences across physical activity instruments.Based on the accelerometer data, more than 90% of the recess period was spent at a light or sedentary intensity. Based on SOFIT and recall, the two most frequent activities were standing and walking. There was a significant difference (p < 0.05) between the three instruments for time spent in all physical activity intensities.The large amount of time spent in light and sedentary intensity activities during recess indicates the necessity to intervene upon this opportunity for adolescents to engage in more vigorous forms of physical activity.
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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 | 0.000 |
| 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 | 0.000 | 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".