School and student characteristics associated with screen-time sedentary behavior among students in grades 5-8, Ontario, Canada, 2007-2008.
Bibliographic record
Abstract
INTRODUCTION: We examined school and student characteristics associated with screen-time sedentary behavior. METHODS: We analyzed data collected from 2,449 students in grades 5 through 8 who attended 30 elementary schools in Ontario, Canada. We used multilevel logistic regression to examine the student- and school-level factors associated with moderate and high screen-time sedentary behavior. RESULTS: Moderate screen time did not vary significantly across schools. Student characteristics significantly associated with moderate screen time were sex, number of friends who are active, and parental encouragement of physical activity. High screen time did vary significantly across schools; school-level differences accounted for 12% of the variability in the odds of a student reporting high screen time. Students who attended a school in the more advanced phase of emphasizing participation in physical activity through school programs were less likely to report high screen time compared with students who attended schools in the earlier phase for this school-level indicator. Student characteristics significantly associated with high screen time were sex, parental encouragement of physical activity, parental support of physical activity, and race/ethnicity. CONCLUSION: High levels of screen-time sedentary behavior are associated with both student characteristics and the characteristics of the school a student attends. Developing a better understanding of the school characteristics associated with sedentary behavior will be valuable for guiding the development of interventions to reduce sedentary behavior among youth populations.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".