Understanding Screen-Related Sedentary Behavior and its Contributing Factors among School-Aged Children: A Social-Ecologic Exploration
Bibliographic record
Abstract
PURPOSE: To explore the factors that contribute to children's screen-related sedentary (S-RS) behaviors. SETTINGS: Elementary schools. SUBJECTS: A random sample of children in grades five and six and their parents. MEASURES: The outcome measure was children's S-RS activity level measured by a self-administered questionnaire. A full spectrum of potential contributing factors for children's S-RS behaviors was obtained through surveys. Multilevel linear regression methods were used to determine the associations between these factors and children's screen time (hours per day) and results were expressed as regression coefficients (g). RESULTS: Of 955 child-parent pairs in 14 participating schools, 508 pairs (53%) completed the surveys. At an intrapersonal level, protective factors included being a girl (g = -.71); belonging to a sports team inside (g = -.56) or outside (g = -.49) of school; having a negative attitude toward S-RS activities (g = -.13); and having a positive attitude toward physical activity (g = .48). At the interpersonal and social levels, parental leisure S-RS behaviors (g = .32) were positively associated, whereas strict parental rules on computer use (g = -.27) and family income (g = -.32) were inversely correlated with S-RS behavior. At the environmental level, the presence of TVs in children's bedrooms (g = .44) and owning videogame devices (g = .58) increased the risk of S-RS behaviors, whereas after school programs (g = - .86) and schools' participation in the Turn Off the Screen Week campaign (g = -.91) decreased the risk. CONCLUSIONS: Public health interventions should target multilevel factors, including increasing children's awareness, promoting parental involvement in healthy lifestyle pursuits, and creating less screenogenic environments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".