Office-Based Randomized Controlled Trial to Reduce Screen Time in Preschool Children
Bibliographic record
Abstract
OBJECTIVE: To determine if an intervention for preschool-aged children in primary care is effective in reducing screen time, meals in front of the television, and BMI. METHODS: A randomized controlled trial was conducted at a primary care pediatric group practice in Toronto, Canada. Three-year-old children and their parents were randomly assigned to receive a short behavioral counseling intervention on strategies to decrease screen time. The primary outcome 1 year later was parent reported screen time. Secondary outcomes included television in the child's bedroom, number of meals in front of the television, and BMI. RESULTS: In the intention-to-treat analysis at 1 year, there were no significant differences in mean total weekday minutes of screen time (60, interquartile range [IQR]: 35-120 vs 65, IQR: 35-120; P = .68) or mean total weekend day minutes of screen time (80, IQR: 45-130 vs 90, IQR: 60-120; P = .33) between the intervention and control group. Adjusting for baseline BMI, there was a reduction in the number of weekday meals in front of the television (1.6 ± 1.0 vs 1.9 ± 1.2; P = .03) but no differences in BMI or number of televisions in the bedroom. CONCLUSIONS: This pragmatic trial was not effective in reducing screen time or BMI but was effective in reducing meals in front of the screen. Short interventions focused solely on reducing screen time implemented in the primary care practice setting may not be effective in this age group.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".