Effectiveness of Interventions Aimed at Reducing Screen Time in Children
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: To evaluate the impact of interventions focused on reducing screen time. DATA SOURCES: Medline, Embase, Cochrane Central Register of Controlled Trials, PsycINFO, ERIC, and CINAHL through April 21, 2011. STUDY SELECTION: Included studies were randomized controlled trials of children aged 18 years or younger with interventions that focused on reducing screen time. INTERVENTION: Efforts to reduce screen time. MAIN OUTCOME MEASURES: The primary outcome was body mass index (BMI); the secondary outcome was screen time (hours per week). RESULTS: A total of 1120 citations were screened, and 13 studies were included in the systematic review. Study samples ranged in age (3.9-11.7 years) and size (21-1295 participants). Interventions ranged in length (1-24 months) and recruitment location (5 in schools, 2 in medical clinics, 1 in a community center, and 5 from the community). For the primary outcome, the meta-analysis included 6 studies, and the difference in mean change in BMI in the intervention group compared with the control group was -0.10 (95% confidence interval [CI], -0.28 to 0.09) (P = .32). The secondary outcome included 9 studies, and the difference in mean change from baseline in the intervention group compared with the control group was -0.90 h/wk (95% CI, -3.47 to 1.66 h/wk) (P = .49). A subgroup analysis of preschool children showed a difference in mean change in screen time of -3.72 h/wk (95% CI, -7.23 to -0.20 h/wk) (P = .04). CONCLUSIONS: Our systematic review and meta-analysis did not demonstrate evidence of effectiveness of interventions aimed at reducing screen time in children for reducing BMI and screen time. However, interventions in the preschool age group hold promise.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it