Delayed bedtime due to screen time in schoolchildren: Importance of area deprivation
Bibliographic record
Abstract
BACKGROUND: Sleep duration is an important predictor of obesity and health. This study evaluated the association between late bedtime and screen time, and the role of geographical deprivation in English schoolchildren. METHODS: We collected bedtime and waking time, screen time, sociodemographic data and measured body mass index in a cross-section of 1332 11-15-year-old schoolchildren (45.7% female) participating in the East of England healthy heart study. Logistic regression was used to determine the likelihood of late bedtime in schoolchildren with different screen time and from a different geographic location. Mean differences were assessed either on ANOVA or t-test. RESULTS: Approximately 42% of boys went to bed late at night compared with 37% of girls. When compared to those with <2 h of daily screen time, schoolchildren with 2-4 h of screen time were more likely [odds ratio (OR) = 1.50, 95% confidence interval (CI): 1.07-2.09] to go to bed late at night while those with >4 h of daily screen time were most likely to go to sleep late at night (OR, 1.97; 95%CI: 1.34-2.89). Late bedtime was associated with deprivation in schoolchildren. CONCLUSIONS: High screen time and deprivation may explain lateness in bedtime in English schoolchildren. This explanation may vary according to area deprivation and geographic location. Family-centered interventions and parental support are important to reduce screen time, late bedtime and increase sleep duration.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".