Social epidemiology of bedtime screen use behaviors and sleep outcomes in early adolescence
Bibliographic record
Abstract
OBJECTIVES: The current study aimed to determine sociodemographic associations of bedtime screen use behaviors and the sociodemographic differences in the associations between bedtime screen use and sleep outcomes in a national (US) study of early adolescents. METHODS: We analyzed cross-sectional data from 10,305 early adolescents (12-13years, 48.4% female) in the Adolescent Brain Cognitive Development (ABCD) Study (Year 3, 2019-2021). Multiple regression analyses examined associations between (1) sociodemographic factors (age, sex, race and ethnicity, sexual orientation, household income, parental education, and number of siblings) and adolescent-reported bedtime screen use and (2) bedtime screen use and sleep outcomes (caregiver-reported sleep disturbance and self-reported sleep duration). RESULTS: Older age, female sex, sexual minority status, lower household income, and lower parent education were associated with more bedtime screen use. Black, Native American, and Latino/Hispanic race/ethnicity were associated with more bedtime screen use compared with White race, regardless of household income or parent education. More bedtime screen use was linked to greater sleep disturbances, with stronger effects observed in male adolescents. More bedtime screen use was also associated with shorter sleep duration, particularly among female adolescents and individuals from households with higher income and parental education levels. Although sexual minority identification was associated with more bedtime screen use, it was not associated with worse sleep outcomes among these adolescents. CONCLUSIONS: Given sociodemographic differences in bedtime screen use, digital literacy education and anticipatory guidance could focus on at-risk early adolescent populations. Findings can inform targeted counseling by pediatricians and family media plans for diverse populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".