Sociodemographic Correlates of Contemporary Screen Time Use among 9- and 10-Year-Old Children
Bibliographic record
Abstract
OBJECTIVE: To determine sociodemographic correlates of contemporary screen time use among a diverse population-based sample of 9- and 10-year-old children. STUDY DESIGN: In 2021, we analyzed cross-sectional baseline (2016-2018) data from the Adolescent Brain Cognitive Development study (n = 10 755). Multiple linear regression analyses were conducted to estimate associations between sociodemographic factors (sex, race/ethnicity, country of birth, household income, parental education) and 6 contemporary forms of screen time (television, videos [eg, YouTube], video games, social networking, texting, and video chat). RESULTS: On average, children reported 3.99 hours of screen time per day across 6 modalities, with the most time spent watching/streaming television shows/movies (1.31 hours), playing video games (1.06 hours), and watching/streaming videos (1.05 hours). On average, Black children reported 1.58 more hours of screen time per day and Asian children reported 0.35 less hours of screen time per day compared with White children (mean 3.46 hours per day), and these trends persisted across most modalities. Boys reported higher overall screen time (0.75 hours more) than girls, which was primarily attributed to video games and videos. Girls reported more time texting, social networking, and video chatting than boys. Higher income was associated with lower screen time usage across all modalities except video chat. However, in high-income households, Latinx children reported 0.65 more hours of screen time per day than White children. CONCLUSIONS: Given the sociodemographic differences in child screen use, guideline implementation strategies can focus on key populations, encourage targeted counseling by pediatricians, and adapt Family Media Use Plans for diverse backgrounds.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".