Social epidemiology of early adolescent problematic screen use in the United States
Bibliographic record
Abstract
OBJECTIVE: To determine sociodemographic correlates of problematic screen use (social media, video games, mobile phones) among a racially/ethnically and socioeconomically diverse population-based sample of 10-14-year-old early adolescents. STUDY DESIGN: We analyzed cross-sectional data from the Adolescent Brain Cognitive Development Study (Year 2, 2018-2020; N = 8753). Multiple linear regression analyses were used to estimate associations between sociodemographic factors (age, sex, race/ethnicity, primary language, household income, parental education) and adolescent-reported problematic video game (Video Game Addiction Questionnaire), social media (Social Media Addiction Questionnaire), and mobile phone use (Mobile Phone Involvement Questionnaire). RESULTS: Boys reported higher problematic video game use while girls reported higher problematic social media and mobile phone use. Native American, black, and Latinx adolescents reported higher scores across all problematic screen measures compared to non-Latinx white adolescents. Having unmarried/unpartnered parents was associated with higher problematic social media use. Although higher household income was generally protective against problematic video game use, these associations were weaker for black than white adolescents (p for interaction <0.05). CONCLUSIONS: Given the sociodemographic differences in problematic screen use, digital literacy education strategies can focus on at-risk populations, encourage targeted counseling by pediatricians, and adapt family media use plans for diverse backgrounds. IMPACT: While sociodemographic differences in screen time are documented, we examined sociodemographic differences in problematic screen use in a large, diverse sample of early adolescents in the US. Boys reported higher problematic video game use while girls reported higher problematic social media and mobile phone use. Native American, black, and Latinx adolescents reported higher scores across all problematic screen measures compared to non-Latinx white adolescents. Although higher household income was generally protective against problematic video game use, these associations were weaker for black than white adolescents. Beyond time spent on screens, pediatricians, parents, and educators should be aware of sociodemographic differences in problematic screen use.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".