Associations Between Adverse Childhood Experiences and Early Adolescent Problematic Screen Use in the United States
Bibliographic record
Abstract
Abstract Background Problematic screen use, defined as loss of control over usage resulting in impairments in personal, social, and occupational functioning, has risen dramatically among adolescents. Adverse Childhood Experiences (ACEs) are important risk factors in the development of addictive behaviors and may play an important role in the development of problematic screen use. Methods Cross-sectional data from the Adolescent Brain Cognitive Development Study (Year 2; 2018-2020; N = 8,794) was analyzed in 2022. Multiple linear analyses were used to determine associations between ACEs and adolescent-reported problematic use of video games (Video Game Addiction Questionnaire), social media (Social Media Addiction Questionnaire), and mobile phones (Mobile Phone Involvement Questionnaire). Analyses were adjusted for potential confounders including age, sex, race/ethnicity, highest parent education, household income, and site. Results The 8,794 adolescent respondents ages 11-12 years old were racially and ethnically diverse (55.1% White, 19.5% Latino/Hispanic, 15.8% Black, 5.1% Asian, 3.2% Native American, 1.3% Other). ACEs were associated with higher problematic video game, social media, and mobile phone use in a dose-dependent fashion in both unadjusted and adjusted models. Conclusions Given the dose-dependent relationship in adolescent ACE exposure and rates of problematic screen use, public health programming for trauma-exposed youth should explore video game, social media, and mobile phone use among this population and implement interventions focused on supporting healthy digital habits.
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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 | 0.001 |
| 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 | 0.001 |
| 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".