Contemporary screen time modalities among children 9–10 years old and binge‐eating disorder at one‐year follow‐up: A prospective cohort study
Bibliographic record
Abstract
OBJECTIVE: To determine the prospective associations between contemporary screen time modalities in a nationally representative cohort of 9-10-year-old children and binge-eating disorder at one-year follow-up. METHOD: We analyzed prospective cohort data from the Adolescent Brain Cognitive Development (ABCD) Study (N = 11,025). Logistic regression analyses were conducted to estimate associations between baseline child-reported screen time (exposure) and parent-reported binge-eating disorder based on the Kiddie Schedule for Affective Disorders and Schizophrenia (KSADS-5, outcome) at one-year follow-up, adjusting for race/ethnicity, sex, household income, parent education, BMI percentile, site, and baseline binge-eating disorder. RESULTS: Each additional hour of total screen time per day was prospectively associated with 1.11 higher odds of binge-eating disorder at 1-year follow-up (95% CI 1.05-1.18) after adjusting for covariates. In particular, each additional hour of social networking (aOR 1.62, 95% CI 1.18-2.22), texting (aOR 1.40, 95% CI 1.08-1.82), and watching/streaming television shows/movies (aOR 1.39, 95% CI 1.14-1.69) was significantly associated with binge-eating disorder. DISCUSSION: Clinicians should assess screen time usage and binge eating in children and adolescents and advise parents about the potential risks associated with excessive screen time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".