Are There Bidirectional Influences Between Screen Time Exposure and Social Behavioral Traits in Young Children?
Bibliographic record
Abstract
OBJECTIVE: Screen time in early childhood has been associated with children's prosocial and behavioral skills; however, the directionality of this relationship is unclear. We aimed to determine the direction of the relationship between screen time, social skills, and nonsocial behavioral traits in young children. METHODS: This was a population-based, prospective cohort study with data across 5 time points. We examined the reciprocal relationships between caregiver-reported children's screen time at 12, 18, 24, 36, and 54 months and social behaviors collected using the Infant-Toddler Social-Emotional Assessment at 12 months; the Quantitative Checklist for Autism at 18, 24, and 36 months; and the Social Responsiveness Scale at 54 months. Cross-lagged path models were used for analysis. RESULTS: A multiple imputation data set and complete data from 229 participants were included in the analyses. Screen time at 12, 18, and 36 months predicted nonsocial behavioral traits at 54 months. Cross-lagged path models showed a clear direction from increased screen time at earlier time points to both poorer social skills and atypical behaviors at later time points (Akaike information criterion 18936.55, Bayesian information criterion 19210.73, root mean square error of approximation 0.037, and comparative fit index 0.943). Social skills or behavioral traits at a younger age did not predict later screen time at any of the time points. CONCLUSION: Screen time in early childhood has lagged influences on social skills and nonsocial behaviors; the reverse relationship is not found. Close monitoring of social behaviors may be warranted in the setting of excessive screen time during early childhood.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".