Determinants and health-related consequences of screen time in children and adolescents: post-COVID-19 insights from a prospective cohort study
Bibliographic record
Abstract
AIMS: This study aims to provide age-specific prevalence of time spent on-screen among children and adolescents, to identify its sociodemographic and family-related determinants and to assess its impact on physical and psychosocial health outcomes. METHODS: Data was drawn from the SEROCoV-KIDS prospective cohort study, which includes randomly selected children living in Geneva, Switzerland. Daily screen time, sociodemographic and family characteristics were collected at baseline (December 2021 to June 2022). Physical and psychosocial health outcomes were measured at one-year follow-up. RESULTS: Among 674 children (2-8 years old), 752 preadolescents (9-13 years old) and 434 adolescents (14-17 years old), median daily screen time was 0h29, 1h14 and 3h18, respectively. Lower parental education and poorer parenting practices were associated with higher screen time in all age groups. In children only, poor parental mental health (+14 minutes/day; 95% CI: 2-27) and work-family conflicts (+6 minutes/day; 95% CI: 2-10) were related to increased screen time. After adjustment, elevated screen time was associated with an increased likelihood of poor physical-, emotional- and school-related quality of life in preadolescents and adolescents and of social difficulties in adolescents one year later. CONCLUSION: Almost all children engage with screens, but those from socially disadvantaged backgrounds and with strained families face a heightened risk of prolonged screen time. The health consequences we identified call for close monitoring.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".