Association between screen time and guardian-reported vision difficulty in children and adolescents: A population-based analysis
Bibliographic record
Abstract
To investigate associations between screen time and guardian-reported vision difficulties among paediatric populations in the United States. Retrospective, population-based, cross-sectional study. Using data from the 2020 and 2022 National Health Interview Survey, this study included participants aged 2-17 years with complete guardian-reported data pertaining to vision status and screen time usage. We performed logistic regressions to explore associations between screen time and vision difficulties, reporting odds ratios (ORs) and 95% confidence intervals (CIs). A total of 5,112 participants (mean age: 10.1 ± 4.7 years) were included in 2020, and 6,473 participants (mean age: 9.9 ± 4.7 years) were included in 2022. In our univariable analysis, children who had a screen time of >2 hours/day had a higher odds of guardian-reported vision difficulty in 2020 (OR=1.53, 95%CI=[1.08, 2.16], p=0.017) and in 2022 (OR=1.38, 95%CI=[1.02, 1.86], p=0.038). These findings were consistent in a subgroup of female children (p=0.002 in 2020 and p=0.014 in 2022). Our multivariable analysis of 2020 data found that the odds of guardian-reported vision difficulty among children with a screen time of >2 hours/day was significantly higher in females (OR=1.73, 95%CI=[1.02, 2.93], p=0.040) and children residing in the Midwest (OR=2.41, 95%CI=[1.11, 5.20], p=0.026). No findings were significant in our adjusted analysis of 2022 data. Screen time was associated with guardian-reported vision difficulties in paediatric populations in the United States in our univariable analysis. However, this association was not consistently observed in adjusted models of 2020 data, and no associations remained significant in the multivariable analysis of 2022 data. Public health initiatives aimed at promoting healthy screen time habits and regular eye care among vulnerable groups of children and adolescents are encouraged.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".