Associations between screen time and cognitive development in preschoolers
Bibliographic record
Abstract
Objectives: To examine the cross-sectional associations between screen time and cognitive development in preschoolers. Methods: Participants were 97 preschoolers (36 to 60 months) in Alberta and Ontario, Canada in the supporting Healthy physical AcTive Childcare setting (HATCH) study. The time that children spent watching television, videos or DVDs (television time) or playing video or computer games (video game time) on a television, computer, or portable device was assessed using a parental questionnaire. Television time and video game time were summed to calculate total screen time. Adherence to the screen time recommendation (≤1 hour/day) of the Canadian 24-Hour Movement Guidelines was calculated. Expressive vocabulary and working memory were assessed using the Early Years Toolbox. Due to the distribution of working memory, it was categorized as a binary variable based on the median score. The associations between screen time and cognitive development were examined using mixed models (expressive vocabulary) or generalized mixed models (working memory). Results: Screen time was not associated with expressive vocabulary. Preschoolers who had higher total screen time were less likely to have better working memory (OR=0.52; 95%CI:0.31, 0.88), despite the null associations for television time (P=0.155) and video game time (P=0.079). Preschoolers who met the screen time recommendation were more likely to have higher working memory capacity (OR=3.48; 95%CI:1.06, 11.47), compared to those who did not meet the recommendation. Conclusion: Limiting total screen time to no more than one hour per day may facilitate working memory development in preschoolers. Screen time may be unrelated to expressive language development in this age group.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".