HOW DOES THE COVID-19 PANDEMIC AFFECT YOUNG CHILDREN’S SCREEN TIME? THE ROLE OF BIOECOLOGICAL FACTORS
Bibliographic record
Abstract
During the COVID-19 pandemic, young children faced a shift to online education due to social isolation rules, resulting in increased time spent in front of digital screens. Even before the pandemic, the World Health Organization had recommended limiting screen time for young children as extended screen exposure was becoming more common with the increased prevalence of digital tools. This study aimed to examine the status of young children’s screen time during the COVID-19 pandemic and explore the factors influencing it, encompassing child, parent, and environmental dynamics. Through a large-scale online survey, 1,346 parents with children aged 2 to 6 from all 81 provinces of Türkiye participated in the research. Hierarchical linear regression analysis revealed that age, digital device ownership, parental screen time, and mediation strategies were positively associated with children’s screen time, while higher parental income, education, and engagement in dramatic play were negatively correlated. These findings underscore the importance of targeted interventions to achieve a healthier degree of screen usage among young children. Policymakers can play a role in raising awareness about limiting both parent and child screen time and promoting screen-free activities within the home environment, thereby contributing to improving the balance between screen usage and other activities among young children as society moves beyond the pandemic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".