An examination of bedtime media and excessive screen time by Canadian preschoolers during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: Risky media use in terms of accumulating too much time in front of screens and usage before bedtime in early childhood is linked to developmental delays, reduced sleep quality, and unhealthy media use in later childhood and adulthood. For this reason, we examine patterns of media use in pre-school children and the extent to which child and family characteristics contribute to media use during the COVID-19 pandemic. METHODS: A cross-sectional study of digital media use by Canadian preschool-aged children (mean age = 3.45, N = 316) was conducted at the start of the COVID-19 pandemic between April and August of 2020. Parents completed a questionnaire and 24-h recall diary in the context of an ongoing study of child digital media use. From these responses we estimated hours of average daily screen time, screen time in the past 24 h, average daily mobile device use, and media use before bedtime. Parents also answered questions about their child (i.e., age, sex, temperament), family characteristics (parental mediation style, parental screen time, education, income), and contextual features of the pandemic (ex., remote work, shared childcare). Daycare closures were directly assessed using a government website. RESULTS: Our results indicate that 64% of preschoolers used more than 2 h of digital media hours/day on average during the pandemic. A majority (56%) of children were also exposed to media within the hour before bedtime. Logistic and multinomial regressions revealed that child age and temperament, restrictive parental mediation, as well as parent digital media use, education, satisfaction with the division of childcare, remote work, and number of siblings and family income were all correlates of risky digital media use by preschoolers. CONCLUSIONS: Our results suggest widespread risky media use by preschoolers during the pandemic. Parenting practices that include using more restrictive mediation strategies may foster benefits in regulating young children's screen time.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".