The intersection of COVID lockdown policies and parent resources with children’s media exposure in Canada, the United States, Israel, Sweden, and New Zealand
Bibliographic record
Abstract
Government responses to COVID-19 included workplace and childcare closures. Many parents worked from home while caring for preschool children. Media use increased worldwide for adults and young children. This study examined how government responses were linked to family media use by testing whether policy stringency predicted preschool children’s screen time and parents’ motivations for child media use. Data were analyzed from 2,365 parents of 2- to 5-year-old children in five countries: Canada, the United States, Israel, Sweden, and New Zealand. Higher stringency was associated with decreased screen time, though changes in screen time depended on parental income and education. Children from lower-resourced homes had the highest screen time while experiencing low stringency levels. Parents’ motivations for children’s media use to calm their children and for their child’s enjoyment were less likely with higher stringency. Motivations also depended on child age; parents were more likely to use media to calm their younger children than their older children. Media motivations also differed with family resources; less-resourced families were more likely to use media for calming their children. Our discussion focuses on how structural factors (policies, supports, and resources for parents) relate to family media patterns and how future research can inform our understanding of policy-related outcomes on children. Media use for children and adults increased during lockdowns prompted by the COVID-19 pandemic. Parenting demands also increased during periods when families stayed at home. However, the severity of these policies differed by geographic location and timing of the pandemic. This study examines media use in relation to lockdown policy severity with data harmonized across five countries. How policies affected families’ screen time and motivations for media use depended on family-level resources. Public health crises, consequences of climate change, and political unrest prompt lockdown policies. Digital media may be an easily deployed resource for practitioners and policymakers to assist families during lockdowns. Understanding children’s media use during COVID-related lockdowns informs these strategies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".