Should we still be concerned about screen time use for Canada’s young children? Understanding the research landscape of screen time after the COVID-19 pandemic.
Bibliographic record
Abstract
During the COVID-19 pandemic, screen time rates well surpassed the recommended guidelines for Canada’s young children despite the evidence for prolonged screen time’s effect on a child’s health being well established. It is unknown if these rates have decreased in our post-pandemic world, underscoring the urgent need for understanding screen time patterns to inform future guidelines and policy for young children. This commentary aims to urge researchers to address the knowledge gap of screen time in the post-pandemic era and outlines recommendations for health care providers to evaluate children’s screen time in their practice. ---------- Pendant la pandémie de la COVID-19, le temps passé par les jeunes enfants devant les écrans a largement dépassé les recommandations au Canada, malgré les preuves bien établies qu’un temps d’écran prolongé a un effet négatif sur leur santé. On ne sait pas si le niveau d’exposition a diminué après la pandémie, ce qui souligne l’urgence de comprendre les habitudes d’utilisation des jeunes enfants des écrans afin d’informer les futures lignes directrices et politiques les concernant. Ce commentaire vise à répondre à la nécessité de recherches supplémentaires sur l’utilisation des écrans par les jeunes enfants dans l’ère post-COVID, après une augmentation documentée de l’utilisation des écrans pendant la pandémie.
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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 | 0.007 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".