39 Physical and social distancing measures and child health behaviours during COVID-19: A cohort study
Bibliographic record
Abstract
Abstract Primary Subject area Public Health and Preventive Medicine Background Various studies have examined the general impact of the COVID-19 outbreak on children’s health behaviours. The impact of public health measures practised by children during COVID-19 is relatively unknown. Objectives The primary objective was to determine the association between physical and social distancing measures and children’s outdoor time, sleep duration, and screen time during COVID-19. Design/Methods A longitudinal study using repeated measures of exposures and outcomes was conducted in healthy children (0 to 10 years) between April 14 and July 15, 2020. Parents were asked to complete questionnaires about isolation, physical distancing practices, and children’s health behaviours. The primary exposure was the average number of days that children practised physical and social distancing measures per week. The three outcomes were children’s outdoor time, total screen time, and sleep duration during COVID-19. Linear mixed effects models were fitted using repeated measures of primary exposure and outcomes. Results This study included 554 observations from 265 children. Physical and social distancing measures were associated with shorter outdoor time (-17.2; 95% CI -22.07, -12.40; p < 0.001) and longer total screen time (11.3; 95% CI 3.88, 18.79; p = 0.003) during COVID-19. The association with outdoor time was stronger in younger children (< 5 years), and the associations with total screen time were stronger in females and in older children (≥ 5 years). Conclusion Physical and social distancing measures during COVID-19 have resulted in negative impacts on the health behaviours of Canadian children living in a large metropolitan area.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".