The association between learning models and child health behaviours during the COVID-19 pandemic
Bibliographic record
Abstract
This paper aimed to explore the association between school learning models (virtual vs. in-person) and child health behaviours (daily screen time, physical activity, outdoor time, sleep duration, and sleep onset time) during COVID-19, and whether these associations were modified by child's age, sex, and family income. A longitudinal cohort study was conducted among children four to 13 years from the TARGet Kids! COVID-19 Study of Children and Families between November 2020 and July 2022. TARGet Kids! is a primary care research network in Ontario, Canada. Data on sociodemographic characteristics, child school learning models and health behaviours were collected from repeated parent-reported questionnaires. Linear mixed effects models were fit adjusting for confounders identified a priori. A total of 367 children [51 % male; 7.3 (± 2.2) years] with 779 observations on school learning model were included. Compared to in-person learning, virtual learning was associated with higher daily screen time (0.22 h; 95 % CI 0.03, 0.40), higher outdoor time (0.71 h; 95 % CI 0.56, 0.86), higher physical activity (0.64 h; 95 % CI 0.44, 0.85), and a later sleep onset time (0.22 h; 95 % CI 0.15, 0.28). Older children had higher daily outdoor time, girls had a later sleep onset time and children with a family income greater than $150,000 reported higher daily physical activity. Virtual learning was associated with higher daily screen time, outdoor time and physical activity, and later sleep onset time during the pandemic. • COVID-19 virtual learning impact on child health behaviours in Canada is unknown. • Virtual learning children had higher daily screen time and later daily sleep onset. • A later sleep onset was most prominent among girls vs. boys. • Virtual learning children had higher physical activity and outdoor time. • Higher daily outdoor time found in children >6 years attending school virtually.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".