Changes in Pediatric Movement Behaviors During the COVID-19 Pandemic by Stages of Lockdown in Ontario, Canada: A Longitudinal Cohort Study
Bibliographic record
Abstract
BACKGROUND: Children's movement behaviors have been affected by the COVID-19 pandemic; however, little is known regarding movement behavior patterns over time by government-issued lockdowns. Our primary objective was to evaluate how children's movement behaviors changed by stages of lockdown/reopening in Ontario, Canada, from 2020 to 2021. METHODS: A longitudinal cohort study with repeated measures of exposure and outcomes was conducted. The exposure variables were dates from before and during COVID-19 when child movement behavior questionnaires were completed. Lockdown/reopening dates were included as knot locations in the spline model. The outcomes were daily screen, physical activity, outdoor, and sleep time. RESULTS: A total of 589 children with 4805 observations were included (53.1% boys, 5.9 [2.6] y). On average, screen time increased during the first and second lockdowns and decreased during the second reopening. Physical activity and outdoor time increased during the first lockdown, decreased during the first reopening, and increased during the second reopening. Younger children (<5 y) had greater increases in screen time and lower increases in physical activity and outdoor time than older children (≥5 y). CONCLUSIONS: Policy makers should consider the impact of lockdowns on child movement behaviors, especially in younger children.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".