Parents’ Report of Canadian Elementary School Children’s Physical Activity and Screen Time during the COVID-19 Pandemic: A Longitudinal Study
Bibliographic record
Abstract
COVID-19 public health protocols have altered children’s daily routines, limiting their physical activity opportunities. The purpose of this study was to examine how the COVID-19 pandemic affected children’s (ages 10–12 years) physical activity and screen time, and to explore the impact of gender, socioeconomic status (SES), and public health constraints (i.e., facility use and social interaction) on the changes in children’s health behaviors. Online surveys were disseminated to parents at two time points: before COVID-19 (May 2019 to February 2020) and during COVID-19 (November to December 2020). Wilcoxon signed-rank tests were used to assess changes in physical activity and screen time, and for subgroup analyses. Parents (n = 95) reported declines in children’s physical activity (Z = −2.53, p = 0.01, d = 0.18), and increases in weekday (Z = −4.61, p < 0.01, d = 0.33) and weekend screen time (Z = −3.79, p < 0.01, d = 0.27). Significant changes in physical activity and screen time behaviors were identified between gender, SES, and facility use groups. All social interaction groups underwent significant changes in screen time. Overall, COVID-19 protocols have negatively influenced children’s physical activity and screen time. Due to the negative consequences of inactivity and excessive screen time, resources must be made available to support families during the pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".