The Impact of COVID-19 on Eating Environments and Activity in Early Childhood Education and Care in Alberta, Canada: A Cross-Sectional Study
Bibliographic record
Abstract
Early childhood education and care (ECEC) environments influence children’s early development and habits that track across a lifespan. The purpose of this study was to explore the impact of COVID-19 government-mandated guidelines on physical activity (PA) and eating environments in ECEC settings. This cross-sectional study involved the recruitment of 19 ECEC centers pre-COVID (2019) and 15 ECEC centers during COVID (2020) in Alberta, Canada (n = 34 ECEC centers; n = 83 educators; n = 361 preschoolers). Educators completed the CHEERS (Creating Healthy Eating and activity Environments Survey) and MEQ (Mindful Eating Questionnaire) self-audit tools while GT3X+ ActiGraph accelerometers measured preschooler PA. The CHEERS healthy eating environment subscale was greater during COVID-19 (5.97 ± 0.52; 5.80 ± 0.62; p = 0.02) and the overall score positively correlated with the MEQ score (r = 0.20; p = 0.002). Preschoolers exhibited greater hourly step counts (800 ± 189; 649 ± 185), moderate-to-vigorous PA (MVPA) (9.3 ± 3.0 min/h; 7.9 ± 3.2 min/h) and lower sedentary times (42.4 ± 3.9 min/h; 44.1 ± 4.9 min/h) during COVID-19 compared to pre-COVID, respectively (p < 0.05). These findings suggest the eating environment and indices of child physical activity were better in 2020, which could possibly be attributed to a change in government-mandated COVID-19 guideline policy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".