The Impact of Financial and Psychological Wellbeing on Children’s Physical Activity and Screen-Based Activities during the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic, and the public health measures to combat it, have strained the finances of many families. While parents transitioned to working from home, children transitioned to learning virtually, limiting their organized social and physical activities. Families also reduced the frequency and size of gatherings, impacting psychological wellbeing. This study sought to understand the influence of financial wellbeing on children's physical activity and leisure screen-based activities via mothers' and children's psychological wellbeing. In May and June of 2020, 254 Grade 7 Canadian children and their mothers completed separate online surveys assessing family financial wellbeing, mothers' and children's psychological wellbeing, and children's physical activity and leisure screen-based activities. Structural equation modelling was used to examine the indirect effects of mothers' and children's psychological wellbeing on the relationship between financial wellbeing and children's physical activity and leisure screen-based activities. Final models were adjusted for potential confounders. Study results indicate a significant indirect association between financial wellbeing and children's physical activity and leisure screen-based activities via mothers' and children's psychological wellbeing. These findings demonstrate that higher levels of financial wellbeing are associated with better mental and physical health benefits in children during the COVID-19 pandemic.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".