Subjective well-being of Canadian children and youth during the COVID-19 pandemic: The role of the social and physical environment and healthy movement behaviours
Bibliographic record
Abstract
The current COVID-19 pandemic has disrupted daily behaviours of children and youth. Yet, little is known about how they are mentally coping with the pandemic-time changes to their lives. This study explores children and youths' self-reported subjective well-being (SWB) during the pandemic, and provides novel insights into the correlates of potential decrease, using data from a pan-Canadian online survey of 932 children/youth and their parents. SWB was measured based on perceived changes in 12 affective/emotional states. The results indicate that in spring 2020, at the height of the first wave of the pandemic, many children and youth were more bored (37.6%) and worried (31%) compared to pre-pandemic time. At the same time, many self-reported that they felt calmer (31.9%) and more rested (30%). A latent class analysis revealed that nearly half (49.4%) of surveyed children and youth reported patterns in changes in their emotional state that may contribute to lower SWB. Results from binomial logistic regression suggest that socio-demographic characteristics and size of the municipality were not associated with low pandemic-time SWB. Instead, other potentially modifiable factors such as having access to friends, indoor and outdoor spaces/places to play and exercise, and healthy movement behaviours during the pandemic, were correlated with a lower likelihood of reporting low SWB. The findings can inform pandemic-time public health policy relating to physical distancing, and in the longer term, mental and physical health promotion. The results will also help improve urban planning and design practices in creating healthier, more resilient and equitable communities.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".