Socio-demographic correlates of health behaviors of children and adolescents during the COVID-19 pandemic: a cross-sectional study
Bibliographic record
Abstract
This study aimed to assess the socio-demographic correlates of health behaviors and their changes during the COVID-19 pandemic. A population-based sample of children (3-17 years) was randomly selected between 12/2021 and 06/2022 in Geneva, Switzerland. Parents reported about children's screen time, physical activity, green spaces time, sleep duration, and whether these changed due to the pandemic. Socio-demographic correlates of changes in health behavior and adherence to corresponding recommendations were evaluated. Of 2104 children, approximately 1/3 did not meet screen or physical activity recommendations, 22% did not sleep sufficiently. Older children and girls were less likely to meet behavior recommendations. During the pandemic, physical activity, green spaces time and sleep quality remained unchanged for most children (72.9%, 72.2% and 93.1%, respectively), while 51.9% experienced an increase in screen time. Older age was associated with a worsening of health behaviors, while children from disadvantaged households were more likely to experience any changes, whether positive or negative. Between 20% and 30% of children fail to meet health behavior recommendations. The COVID-19 pandemic possibly exacerbated existing disparities among older and socio-economically disadvantaged children, although positive changes were also reported. Continuous monitoring and targeted interventions are crucial to promoting healthy lifestyles among youth in the post-pandemic period.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".