COVID-19 Stress and Family Well-Being : The Role of Sleep in Mental Health Outcomes for Parents and Children
Bibliographic record
Abstract
Background/Objectives: The COVID-19 pandemic introduced various stressors for families, including changes to daily routines, work, and schooling. Studies have linked these stressors to increased mental health challenges for parents and children. Sleep difficulties were also common during the pandemic, with some children and parents experiencing poorer sleep quality and shorter sleep duration. However, it remains unclear whether the effects of COVID-19 stress on mental health challenges are explained, at least in part, by effects of COVID-19 stress on child and/or parent sleep challenges. This study examined the impacts of COVID-19 stress on sleep and, in turn, mental health difficulties in school-aged children and their parents in Canada and the United States. Methods: Parents (N = 961) completed validated measures of COVID-19 stress, and of their own and their child’s sleep and mental health. Path analyses tested direct and indirect effects of COVID-19 stress on mental health outcomes with sleep problems as the potential mediator. Results: Child sleep problems partially mediated COVID-19 stress effects on both parent (β = 0.33) and child (β = 0.20) mental health difficulties, while parent sleep problems contributed significantly but to a lesser degree (parent mental health: β = 0.07; child mental health: β = 0.03). There also remained significant direct effects of COVID-19 stress on both child and parent mental health difficulties that were not mediated through sleep difficulties. Conclusions: Our findings underscore the interconnected nature of sleep and mental health, demonstrating that stress-related disruptions in sleep (particularly children’s sleep) can exacerbate mental health difficulties for both parents and children during crises like 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 | 0.001 |
| 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".