STRESS AND COPING AMONG PARENTS WITH CHILDREN ENROLLED IN REMOTE SCHOOLING DURING COVID-19
Bibliographic record
Abstract
The COVID-19 pandemic necessitated the closure of schools, prompting 93% of U.S. households with children to transition to remote schooling. This study investigates coping mechanisms used by parents and the emotional impact of remote schooling on their well-being. A cross-sectional online survey, grounded in the Transactional Model of Stress and Coping, was conducted among 133 U.S. parents with children engaged in remote schooling from May to October 2020. Pearson correlations and paired sample t-tests were calculated. Multiple regression was performed to determine how well stress, resilience, and gender predict depressive symptoms. The study participants had an average of 1.96 children. Most commonly used coping mechanisms included planful problem-solving, seeking social support, and escape/avoidance. A statistically significant positive association was found between stress and depressive symptoms. Stress and depressive symptoms increased during the pandemic, while resilience decreased. The resulting statistically significant regression model of stress, resilience, and gender accounted for 75.4% of the variability in depressive symptoms. These results underscore the importance of addressing parental well-being and mental health during times of crisis, particularly when children are engaged in remote schooling.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".