Mothers’ and Children’s Mental Distress and Family Strain during the COVID-19 Pandemic: A Prospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic had a widespread impact on families with dependent children. To better understand the impact of the pandemic on families' health and relationships, we examined the association between mothers' and children's mental distress and family strain. METHODS: = 157) from the Alberta Pregnancy Outcomes and Nutrition (APrON) longitudinal cohort in Alberta, Canada. Latent class analyses were performed to determine patterns and group memberships in mothers' and children's mental distress and family strain. Multivariable logistic regression models were conducted to test associations between mothers' and children's mental distress and family strain trajectory classes. RESULTS: Mothers with medium/high levels of mental distress were at increased odds of experiencing high family strain compared to those with low levels of distress (medium aOR = 3.90 [95% CI: 1.08-14.03]; high aOR = 4.57 [95% CI: 1.03-20.25]). The association between children's mental distress and family strain was not significant (aOR = 1.75 [95% CI: 0.56-5.20]). CONCLUSION: Mothers' mental distress, but not children's, was associated with family strain during the pandemic. More distressed individuals experienced greater family strain over time, suggesting that this association may become a chronic problem.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".