The Work/Care Interface and Parents’ Mid-Pandemic Mental Health: Inequalities at the Intersection of Gender and High-Risk Household Status
Bibliographic record
Abstract
The COVID-19 pandemic generated mental health stressors for parents as they faced new health risks and navigated disruptions to employment, schooling, and care arrangements. Drawing on 2021 survey data from Canadian parents of children 10 years old and younger, we describe the relationship between work/care pandemic stressors and mental health, and employ Kitagawa-Oaxaca-Blinder decompositions to examine how these contribute to mental health gaps by gender and its intersection with having household members perceived to be at high risk in relation to COVID-19. We find that mothers' mental health was more negatively affected than fathers'. Differences in exposure to work/care stressors help explain this gap, with the "mental load," perceptions of inequity in how households responded to pandemic care demands, and greater reported deterioration in work-family balance and career prospects particularly salient. Mothers, but not fathers, with high-risk household members were also more exposed to key work/care stressors, contributing to the worst pandemic mental health for this group. While the relationship between stressors and mental health was similar for mothers and fathers overall, high-risk status moderated this relationship, with employment or care disruptions that reduced COVID-19 exposure less likely to be associated with poorer mental health for parents in high-risk families.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".