Division of Labour and Parental Mental Health and Relationship Well-Being during COVID-19 Pandemic-Mandated Homeschooling
Bibliographic record
Abstract
While the COVID-19 pandemic has impacted the way parents partition tasks between one another, it is not clear how these division of labour arrangements affect well-being. Pre-pandemic research offers two hypotheses: economic theory argues optimal outcomes result from partners specialising in different tasks, whereas psychological theory argues for a more equitable division of labour. The question of which approach optimizes well-being is more pressing in recent times, with COVID-19 school closures leaving many couples with the burden of homeschooling. It is unknown whether specialisation or equity confer more benefits for mandated homeschoolers, relative to non-homeschoolers or voluntary homeschoolers. Couples (n = 962) with children in grades 1–5 completed measures of workload division and parental well-being. A linear mixed modelling in the total sample revealed that specialisation, but not equity, promoted increased parental emotional and relationship well-being. These relations were moderated by schooling status: voluntary homeschoolers’ well-being benefitted from specialisation, whereas mandated homeschoolers’ well-being did not benefit from either strategy; non-homeschoolers well-being benefitted from both strategies. Across the mixed-gender couples, mothers’ and fathers’ well-being both benefitted from specialisation; equity was only beneficial for mothers’ well-being. Overall, couples might be advised to adopt highly equitable and specialised arrangements to promote both parents’ well-being.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".