Understanding the unpaid work roles amongst households, during COVID-19
Bibliographic record
Abstract
Worldwide, the COVID-19 pandemic has had a rapid disruption on work, social activities and family life. Pre-pandemic norms suggested that women spend more time in unpaid work roles and with childcare, while men spend more time in paid work roles. This study aims to understand: 1) the distribution of unpaid work roles within households, and 2) if there are certain factors that explain the unpaid work roles within a household during the pandemic. This study used a cross-sectional survey of people across the globe, during the pandemic. The survey, administered through a virtual platform of Qualtrics, consisted of the following sections: (a) consent, (b) location and job description (c) marital status and household numbers (d) age, sex, and gender (e) unpaid work roles and family responsibilities. Descriptive statistics and percentages were reported for all the data regarding the study variables. A multivariable regression model was used to understand which factors may explain the changes in unpaid work roles recalling before and during the pandemic This survey was completed by 1847 participants. The mean age was 30 years old (standard deviation of 13.3). The majority of participants identified themselves as women (76.0%) and single (62.1%). The multivariable linear regression indicated that marital status (single, common-law, married, divorced), higher number of household members (1-8,12), older age, higher number of dependent children, and gender (female) were positive and significant predictors of baseline changes in unpaid work role scores, explaining 50% of the variance (R2 = 0.50). All households experienced a significant increase in the amount of unpaid work roles during the pandemic. However, older women who were in a relationship and experienced additional household members such as dependent children or sick older adults, were faced with more changes in unpaid work roles during COVID-19, than other individuals.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".