Domestic Burdens Amid Covid-19 and Women’s Mental Health in Middle-Income Africa
Bibliographic record
Abstract
This article analyzes two longitudinal datasets (October – December 2020; April 2021) of 1,000 and 900 women in Kenya and Nigeria, respectively, alongside in-depth qualitative interviews with women at risk of changes to time use, to study two pandemic issues: women’s substitution of paid for unpaid work and how these shifts compromise their mental health. Women devote more time to domestic care (30–38 percent), less time to employment (29–46 percent), and become unemployed (12–17 percent). A rise in domestic work is correlated with depressive (Nigeria) and anxiety symptoms (Kenya and Nigeria). Women with greater agency (Kenya) and fewer children (Nigeria) are less likely to report a domestic burden or loss in paid activities. Social protection programs may fill the void of assistance traditionally provided by informal networks in the short term, while campaigns shifting norms around household work may preserve women’s economic participation in the long term.HIGHLIGHTS Women in Kenya and Nigeria reported increases in domestic labor amid the pandemic.Women’s agency is negatively associated with the domestic burden and a reduction in paid activities in Kenya.Women in households with two or more children face greater domestic burdens and losses in paid activities in Nigeria.Increases in domestic work render women more likely to be anxious (Kenya and Nigeria) and depressed (Nigeria).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".