From public to private: the gendered impact of COVID-19 pandemic on work-life balance and work-family balance
Bibliographic record
Abstract
This article provides insights into the ways flexible, hybrid and work-from-home arrangements have impacted women during COVID-19 lockdowns in the UK.Based on 10 in-depth interviews with women living and working in the East Midlands, England, who turned to work from home during COVID lockdowns, this study found that despite heightened care needs and the additional burdens women faced during the pandemic, one silver lining was that flexible and hybrid work has positively impacted some.All women spoke about how the pandemic and associated restrictions have altered their conceptualisation of space both positively and negatively.Life during the pandemic gave participants extra care needs and added burdens, but it also gave them more space to be with family and to manage their lives more effectively.This sense of increased space for social and family bonding and life and time management was reduced (again) after the pandemic due to the difficulties women had to bear in balancing the demands of work and family obligations.This article contributes to the studies on the impact of COVID-19 lockdowns on women's work-life-balance (WLB) and work-familybalance (WFB),demonstrating the need to think of innovative ways to support women's flexible work in the long term.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".