Challenges of working from home during the <scp>COVID</scp>‐19 pandemic for women in the <scp>UAE</scp>
Bibliographic record
Abstract
This study explored the experiences of working from home among women in the United Arab Emirates (UAE) during the lockdown. Adopting the interpretative philosophical approach, this study conducts semi-structured interviews with sixteen (16) randomly selected women actively employed in different sectors in the UAE economy. The analysis was carried out using the thematic analysis to derive the themes and sub-themes emerging from the coded data. The research finds that most of the challenges are associated with spillover from work, affecting family time, and invading personal space. The research concluded that women working remotely faced issues linked to glitches, malfunctions, and knowledge deficiencies. The third most identified challenge to working from home was the distractions that come with the conscious attempt to divide attention between work and family, trying to stop one from interfering with the other. However, the research observed some notable advantages including workplace flexibility and control, as well as the opportunity to work from the comfort of the home. The findings also revealed the mixed feelings to continue working from home and its impact on the career progression of women in the UAE.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".