The Impact of COVID-19 Pandemic on Egyptian Women Psychological Empowerment and Work-Life Balance
Bibliographic record
Abstract
The main objective of this study is to examine the impact of the COVID-19 Pandemic on Egyptian Women Psychological Empowerment and Work-Life Balance. The study was conducted on (107) Egyptian working women in different sectors and located in the Greater Cairo region. An online survey link was sent directly to these respondents to answer. They were selected using the non-probability judgmental sampling method; the only criterion for inclusion was that these respondents were working women operating in the Egyptian business context. Research hypotheses were tested using correlation and multiple regression analysis. After testing the effect of Egyptian working women’s psychological empowerment dimensions on the perceived work-life balance, it was concluded that competence and self-determination dimensions were the two main psychological empowerment dimensions that positively affected the perceived work life balance. The other three dimensions: meaning, impact, and trust dimensions, had an insignificant effect on the perceived work-life balance. This research will help in designing a practical roadmap showing how to empower women psychologically while preserving their well-being and balancing their work-life duties and responsibilities. In addition to implementing work-life strategies and HR policies that will support working women in Egypt. Most of the studies have tackled the positive benefits of women empowerment and ignored its consequences on women’s emotional and psychological well-being. Besides, few researches have been empirically administered on working women’s psychological empowerment especially in Egypt.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".