The Effect of Remote Working on Employees Wellbeing and Work-Life Integration during Pandemic in Egypt
Bibliographic record
Abstract
The study aims to enrich employers' understanding of how employees perceive remote working Post COVID-19's quarantine period and its effect on employees' psychological wellbeing and work-life integration in Egypt. A structured questionnaire was distributed post-COVID-19 pandemic lockdown period on a sample of 318 employees who are supposed to be working remotely in different sectors from home. Correlation and regression analyses were conducted to test the research hypotheses. The results suggest a significant positive effect of employees' perception of remote working on psychological wellbeing and work-life integration. Simultaneously, there is a significant negative effect of employees' perception of remote working and emotional exhaustion. This study should help employers design the appropriate intervention plan to sustain operations and maintain effective communication with remote workers. It contributes to the literature by considering it as one of the growing empirical studies that will tackle remote working in relation to employee psychological wellbeing and work-life integration Post-COVID-19 quarantine period in Egypt. The majority of research nowadays tackling COVID-19 is from a biomedical perspective, focusing on physical and mental health, but this research will tackle COVID-19 from a psychological and managerial standpoint. The research results will assist researchers and practitioners in gaining insights into the future role of remote working.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".