A study on work-life balance in the era of work from home with reference to understanding the change in perceived job satisfaction through statistical analysis
Bibliographic record
Abstract
Working from anywhere or Working from Home has been prevalent in many developed countries, specifically in the IT sector. Still, the pandemic brought in the wave for such concepts in India, and the people here were not ready for it socially and culturally. As it was an unforeseen and forced situation here in the country, its adaptability raised several questions and issues in the minds of employers and employees. With the shift happening in work culture, which is, working from home due to the pandemic, many changes have crept into the employees’ minds. One such notable arena, which should be addressed for better human resources management and efficiency, is perceived job satisfaction and understanding the employees’ work-life balance amidst these changes. In the study, 90 employees from selected IT companies at various levels are under consideration to understand their perseverance of job satisfaction and work-life balance in and before the change. The stability and the effects of the different attributes on the subject are studied. Statistical tools like Multiple Regression Analysis, Pearson Correlation, and Z-test are used.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".