Exploring the Impact of Work-Life Balance on Employees: A Systematic Literature Review
Bibliographic record
Abstract
The objective of this research is to provide a comprehensive understanding of work-life balance outcomes toward employees. The research adopts the Systematic Literature Review (SLR) approach. The researcher conducts a literature review encompassing the search and analysis of initial data collected from databases such as Scopus, PsycInfo, Emerald, Sciencedirect, Sage and Springerlink. The systematic literature review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The systematic literature review retrieved a total of 38 articles. After undergoing several stages, 25 articles met the criteria and were subsequently subjected to a more in-depth analysis. The findings of this systematic literature review have been categorized into two main outcomes: individual and organizational outcome. By knowing the outcome of work-life balance, the organization can identify the positive and negative impact of work-life balance for both individual and organization. Moreover, it also can be used to gain deeper insight into the importance of balancing work and personal life, as well as its implications in both individual and organization context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.033 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.024 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".