The Association Between Work-life Balance and Employee Mental Health: A systemic review
Bibliographic record
Abstract
The existence of an association between work-life balance and mental health in employees has not been found. The purpose of the systematic review is to assess the relationship between work-life balance and mental health among employees. This study used the SLR method; a literature search was conducted on the PubMed, Scopus, Google Scholar, and Crossref databases in early January 2023. The results showed n = 79 on Scopus, n = 2 on PubMed, n = 147 on Google Scholar, and n = 4000 on Crossref. After PRISMA analysis, n = 30 studies were included in the review. Number of reviews Positive correlation between work-life balance and positive mental health (n = 19); positive correlation between work-life balance and positive mental health (n = 7); positive correlation between work-life imbalance and positive mental health (n = 4). Countries where research was conducted: Malaysia, South Korea, India, Indonesia, Pakistan, Spanish, Nigeria, Ghana, Australia, New Zealand Maori, China, UK, Chinese, New Zealand European, French, Italian, Brazil, Canada, Taiwan, Egyptian, Saudi, Switzerland, and America. Positive mental health variations that have a positive relationship with work-life balance are psychological well-being, resilience, life satisfaction, well-being, positive mental health, higher job satisfaction, lower turnover intention, psychological well-being, well-being, job performance, and work involvement. There are a variety of workers: priests, public servants, lecturers, campus administrative staff, bankers, high school teachers, academics, media workers, midwives, and professors. Depression, anxiety, mental burden, work stress, the severity of insomnia, burnout, turnover intention, and technostress are all variations of mental health problems that have a positive relationship with work-life balance. Worker variations include bankers, health care professionals, work-from-home moms, working students, bus transportation workers, and full-time insurance tech employees.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".