Work-Life Balance among Teaching Hospital Nurses in Malaysia
Bibliographic record
Abstract
Extreme workload and poor working environment have a negative impact on the emotional and physical statuses among nurses. The study has contributed to evaluate work-life balance and its related factors among teaching hospital nurses. It was aimed to examine the work-life balance and its related factors among teaching hospital nurses. A cross-sectional study using a universal sampling technique was conducted. 1002 nurses were included from the Teaching hospital of Klang Valley, Malaysia. The instrument was adapted from NIOSH Generic Job Stress Questionnaire and QoL questionnaire from WHO, and it was used to measure the quality of work-life balance. Non-work activities, job requirement, supervisor support, job satisfaction, manageability, social and environmental variables have independently influenced work-life balance among nurses. Furthermore, quality of life variables has positively influenced the work-life balance (P<0.050). Work life balance and organizational commitment can have a positive relationship. Whereas, Nurses working in fixed shifts were observed with greater work-life balance as compared to the nurses working in multiple shifts. A friendly environment in the professional sector plays a major role for developing motivation and enthusiasm among workers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".