Effects of Work-Life Balance Training Programs on Employee Job Motivation: A Quantitative Analysis
Bibliographic record
Abstract
This study aims to evaluate the efficacy of work-life balance training on improving job motivation among employees, utilizing a controlled experimental design to assess changes over time and sustainment of these changes post-intervention. A total of 40 participants were divided equally into experimental and control groups. The experimental group received work-life balance training, while the control group did not. Job motivation was measured for both groups at three time points: pre-test, post-test, and follow-up (three months post-intervention), using standardized questionnaires. Descriptive statistics and Analysis of Variance (ANOVA) with repeated measurements, followed by Bonferroni Post-Hoc tests, were employed to analyze the data. The experimental group showed a significant increase in job motivation from the pre-test (M=92.40, SD=20.10) to the post-test (M=110.73, SD=22.15), which was sustained at the follow-up (M=110.09, SD=22.49). In contrast, the control group's job motivation scores remained stable and showed no significant improvement. ANOVA results confirmed significant effects of time, group, and time × group interaction on job motivation, indicating the positive impact of the work-life balance training. Work-life balance training significantly improves job motivation among employees, with effects that are maintained over a medium-term period. This suggests that such interventions can be an effective strategy for organizations looking to enhance employee well-being and job motivation.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".