Reducing Work Stress through Employee Engagement: A Randomized Controlled Trial
Bibliographic record
Abstract
This study aimed to evaluate the effectiveness of an Employee Engagement Training Program in reducing work-related stress among employees. The research sought to determine whether structured training could foster employee engagement and alleviate stress, contributing to improved job satisfaction and organizational success. A randomized controlled trial was conducted with 40 full-time employees experiencing mild to moderate work-related stress. Participants were divided into an experimental group, which received the Employee Engagement Training Program, and a control group, which did not receive any intervention. The training consisted of 8 sessions, each lasting 90 minutes, focusing on stress management, communication skills, resilience, and goal setting. Data were analyzed using a two-way Analysis of Variance (ANOVA) with repeated measurements and Bonferroni post-hoc tests. The results demonstrated a significant reduction in perceived work stress levels among participants in the experimental group compared to the control group. Specifically, the experimental group showed a notable decrease in work stress from pre-test to post-test and maintained this reduction at the two-month follow-up. The ANOVA revealed significant effects for time, group, and their interaction on work stress levels, indicating the training program's effectiveness. The Employee Engagement Training Program significantly reduced work-related stress among participants, underscoring the importance of structured training in enhancing employee engagement and well-being. These findings suggest that organizations can benefit from implementing similar programs to foster a positive work environment, improve job satisfaction, and achieve organizational success. Future research should aim to explore the long-term effects of such interventions and their applicability across different sectors.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".