Positive Effects of Workplace Meditation Training and Practice
Bibliographic record
Abstract
There is evidence that meditation is a powerful organisational tool for enhancing employee effectiveness, wellbeing, and job satisfaction; however, experimental studies on the effects of meditation on other organisational factors such as presenteeism and emotional intelligence are limited. This study investigated the impact of meditation on mindfulness, emotional intelligence, job satisfaction, and job stress-related presenteeism in an Australian workplace. Participants learned and practised an 'Auto Transcending Meditation Technique’ (ATMT) at their workplace. The study used the switching replications experimental design, comparing an intervention group with a control group. Quantitative data analysis used descriptive statistics and repeated measures to compare the mean pre-post intervention differences. Thematic analysis was completed on qualitative data gathered in focus groups and from the training evaluation. As a consequence of ATMT, participants showed significant improvements in their levels of mindfulness and emotional intelligence. Thematic analysis indicated that participants felt the meditation training and practice led to positive personal changes. In addition, the results showed that higher mindfulness buffers the effect of stress-related presenteeism on participants’ mental and physical health. Our results demonstrate that meditation training and practice enhances mindfulness and emotional intelligence, with benefits for employees’ physical and mental health. Workplace meditation should be considered in health promoting work settings.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 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".