Mindfulness, character, and workplace happiness: the moderating role of baseline levels of employee wellbeing
Bibliographic record
Abstract
Following the logic of the “happy-productive worker” hypothesis, organizations have been increasingly interested in new ways to elicit employee wellbeing. Consequently, research on mindfulness in work contexts has been burgeoning in recent years, as both conceptual and empirical reviews substantiated its importance as a cost-effective approach to promoting employee wellbeing. The purpose of the present study was to investigate whether employee happiness extends or transcends the conventional notions of employee wellbeing. More specifically, we invoke the positive psychology literature to argue that (a) employee happiness is related but distinct from employee wellbeing and (b) that initial levels of employee wellbeing might moderate the effect of mindfulness-based interventions. We conducted a secondary analysis of a publicly available dataset to test our predictions: focusing on 35 healthcare professionals from a healthcare organization in Barcelona, Spain. More precisely, employing a multivariate hierarchical regression, we compared if the incremental effect of an eight-week mindfulness-based strength intervention (MBSI) over a Mindfulness-based intervention (MBI) might be moderated by employees' initial levels before the intervention starts. Our results supported a moderating effect of employees' initial psychological wellbeing on a MBSI versus MBI. Implications for theory and practice are discussed.
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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 | 0.003 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".