How Mindfulness-Trained Leaders Drive Compassion in Organizations
Bibliographic record
Abstract
In this paper we explore the role of mindfulness interventions in developing self- and other-oriented compassion among leaders within organizational settings. We draw on a longitudinal study of 62 organizational leaders who participated in an eight-week mindfulness training to examine how mindfulness contributes to the cultivation of a compassionate mindset and leadership practices. The data for analysis, collected at four time-points, comprises pre-intervention assessments and post-intervention interviews (in total 159 interviews), including six- and twelve-month follow-ups. We find mechanisms underpinning co-active compassion, such as interconnectedness, perspective-taking, and mutual support, which manifest at the collective level. Co-active compassion reveals a reciprocal interplay between self-care and the care for others, enhancing both individual and collective compassionate competencies in leadership. This study contributes significantly to both mindfulness and compassion in leadership literatures by showing that mindful self-compassion in leadership is a dynamic, interpersonal phenomenon, crucial for leaders who aim to effectively balance self-care with their responsibility towards others. We also extend current understanding of compassion in leadership by providing empirical evidence of the role of mindfulness in fostering both self- and other-oriented compassion. We discuss theoretical and practical insights for developing compassionate leadership competencies in the face of modern organizational challenges.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".