Mindful police leadership: Opening essential new pathways to 21st-century police leadership and culture
Bibliographic record
Abstract
Canadian police managers are facing a series of daunting new challenges that will require significant personal resources to address. Growing research suggests mindfulness training, meditation, and other related contemplative practices appear to benefit leaders in other professions; however, little is known about how these practices may, or may not, be helpful for police leaders. This article contributes to this understanding by sharing results from an exploratory qualitative study that asked senior police leaders who self-identified as regular meditators to discuss how their practices might influence their leadership. Guided by a reflective thematic analysis approach and utilizing NVivo Qualitative Data Analysis Software, data from semi-structured interviews and focus groups with 11 Commissioned Officers from a large Canadian police service were analyzed for themes. Broadly organized by influence on job performance, relationships, and well-being, eight distinct themes were developed: enhanced calmness and self-control; better clarity and decision making; improved focus and efficiency; enhanced presence with others; improved conflict resolution practices; greater compassion and empathy; reduced harmful stress; and enhanced resilience and work/life integration. These results begin to extend the literature on mindful leadership from other workplace contexts into the realm of police leadership and suggest that meditation and mindfulness are beneficial practices that may open new and essential pathways towards 21st century police leadership and culture.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".