Barriers and bridges: Exploring the introduction of meditation and mindfulness training into Canadian policing
Bibliographic record
Abstract
Canadian police organizations are under significant pressure to enhance the health and wellness of their employees. Growing research suggests that training in meditation and mindfulness can contribute to the well-being of police personnel and may even be a catalyst for police reform. Limited research, however, has been conducted that seeks to understand how these practices should be introduced into Canadian police organizations. This article contributes to this understanding by sharing results from an exploratory study that asked 11 Commissioned Officers, who regularly practice meditation, to identify the key factors that should be considered when introducing mindfulness practice into their large Canadian police service. Using semi-structured interviews and focus groups, and guided by a reflexive thematic analysis approach, six themes were developed. These can be viewed as both barriers (invincibility and stigma; overworked and overstressed; and checkbox cynicism) and bridges (credible champions; the whole person perspective; and the philosophy of servant leadership) to the successful introduction of meditation and mindfulness practices into Canadian police organizations. This study advances the literature on introducing mindfulness to policing as it is one of the first to focus on the perceptions of mindfulness practicing Commissioned Police Officers. It also offers practical suggestions for police leaders, and leaders from other public safety professions, to consider when contemplating the introduction of these mental practices into their organizations.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".