POWER training improves officer autonomic health, mindfulness and social connection
Bibliographic record
Abstract
In the profession of policing, the accumulation of stressful incidents over the course of a career can lead to a host of adverse health outcomes: increased incidence of injury and illness, diminished cognitive performance, mental health impacts (including anxiety, depression, addiction and elevated risk of suicide), increased risk of cardiovascular disease and early mortality. The toxic climate of dysfunctional agency culture, local community resistance and distrust, and the national political discourse around policing all serve to increase the stress that first responders bear, contributing to erosion of police-community relationships. Beyond Us & Them partnered with California State University San Marcos to offer the Peace Officer Wellness, Empathy & Resilience (POWER) training to university police officers. POWER is a nationally certified 12-week training program, which teaches skills and practices that promote well-being, mindfulness and relationality, and improve police-community relations. Based on survey data from prior cohorts, we realized the potential benefit of adding biometric measurements to look for improvement in autonomic health. Other studies have demonstrated an inverse correlation between heart rate variability (HRV) and cardiovascular disease, cognitive decline and risk of all-cause mortality. Of the 17 participants, 15 completed pre- and post-intervention surveys, and HRV was obtained from 13 of these participants: findings demonstrated improved autonomic health, as well as statistically significant changes in empathy, mindfulness and social connection. Additionally, we noted increased HRV coherence, which may be a physiologic marker of enhanced social connection. Future studies offer the possibility of utilizing HRV coherence as a marker of group connection and performance.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".