Police well-being interventions: Using awe narratives to promote resilience
Bibliographic record
Abstract
The well-being of the police workforce needs to be a priority as law enforcement agencies continue to adapt to emerging issues while serving their communities. Reflecting and sharing awe narratives, as well as being exposed to the awe stories of others, can enhance their resilience and support their overall well-being. This article, which is based on a special lecture given during the Law Enforcement and Public Health Conference held May 21–24, 2023, at Umea University in Umea, Sweden, uses phenomenology to examine the awe stories and experiences of police participants who took part in a resilience program as well as feedback during the conference discussion. The analysis demonstrates that awe narratives can serve as a gateway to other resilience practices including cognitive reappraisal, emotional intelligence, gratitude, humility, finding meaning and purpose in life, mindfulness, optimism and hope, self-compassion, self-efficacy, social connection, and managing uncertainty and ambiguity. Based on the findings, awe narratives should be considered for implementation in future police mental health and resilience training as an evidence-based practice to support the police workforce.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".