Applying Resilience Promotion Training Among Special Forces Police Officers
Bibliographic record
Abstract
Police Special Forces (a.k.a. special weapons and tactics [SWAT]) officers are tasked with responding to the most critical situations, including incidents that require specialized skills and equipment beyond typical policing activities. In this study, we tested the feasibility of applying Arnetz and colleagues’ resilience promotion training that was developed for patrol officers to SWAT team officers ( n = 18). The resilience promotion training program included psychoeducation focused on police stress and resilience, and the practice of resilience promotion techniques (controlled breathing and imagery) while listening to audio-recorded critical incident scenarios. The aims of this study were to (a) examine if a resilience training program was relevant and accepted by SWAT team officers and (b) assess participants’ physiological stress responses (heart rate, respiration) during the resilience training sessions to note if there were improvements in stress responding over time. Our findings revealed that participants were able to significantly reduce their average heart rate and improve their ability to engage in controlled respiration (i.e., breathing) during simulated critical incidents over the course of the 5-day training. Improvements in stress responding were observed even when the critical incident scenarios became more graphic. Results suggest that an intervention to reduce stress responses of SWAT officers to critical incident scenarios works in a simulated training setting. Translation of these findings to real-world occupational hazards is a recommended next step.
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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 | 0.002 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".