Testing the Efficacy of a 1-Day Police Decision-Making and Autonomic Modulation Intervention: A Quasi-Random Pragmatic Controlled Trial
Bibliographic record
Abstract
Contemporary discourse has identified several urgent priorities concerning police training and education, including: (a) empirically testing and validating the effectiveness of current programming in reducing lethal force decision-making errors; (b) integrating evidence-based content and pedagogical approaches into police curriculum; and (c) understanding the breadth and length of programming necessary to ensure learning and transfer of skills to operational field settings. Widespread calls to identify effective and actionable training programs have been met with numerous research studies, systematic reviews, and policy recommendations that reveal the need to train officers’ internal physiological awareness, which is foundational in shaping cognitive decision-making, emotion regulation, and behavior under stressful conditions. Several investigations have shown improvements to both lethal force errors and physiological recovery following a multi-day autonomic modulation (AM) intervention. Immediate and sustained training gains are observed following repeated practice with clinically validated protocols integrated into training scenarios. Despite evidence-based support for AM in addressing the aforementioned priorities, police organizations are faced with limited time and funding for training and education. The goal of the current quasi-random pragmatic controlled trial was to evaluate the effectiveness of a modified 1-day version of an established AM intervention. A sample of active-duty police officers were quasi-randomly assigned to an AM intervention (n = 82) or waitlist control group (n = 105). Lethal force errors and objective measures of autonomic arousal and recovery were measured during reality-based scenarios pre- and post-training and at 12-month follow-up. In contrast to previous investigations of longer AM intervention protocols, no significant training-related improvements to behavioral or physiological outcomes were found immediately post-intervention or at follow-up. The current results suggest that single-day training is insufficient to learn the physiological awareness and regulation skills necessary to perform effectively during lethal force encounters, as demonstrated by a lack of immediate or sustained training effects. Practical considerations, such as resource allocation, that may undermine the effectiveness of implementing evidence-based police training are discussed.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".