A Training Method to Improve Police Use of Force Decision Making
Bibliographic record
Abstract
Police safety and use of force decisions during critical incidents are an ongoing source of concern for both police practitioners and the public. Prior research in the area of police performance reveals that psychological and physiological stress responses during critical incidents can shape the outcome of the incident, either positively or negatively. The goal of this study was to test a training method to improve use of force decision making among police. This randomized controlled pilot study consisted of training officers to apply techniques to enhance psychological and physiological control during stressful critical incidents. Of a pool of 80 police officers, potential participants were invited based on equivalent age, years of experience, physiological characteristics (i.e., body mass index [BMI] and cardiovascular reactivity), and expertise. Results revealed that the intervention group displayed significantly better physiological control, situational awareness, and overall performance, and made a greater number of correct use of force decisions than officers in the control group (all ps < .01). The relevant improvements in use of force decision-making found in this pilot study indicate that this training method warrants further investigation. Improved use of force decision making directly translates into potential lifesaving decisions for police and the civilians they are working with.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".