Police lethal force errors and stress physiology during video and live evaluation simulations
Bibliographic record
Abstract
Police officers are regularly evaluated for their competency in a variety of skills related to use of force (UOF), including lethal force decision-making, which is usually tested using stressful reality-based scenarios in virtual or live formats. The current observational study fills a literature gap by examining performance (i.e., shoot/no-shoot errors) and stress physiology among 187 police officers during virtual (i.e., video-based) and live UOF scenarios as part of their agency’s annual requalification assessment. While moderately low rates of lethal force errors we\re observed overall, there were significantly fewer errors in live (0.81%) versus video scenarios (5.92%). Both conditions elicited significant stress physiology, as measured by heart rate (HR) relative to rest, with higher maximum heart rate in live scenarios. Based on emerging empirical literature and the current findings, we contribute to the discussion on the practical benefits and limitations of video and live simulation approaches in policing. We also provide evidence-based recommendations on how each approach may be most effectively employed for the purpose of evaluating police officers’ UOF skills.
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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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".