Reducing Lethal Force Errors by Modulating Police Physiology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to test an intervention modifying officer physiology to reduce lethal force errors and improve health. METHODS: A longitudinal, within-subjects intervention study was conducted with urban front-line police officers (n = 57). The physiological intervention applied an empirically validated method of enhancing parasympathetic engagement (ie, heart rate variability biofeedback) during stressful training that required lethal force decision-making. RESULTS: Significant post-intervention reductions in lethal force errors, and in the extent and duration of autonomic arousal, were maintained across 12 months. Results at 18 months begin to return to pre-intervention levels. CONCLUSION: We provide objective evidence for a physiologically focused intervention in reducing errors in lethal force decision-making, improving health and safety for both police and the public. Results provide a timeline of skill retention, suggesting annual retraining to maintain health and safety gains.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it