Monitoring the Impact of Scenario-based Use-of-force Simulations on Police Heart Rate: Evaluating the Royal Canadian Mounted Police Skills Refresher Program
Bibliographic record
Abstract
This research aimed to establish the extent to which scenario-based use-of-force training undertaken by the Royal Canadian Mounted Police (RCMP) replicates aspects of the essential physiological characteristics of real-life, highstress police activity. Using heart rate monitors, the physiological stress reactions of 132 officers were recorded while they completed one of four use-of-force training scenarios (including a control, where no use-of-force was required). Average heart rate information was used as a proxy measure for officer stress reactions at four time points during the scenarios: (a) 10 minute pre-scenario, (b) during the scenario when verbal contact was made, (c) during the scenario when physical contact was made, and (d) 10 minute post-scenario. Relative to pre- and post-scenario rates, heart rates were elevated during verbal and physical contact. No differences in this pattern were observed between scenarios, including the control scenario. Relative to previous use-of-force simulation evaluations, the strengths of this design are the size and quality of the sample of participants, the collection of the stress proxy measure during the scenarios, and the inclusion of a control scenario. Overall, this examination demonstrated that the RCMP's current scenario-based use-of-force skills refresher program produces heart rate patterns that are consistent with the elevated physiological stress produced by real-world policing as demonstrated in prior field research.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".