An evidence-based approach to critical incident scenario development
Bibliographic record
Abstract
Purpose There has been an increasing emphasis on developing officers who can effectively make decisions in dynamic and stressful environments to manage volatile situations. The aim of this paper is to guide those seeking to optimize the limited resources dedicated to police training. Design/methodology/approach Drawing on research related to stress exposure training, principles of adult learning, the event-based approach to training and policing more broadly, the authors show how carefully crafted training scenarios can maximize the benefits of police training. Findings The authors’ review highlights various training principles that, if relied on, can result in scenarios that are likely to result in the development of flexible, sound decision-making skills when operating under stressful conditions. The paper concludes with an example of scenario development, which takes the reviewed principles into account. Originality/value The authors hope this discussion will be useful for police instructors and curriculum designers in making evidence-informed decisions when designing training scenarios.
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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 | 0.208 | 0.348 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.025 | 0.010 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.014 | 0.017 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".