Promising practices for de-escalation and use-of-force training in the police setting: a narrative review
Bibliographic record
Abstract
Purpose A narrative review of existing research literature was conducted to identify practices that are likely to improve the quality of de-escalation and use-of-force training for police officers. Design/methodology/approach Previous reviews of de-escalation and use-of-force training literature were examined to identify promising training practices, and more targeted literature searches of various databases were undertaken to learn more about the potential impact of each practice on a trainee's ability to learn, retain, and transfer their training. Semi-structured interviews with five subject matter experts were also conducted to assess the degree to which they believed the identified practices were relevant to de-escalation and use-of-force training, and would enhance the quality of such training. Findings Twenty practices emerged from the literature search. Each was deemed relevant and useful by the subject matter experts. These could be mapped on to four elements of training: (1) commitment to training (e.g. securing organizational support for training), (2) development of training (e.g. aligning training formats with learning objectives), (3) implementation of training (e.g. providing effective corrective feedback) and (4) evaluation and ongoing assessment of training (e.g. using multifaceted evaluation tools to monitor and modify training as necessary). Originality/value This review of training practices that may be relevant to de-escalation and use-of-force training is the broadest one conducted to date. The review should prompt more organized attempts to quantify the effectiveness of the training practices (e.g. through meta-analyses), and encourage more focused testing in a police training environment to determine their impact.
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How this classification was reachedexpand
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".