Knowledge, Skills, and Abilities for Managing Potentially Volatile Police–Public Interactions: A Narrative Review
Bibliographic record
Abstract
We conducted a narrative review of existing literature to identify the knowledge, skills, and abilities (KSAs) necessary for officers who police in democratic societies to successfully manage potentially volatile police–public interactions. This review revealed 10 such KSAs that are frequently discussed in the literature. These KSAs include: (1) knowledge of policies and laws; (2) an understanding of mental health-related issues; (3) an ability to interact effectively with, and show respect for, individuals from diverse community groups; (4) awareness and management of stress effects; (5) communication skills; (6) decision-making and problem-solving skills; (7) perceptual skills; (8) motor skills related to use-of-force; (9) emotion and behavior regulation; and (10) an ability to treat people in a procedurally just manner. Following our review, we conducted semi-structured interviews (N = 7) with researchers who specialize in police training and adult education, interactions with individuals in crisis, and racialized policing, as well as two police trainers with expertise in de-escalation and use-of-force training. These interviews confirmed the importance of the 10 KSAs and highlighted two additional KSAs that are likely to be critical: understanding the role of policing in a free and democratic society and tactical knowledge and skills. To ensure that police–public interactions are managed effectively, police trainers may want to focus on the development and evaluation of these KSAs—something that is not always done currently.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".