From pain compliance to leverage-based control: Evidence of reduced use of force severity and injuries following police training

Jessica Huff, Sean Zauhar, Denis Agniel
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Abstract

Training is a frequently requested response to contentious police use of force incidents. Yet limited research evaluating use of force training has been conducted and most has focussed on officer perceptions of training, as opposed to the impact of training on use of force in the field. We address this gap through evaluating a 120-h Response to Resistance and Aggression training developed and implemented by the Saint Paul Police Department. By integrating leverage-based control and de-escalation techniques, this program sought to reduce the severity of force used by police officers. Results from machine learning models indicate that training did reduce reliance on pain-compliance techniques. Adjusting for factors including encounter, subject, and officer characteristics, training was associated with an estimated 3.3 percentage point reduction in officer injuries and a 1.3 percentage point reduction in significant subject injury. These findings contribute to the evidence base surrounding effective police training programs.
从顺从疼痛到基于杠杆的控制:警察培训后使用武力的严重程度和伤害减少的证据
对于有争议的警察使用武力事件,培训是经常被要求的应对措施。然而,对使用武力培训的评估研究却十分有限,而且大多数研究都集中在警官对培训的看法上,而不是培训对现场使用武力的影响上。我们通过评估圣保罗警察局开发和实施的 120 小时 "应对反抗和攻击 "培训,弥补了这一不足。通过整合基于杠杆的控制和降级技术,该计划旨在降低警察使用武力的严重程度。机器学习模型的结果表明,培训确实减少了对疼痛顺从技术的依赖。在对包括遭遇、目标和警官特征在内的因素进行调整后,培训与警官受伤率降低 3.3 个百分点和目标严重受伤率降低 1.3 个百分点有关。这些发现为有效的警察培训计划提供了证据基础。
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