Clemmow Caitlin, Fowler Nicola, Seaward Amber, Gill Paul
Department of Security & Crime Science, UCL, London, UK.
Prevent In-Place Team, Birmingham & Solihull Mental Health NHS Foundation Trust, Birmingham, UK.
Behav Sci Law. 2025 Mar-Apr;43(2):228-247. doi: 10.1002/bsl.2710. Epub 2024 Dec 8.
Best practice in violent extremist risk assessment and management recommends adopting a Structured Professional Judgement (SPJ) approach. The SPJ approach identifies relevant, evidence-based risk and protective factors and requires experts to articulate hypotheses about a) what the person might do (risk of what), and b) how they've come to engage in the concerning behaviour (and why) (Logan 2021) to inform who, needs to do what, and when. Whilst the field continues to move towards adopting an SPJ approach, there remains a gap between what is known empirically and what is needed in practice. We apply psychometric network modelling to a sample of 485 individuals entered into Channel, the UK's preventing and countering violent extremism (P/CVE) program. We model the system of interactions from which susceptibility to violent extremism emerges, providing data driven evidence which speaks to risk of what and why. Our research highlights a way to generate evidence which captures the multifactorial nature of susceptibility to violent extremism, to support professional decision making in the context of an SPJ approach.
暴力极端主义风险评估与管理的最佳实践建议采用结构化专业判断（SPJ）方法。SPJ方法识别相关的、基于证据的风险和保护因素，并要求专家阐明关于以下方面的假设：a）此人可能会做什么（做什么的风险），以及b）他们是如何开始从事相关行为的（以及原因）（洛根，2021年），以便告知谁需要做什么以及何时去做。虽然该领域继续朝着采用SPJ方法发展，但实证所知与实践所需之间仍存在差距。我们将心理测量网络建模应用于参与英国预防和打击暴力极端主义（P/CVE）项目“通道”的485名个体样本。我们对暴力极端主义易感性产生的互动系统进行建模，提供数据驱动的证据，说明做什么的风险以及原因。我们的研究突出了一种生成证据的方法，该方法捕捉了暴力极端主义易感性的多因素性质，以支持在SPJ方法背景下的专业决策。
