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使用结构化专业判断预测不同精神和行为障碍患者的暴力行为。

Predicting violence using structured professional judgment in patients with different mental and behavioral disorders.

机构信息

Ty Catrin Personality Disorder Service, Pastoral Cymru, Cardiff CF5 5AD, UK.

出版信息

Psychiatry Res. 2011 May 15;187(1-2):248-53. doi: 10.1016/j.psychres.2010.10.011. Epub 2010 Nov 20.

Abstract

We examined whether a leading instrument for the prediction of future violence in those with a mental disorder. The Historical, Clinical, Risk Management-20 (HCR-20) was equally effective across a wide range of mental health diagnoses. Records at the time of discharge from secure psychiatric services were used to score the HCR-20 risk assessment scheme. Patients were stratified according to whether they had received a particular mental health diagnosis. Reconvictions within 2 years of discharge were obtained from official sources and classified as to whether the offence was violent or not. Those with a diagnosis of either personality disorder or substance abuse were most likely to be reconvicted, whilst those with either a diagnosis of schizophrenia or mental retardation were the least likely. The HCR-20 was a statistically significant predictor of future violence in all groups; however, it returned only weak effects for the personality disordered group, but strong effects for those in the schizophrenia or mental retardation group. The HCR-20 risk assessment scheme is effective across a wide range of diagnoses. Nevertheless, the prediction of future events appears more difficult in those disorders characterized by impulsive behaviors and further research efforts are needed to understand how such prediction can be improved.

摘要

我们研究了一种用于预测精神障碍患者未来暴力行为的主要工具,即历史、临床、风险管理-20 版(HCR-20),其在广泛的精神健康诊断中是否同样有效。使用从安全精神病服务机构出院时的记录对 HCR-20 风险评估方案进行评分。根据他们是否接受过特定的精神健康诊断,将患者分层。从官方来源获得出院后 2 年内的重审,并将犯罪分类为暴力犯罪或非暴力犯罪。患有人格障碍或药物滥用诊断的患者最有可能被重审,而患有精神分裂症或智力迟钝诊断的患者则最不可能被重审。HCR-20 在所有群体中都是未来暴力行为的统计学显著预测因子;然而,它对人格障碍组的预测效果较弱,而对精神分裂症或智力迟钝组的预测效果较强。HCR-20 风险评估方案在广泛的诊断中都有效。然而,对于以冲动行为为特征的障碍,对未来事件的预测似乎更加困难,需要进一步的研究努力来了解如何改进这种预测。

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