Hartford Kathleen, Heslop Lisa, Stitt Larry, Hoch Jeffrey S
Scientist-Epidemiologist, Lawson Health Research Institute, Associate Professor, Faculties of Health Sciences, Medicine and Dentistry, University of Western Ontario, 375 South Street, NRA220, London, Ontario, Canada.
Int J Law Psychiatry. 2005 Jan-Feb;28(1):1-11. doi: 10.1016/j.ijlp.2004.12.001. Epub 2005 Jan 18.
North American police maintain a database to track events and information related to their involvement with the public that contain a series of electronic caution/dependency flags attached to an individual's name for internal communication. To identify persons with mental illness in a police administrative database, an algorithm was developed that was composed of (a) caution/dependency flags, (b) addresses, and (c) key search words indicative of mental illness. Based on the level of confidence of the algorithm, persons with mental illness (PMI) were then assigned to one of three categories: Definite, Probable and Possible PMI. Results for 2000 include the sociodemographic characteristics of PMI and non-PMI in the database. The mean number of contacts, types of interactions, re-involvement with a year, charges and dispositions are described. The algorithm provides a cheap, quick method to identify PMI for North American police. It enables police to monitor the effectiveness of pre-arrest diversion programs and allows researchers to analyze questions of criminalization and mental illness.
北美警方维护一个数据库，用于跟踪与他们与公众互动相关的事件和信息，该数据库包含一系列附加在个人姓名上的电子警示/依赖标记，用于内部通信。为了在警方管理数据库中识别患有精神疾病的人，开发了一种算法，该算法由（a）警示/依赖标记、（b）地址和（c）表明精神疾病的关键搜索词组成。根据该算法的置信度水平，患有精神疾病的人（PMI）随后被分为三类之一：确诊、可能和疑似PMI。2000年的结果包括数据库中PMI和非PMI的社会人口特征。描述了接触的平均次数、互动类型、一年内再次卷入情况、指控和处置情况。该算法为北美警方提供了一种廉价、快速的识别PMI的方法。它使警方能够监测逮捕前分流计划的有效性，并使研究人员能够分析刑事定罪和精神疾病问题。
