Neal Tess M S
New College of Interdisciplinary Arts & Sciences, Arizona State University, Glendale, AZ, USA.
Behav Sci Law. 2018 May;36(3):325-338. doi: 10.1002/bsl.2346. Epub 2018 Apr 19.
This project began as an attempt to develop systematic, measurable indicators of bias in written forensic mental health evaluations focused on the issue of insanity. Although forensic clinicians observed in this study did vary systematically in their report-writing behaviors on several of the indicators of interest, the data are most useful in demonstrating how and why bias is hard to ferret out. Naturalistic data were used in this project (i.e., 122 real forensic insanity reports), which in some ways is a strength. However, given the nature of bias and the problem of inferring whether a particular judgment is biased, naturalistic data also made arriving at conclusions about bias difficult. This paper describes the nature of bias - including why it is a special problem in insanity evaluations - and why it is hard to study and document. It details the efforts made in an attempt to find systematic indicators of potential bias, and how this effort was successful in part, but also how and why it failed. The lessons these efforts yield for future research are described. We close with a discussion of the limitations of this study and future directions for work in this area.
本项目始于一项尝试,即针对精神错乱问题,开发系统的、可衡量的书面法医精神健康评估中的偏差指标。尽管本研究中观察到的法医临床医生在若干感兴趣的指标上的报告撰写行为确实存在系统差异,但这些数据在展示偏差难以查明的方式和原因方面最为有用。本项目使用了自然主义数据(即122份真实的法医精神错乱报告),这在某些方面是一个优势。然而,鉴于偏差的性质以及推断特定判断是否存在偏差的问题,自然主义数据也使得得出关于偏差的结论变得困难。本文描述了偏差的性质——包括为什么它在精神错乱评估中是一个特殊问题——以及为什么它难以研究和记录。它详细说明了为寻找潜在偏差的系统指标所做的努力,以及这项努力如何部分取得成功,又如何以及为何失败。描述了这些努力为未来研究带来的经验教训。我们最后讨论了本研究的局限性以及该领域未来的工作方向。