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精神科司法鉴定住院患者的自伤行为:探索性分析。

Self-Harm Among Forensic Psychiatric Inpatients With Schizophrenia Spectrum Disorders: An Explorative Analysis.

机构信息

Psychiatric University Hospital Zurich, Switzerland.

University of Zurich, Switzerland.

出版信息

Int J Offender Ther Comp Criminol. 2023 Mar;67(4):352-372. doi: 10.1177/0306624X211062139. Epub 2021 Dec 3.

Abstract

The burden of self-injury among offenders undergoing inpatient treatment in forensic psychiatry is substantial. This exploratory study aims to add to the previously sparse literature on the correlates of self-injury in inpatient forensic patients with schizophrenia spectrum disorders (SSD). Employing a sample of 356 inpatients with SSD treated in a Swiss forensic psychiatry hospital, patient data on 512 potential predictor variables were retrospectively collected via file analysis. The dataset was examined using supervised machine learning to distinguish between patients who had engaged in self-injurious behavior during forensic hospitalization and those who had not. Based on a combination of ten variables, including psychiatric history, criminal history, psychopathology, and pharmacotherapy, the final machine learning model was able to discriminate between self-injury and no self-injury with a balanced accuracy of 68% and a predictive power of AUC = 71%. Results suggest that forensic psychiatric patients with SSD who self-injured were younger both at the time of onset and at the time of first entry into the federal criminal record. They exhibited more severe psychopathological symptoms at the time of admission, including higher levels of depression and anxiety and greater difficulty with abstract reasoning. Of all the predictors identified, symptoms of depression and anxiety may be the most promising treatment targets for the prevention of self-injury in inpatient forensic patients with SSD due to their modifiability and should be further substantiated in future studies.

摘要

在接受法医精神病学住院治疗的罪犯中,自残的负担相当大。这项探索性研究旨在补充先前关于精神分裂症谱系障碍(SSD)住院法医患者自残相关因素的文献。该研究采用了瑞士一家法医精神病院治疗的 356 名 SSD 住院患者的样本,通过文件分析回顾性地收集了 512 个潜在预测变量的患者数据。使用监督机器学习检查数据集,以区分在法医住院期间进行自残行为的患者和未进行自残行为的患者。基于包括精神病病史、犯罪史、精神病理学和药物治疗在内的十个变量的组合,最终的机器学习模型能够以 68%的平衡准确性和预测能力 AUC=71%区分自残和无自残。结果表明,自残的 SSD 法医精神病患者在发病时和首次进入联邦犯罪记录时都更年轻。他们在入院时表现出更严重的精神病理学症状,包括更高水平的抑郁和焦虑,以及更难以进行抽象推理。在所确定的所有预测因素中,抑郁和焦虑症状可能是预防 SSD 住院法医患者自残的最有前途的治疗目标,因为它们具有可改变性,应该在未来的研究中进一步证实。

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