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验证人格评估量表(PAI)在混合临床样本中的量表。

Validation of the Personality Assessment Inventory (PAI) scale of scales in a mixed clinical sample.

机构信息

Department of Psychiatry, University of Iowa Hospitals and Clinics, Iowa City, IA, USA.

New Mexico VA Health Care System, Albuquerque, NM, USA.

出版信息

Clin Neuropsychol. 2022 Oct;36(7):1844-1859. doi: 10.1080/13854046.2021.1900400. Epub 2021 Mar 17.

Abstract

This exploratory study examined the classification accuracy of three derived scales aimed at detecting cognitive response bias in neuropsychological samples. The derived scales are composed of existing scales from the Personality Assessment Inventory (PAI). A mixed clinical sample of consecutive outpatients referred for neuropsychological assessment at a large Midwestern academic medical center was utilized. Participants included 332 patients who completed study's embedded and free-standing performance validity tests (PVTs) and the PAI. PASS and FAIL groups were created based on PVT performance to evaluate the classification accuracy of the derived scales. Three new scales, Cognitive Bias Scale of Scales 1-3, (CB-SOS1-3) were derived by combining existing scales by either summing the scales together and dividing by the total number of scales summed, or by logistically deriving a variable from the contributions of several scales. All of the newly derived scales significantly differentiated between PASS and FAIL groups. All of the derived SOS scales demonstrated acceptable classification accuracy (i.e. CB-SOS1 AUC = 0.72; CB-SOS2 AUC = 0.73; CB-SOS3 AUC = 0.75). This exploratory study demonstrates that attending to scale-level PAI data may be a promising area of research in improving prediction of PVT failure.

摘要

这项探索性研究考察了旨在检测神经心理样本中认知反应偏差的三个衍生量表的分类准确性。衍生量表由人格评估量表(PAI)中的现有量表组成。利用了一个在中西部大型学术医疗中心进行神经心理评估的连续门诊患者的混合临床样本。参与者包括 332 名完成研究嵌入式和独立绩效有效性测试(PVT)和 PAI 的患者。根据 PVT 表现创建了 PASS 和 FAIL 组,以评估衍生量表的分类准确性。通过将现有量表相加并除以相加的量表总数,或通过对数推导几个量表的贡献变量,组合现有的量表来衍生三个新的量表,即量表 1-3 的认知偏差量表(CB-SOS1-3)。所有新衍生的量表均能显著区分 PASS 和 FAIL 组。所有衍生的 SOS 量表均表现出可接受的分类准确性(即 CB-SOS1 AUC = 0.72;CB-SOS2 AUC = 0.73;CB-SOS3 AUC = 0.75)。这项探索性研究表明,关注量表级别的 PAI 数据可能是提高 PVT 失败预测的一个有前途的研究领域。

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