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认知扭曲问卷的优化短式版本。

Optimized short-forms of the Cognitive Distortions Questionnaire.

机构信息

Department of Psychology, California State University, East Bay, Hayward, CA, USA.

Halıcıoğlu Data Science Institute, UC San Diego, La Jolla, CA, USA.

出版信息

J Anxiety Disord. 2022 Dec;92:102624. doi: 10.1016/j.janxdis.2022.102624. Epub 2022 Aug 20.

DOI:10.1016/j.janxdis.2022.102624
PMID:36087565
Abstract

INTRODUCTION

The Cognitive Distortions Questionnaire (CD-Quest) is a self-report questionnaire that assesses common cognitive distortions. Although the CD-Quest has excellent psychometric properties, its length may limit its use.

METHODS

We attempted to develop short-forms of the CD-Quest using RiskSLIM - a machine learning method to build short-form scales that can be scored by hand. Each short-form was fit to maximize concordance with the total CD-Quest score for a specified number of items based on an objective function, in this case R, by selecting an optimal subset of items and an optimal set of small integer weights. The models were trained in a sample of US undergraduate students (N = 906). We then validated each short-form on five independent samples: two samples of undergraduate students in Brazil (Ns = 182, 183); patients with depression in Brazil (N = 62); patients with social anxiety disorder in the US (N = 198); and psychiatric outpatients in Turkey (N = 269).

RESULTS

A 9-item short-form with integer scoring was created that reproduced the total 15-item CD-Quest score in all validation samples with excellent accuracy (R = 90.4-93.6%). A 5-item ultra-short-form had good accuracy (R = 78.2-85.5%).

DISCUSSION

A 9-item short-form and a 5-item ultra-short-form of the CD-Quest both reproduced full CD-Quest scores with excellent to good accuracy. These shorter versions of the full CD-Quest could facilitate measurement of cognitive distortions for users with limited time and resources.

摘要

简介

认知扭曲问卷(CD-Quest)是一种自我报告问卷,用于评估常见的认知扭曲。尽管 CD-Quest 具有出色的心理测量学特性,但它的长度可能会限制其使用。

方法

我们尝试使用 RiskSLIM 开发 CD-Quest 的简短形式 - 一种用于构建可以通过手动评分的简短形式量表的机器学习方法。根据目标函数(在这种情况下为 R),通过选择最佳项目子集和最佳小整数权重集,为每个简短形式拟合以最大限度地与指定数量项目的总 CD-Quest 分数一致。模型在一组美国本科生样本(N = 906)中进行了训练。然后,我们在五个独立的样本中验证了每个简短形式:两个巴西本科生样本(N = 182,183);巴西的抑郁症患者(N = 62);美国的社交焦虑症患者(N = 198);土耳其的精神病门诊患者(N = 269)。

结果

创建了一个 9 项整数评分的简短形式,在所有验证样本中都以极好的准确性再现了 15 项总 CD-Quest 评分(R = 90.4-93.6%)。具有良好准确性的 5 项超简短形式(R = 78.2-85.5%)。

讨论

CD-Quest 的 9 项简短形式和 5 项超简短形式都以极好到良好的准确性再现了完整的 CD-Quest 评分。这些简短形式的完整 CD-Quest 可以为时间和资源有限的用户提供更方便的认知扭曲测量。

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