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将 PACIC 回归基础:慢性病护理患者评估的结构。

Taking the PACIC back to basics: the structure of the Patient Assessment of Chronic Illness Care.

机构信息

School of Psychology, Massey University, Palmerston North, New Zealand.

出版信息

J Eval Clin Pract. 2012 Apr;18(2):307-12. doi: 10.1111/j.1365-2753.2010.01568.x. Epub 2010 Oct 25.

Abstract

RATIONALE, AIMS AND OBJECTIVES: The Patient Assessment of Chronic Illness Care (PACIC) is a widely used 20-item measure consisting of five subscales. Published factor analyses of PACIC scores have produced conflicting results on the measure's factorial validity, and therefore some confusion as to the utility of its subscales. We aim to reduce this confusion by reviewing the evidence on the PACIC's factorial validity, exploring the statistical issues it raises, and considering more broadly what such analyses can reveal about the validity of the PACIC.

METHODS

To achieve these aims we review six published studies on the PACIC's factorial validity, present confirmatory factor analyses of our own PACIC data from 251 chronic care patients, and assess the PACIC with respect to its status as a reflective or a formative measure.

RESULTS

Our statistical analyses support the view that a 5-factor model does not fit the structure of the PACIC, and highlight a variety of technical issues that confront researchers who wish to factor analyse the measure. However, we argue that, as the PACIC is more accurately seen as a formative measure, such analyses do not provide information that should be used to assess the PACIC's validity.

CONCLUSIONS

We conclude that, while it is important to continue examining the reliability and validity of the PACIC in a variety of ways, traditional analyses of its factorial validity (and internal consistency) are inappropriate. Meanwhile, use of the subscales is defensible as long as they continue to meet other types of reliability and validity requirements.

摘要

背景、目的和目标:慢性疾病护理患者评估(PACIC)是一种广泛使用的 20 项测量工具,由五个分量表组成。PACIC 评分的已发表因子分析对该测量工具的因子有效性产生了相互矛盾的结果,因此对其分量表的实用性存在一些混淆。我们旨在通过回顾 PACIC 因子有效性的证据,探讨其提出的统计问题,并更广泛地考虑此类分析可以揭示 PACIC 的有效性,从而减少这种混淆。

方法

为了实现这些目标,我们回顾了六项关于 PACIC 因子有效性的已发表研究,展示了我们自己来自 251 名慢性护理患者的 PACIC 数据的验证性因子分析,并评估了 PACIC 作为反射性或形成性测量的地位。

结果

我们的统计分析支持以下观点,即 5 因子模型不符合 PACIC 的结构,并突出了希望对该测量进行因子分析的研究人员所面临的各种技术问题。然而,我们认为,由于 PACIC 更准确地被视为形成性测量,因此此类分析并未提供应用于评估 PACIC 有效性的信息。

结论

我们得出的结论是,虽然继续以各种方式检查 PACIC 的可靠性和有效性非常重要,但对其因子有效性(和内部一致性)的传统分析是不适当的。同时,只要它们继续满足其他类型的可靠性和有效性要求,就可以使用分量表。

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