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基于拉施模型的结果潜在特质测量:深入应用可实现功能性医疗保健中的精准病例管理和循证实践。

Rasch-derived latent trait measurement of outcomes: insightful use leads to precision case management and evidence-based practices in functional healthcare.

作者信息

Granger Carl V, Carlin Marsha, Linacre John M, Mead Ronald, Niewczyk Paulette, Stenner A Jackson, Tesio Luigi

机构信息

Department of Rehabilitation Medicine, University at Buffalo, The State University of New York, NY, USA.

出版信息

J Appl Meas. 2010;11(3):230-43.

Abstract

The use of Rasch-derived latent trait measurement of outcomes for persons with chronic disease and disablement evolved from other fields, particularly education. Person-metrics is the measurement of how much chronic disease and disablement affects an individual's daily activities physically, cognitively, and through vocational and social role participation. The ability of the Rasch model to assume that the probability of a given person/item interaction is governed by the difficulty of the item and the ability of the person is invaluable to disability measurement. The difference between raw scores and true measures is illustrated by an example of a patient whose physical difficulty is rated on rising from a wheelchair and walking 100m (known to be more difficult), and then walking an additional 200m. Though number ratings of 0-1-2 are assigned to these tasks, they are not equidistant, and only a true measure shows the actual levels of physical difficulty.

摘要

针对慢性病患者和残疾人的基于拉施模型得出的潜在特质结果测量方法源自其他领域,尤其是教育领域。个体指标衡量的是慢性病和残疾在身体、认知方面以及通过职业和社会角色参与对个人日常活动产生的影响程度。拉施模型假定给定的人与项目互动的概率由项目难度和个人能力决定,这一特性对于残疾测量而言非常重要。原始分数与真实测量值之间的差异通过一个例子来说明:有一位患者,从轮椅起身行走100米(已知难度更大),然后再行走额外的200米,对其身体困难程度进行数字评分,尽管这些任务被赋予了0 - 1 - 2的数字评分,但它们并非等距的,只有真实测量值才能显示身体困难的实际水平。

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