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开发并测试健康状态描述的多媒体展示。

Developing and testing a multimedia presentation of a health-state description.

作者信息

Goldstein M K, Clarke A E, Michelson D, Garber A M, Bergen M R, Lenert L A

机构信息

Department of Medicine, Stanford University School of Medicine, CA 94305-5113.

出版信息

Med Decis Making. 1994 Oct-Dec;14(4):336-44. doi: 10.1177/0272989X9401400404.

Abstract

Quality-adjustment weights for health states are an essential component of cost-utility analysis (CUA). Quality-adjustment weights are obtained by presenting large numbers of subjects with multiattribute descriptions of health states for rating. Comprehending multiattribute health states is a difficult task for most respondents. The authors hypothesized that multimedia (MM) presentation using computers might facilitate this task better than would a paper-based text (Text). To test this hypothesis, they developed closely matched MM and Text descriptions of health states in the first-person narrative style, and developed a method of testing the presentation of a health state. Subjects were randomized to exposure to either MM or Text and subject recall of the health state and recognition of features of the health state were tested. How well defined the preferences of the subjects were after each presentation method was assessed by having the subjects mark on a double-anchored visual-analog scale the "best" and "worst" they believed the quality of life in the health state might be. MM subjects had better recall (11.85 vs 9.44 of a total of 24 meaning units, p = 0.098) and better recognition (4.71 vs 4.22, p = 0.08). The average interval between the "best" and "worst" ratings was shorter for the MM subjects (2.19 cm vs 3.26 cm, p = 0.12).(ABSTRACT TRUNCATED AT 250 WORDS)

摘要

健康状态的质量调整权重是成本效用分析(CUA)的重要组成部分。质量调整权重是通过向大量受试者呈现健康状态的多属性描述以供评级而获得的。对于大多数受访者来说,理解多属性健康状态是一项艰巨的任务。作者假设,使用计算机的多媒体(MM)呈现方式可能比纸质文本(Text)更能促进这项任务。为了验证这一假设,他们以第一人称叙述的方式开发了与健康状态紧密匹配的MM和文本描述,并开发了一种测试健康状态呈现的方法。受试者被随机分为接触MM或文本两组,并测试他们对健康状态的回忆和对健康状态特征的识别。通过让受试者在双锚视觉模拟量表上标记他们认为健康状态下生活质量可能的“最佳”和“最差”情况,来评估每种呈现方法后受试者偏好的明确程度。MM组受试者的回忆更好(在总共24个意义单元中,MM组为11.85,文本组为9.44,p = 0.098),识别也更好(4.71对4.22,p = 0.08)。MM组受试者“最佳”和“最差”评分之间的平均间隔更短(2.19厘米对3.26厘米,p = 0.12)。(摘要截断于250字)

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