Development of the Simplified-Chinese Version of DISCERN: Translation, Adaptation, and Validation (Preprint)

Author:

Shan Yi,Xing Zhaoquan,Dong Zhaogang,Ji MengORCID,Wang Ding,Cao Xiangting

Abstract

BACKGROUND

There is a wide variation in the quality of information available to patients who seek information on the treatment of the diseases afflicting them. To help patients find clear and accessible information, many scales have been designed to evaluate the quality of health information. These instruments are primarily in English. Few of them have been translated and adapted into simplified-Chinese tools for health information assessment in China.

OBJECTIVE

This study aimed to translate and adapt DISCERN into the first simplified-Chinese version and to validate the psychometric properties of this newly-developed scale for judging the quality of patient-oriented health information on treatment choices in China.

METHODS

First we translated DICERN into simplified-Chinese using rigorous guidelines for translation and validation studies. We tested translation equivalence and measured the content validity index. Then we presented the simplified-Chinese instrument to 2 health educators and asked them to use it to assess the quality of 15 lung cancer-related materials. Subsequently, we invited another 3 health educators to rate 1 lung cancer-related brochure. Finally, we calculated Cohen’s kappa coefficient and Cronbach’s alpha to determine the reliability of the new instrument.

RESULTS

We decided on the final version of the simplified-Chinese DISCERN (C-DISCERN) after resolving all problems in translation, adaptation, and content validation. The C-DISCERN was valid and reliable: (1) the content validity index was .98 (47/48) in clarity and .94 (45/48) in relevance; (2) Cohen’s kappa coefficient for interrater agreement was .53 (p<.05); and (3) Cronbach’s alpha for internal consistency was .93 (confidence interval=95%) for the whole translated scale.

CONCLUSIONS

The C-DISCERN is the first simplified-Chinese version of the DISCERN instrument. Its validity and reliability has been attested for assessing the quality of patient-targeted information for treatment choices.

Publisher

JMIR Publications Inc.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3