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An Informatics Framework to Assess Consumer Health Language Complexity Differences: Proof-of-Concept Study

机译:评估消费者健康语言复杂性差异的信息框架:概念证明研究

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Background The language gap between health consumers and health professionals has been long recognized as the main hindrance to effective health information comprehension. Although providing health information access in consumer health language (CHL) is widely accepted as the solution to the problem, health consumers are found to have varying health language preferences and proficiencies. To simplify health documents for heterogeneous consumer groups, it is important to quantify how CHLs are different in terms of complexity among various consumer groups. Objective This study aimed to propose an informatics framework (consumer health language complexity [CHELC]) to assess the complexity differences of CHL using syntax-level, text-level, term-level, and semantic-level complexity metrics. Specifically, we identified 8 language complexity metrics validated in previous literature and combined them into a 4-faceted framework. Through a rank-based algorithm, we developed unifying scores (CHELC scores [CHELCS]) to quantify syntax-level, text-level, term-level, semantic-level, and overall CHL complexity. We applied CHELCS to compare posts of each individual on online health forums designed for (1) the general public, (2) deaf and hearing-impaired people, and (3) people with autism spectrum disorder (ASD). Methods We examined posts with more than 4 sentences of each user from 3 health forums to understand CHL complexity differences among these groups: 12,560 posts from 3756 users in Yahoo! Answers, 25,545 posts from 1623 users in AllDeaf, and 26,484 posts from 2751 users in Wrong Planet. We calculated CHELCS for each user and compared the scores of 3 user groups (ie, deaf and hearing-impaired people, people with ASD, and the public) through 2-sample Kolmogorov-Smirnov tests and analysis of covariance tests. Results The results suggest that users in the public forum used more complex CHL, particularly more diverse semantics and more complex health terms compared with users in the ASD and deaf and hearing-impaired user forums. However, between the latter 2 groups, people with ASD used more complex words, and deaf and hearing-impaired users used more complex syntax. Conclusions Our results show that the users in 3 online forums had significantly different CHL complexities in different facets. The proposed framework and detailed measurements help to quantify these CHL complexity differences comprehensively. The results emphasize the importance of tailoring health-related content for different consumer groups with varying CHL complexities.
机译:背景技术卫生消费者和卫生专业人士之间的语言差距已经被认为是有效健康信息理解的主要障碍。尽管在消费者健康语言(CHL)中提供健康信息访问被广泛被接受作为问题的解决方案,但卫生消费者被发现有不同的健康语言偏好和潜在。为了简化异质消费者群体的健康文件,重要的是要量化CHL在各种消费群体之间的复杂性方面的不同。目的本研究旨在提出信息框架(消费者健康语言复杂性[Chelc]),以评估CHL的复杂性差异,使用语法级别,文本级别,术语级别和语义级复杂度指标。具体而言,我们确定了以前文献中验证的8个语言复杂度指标,并将它们组合成4位框架。通过基于秩的算法,我们开发了统一分数(Chelc分数[chelcs])来量化语法级,文本级别,术语级别,语义级别和整体CHL复杂性。我们将Chelcs应用于在线健康论坛上的每个人的帖子,专为(1)公众,(2)聋人和听力受损人员,(3)患有自闭症谱系(ASD)的人。方法我们从3个卫生论坛中审查了超过4个句子的帖子,以了解这些组中的CHL复杂性差异:来自雅虎3756名用户的12,560个帖子!答案,25,545个帖子来自alldeaf的1623名用户,26,484名来自2751名错误的行星。我们计算了每个用户的Chelcs,并将3个用户组(即聋人和听力受损人员,有ASD的人员和公众)的分数进行了比较到2-Sample Kolmogorov-Smirnov测试和协方差测试分析。结果结果表明,与ASD和聋人和听力受损的用户论坛中的用户相比,公共论坛中的用户使用更复杂的CHL,特别是更多样化的语义和更复杂的健康术语。但是,在后两组之间,ASD的人使用了更复杂的单词,聋哑者和听力受损的用户使用更复杂的语法。结论我们的结果表明,3在线论坛中的用户在不同方面具有显着不同的CHL复杂性。所提出的框架和详细测量有助于全面地量化这些CHL复杂性差异。结果强调了针对不同消费群体定制与不同消费者群体的健康相关内容的重要性,不同的CHL复杂性。

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