Large-scale evaluation of text features affecting perceived and actual text diffi

描述(申请人提供)：随着越来越多的医学测试和治疗，越来越多的患者被诊断出患有需要终身管理的慢性病，以及临床医生在有限的时间内看更多患者的压力越来越大，患者学习和了解如何最好地照顾自己的健康是至关重要的。不幸的是，估计有8900万人没有足够的健康素养来做到这一点，相关费用估计每年高达数十亿美元。尽管有许多令人兴奋的机会来教育消费者，从可视化到虚拟环境，但文本仍然是社会上所有群体可用的最有效和最具成本效益的媒介。不幸的是，现有的写作指南严重依赖可读性公式，并没有显示出对文本难度或消费者理解的影响。因此，本项目的目标是解决和克服当前可读性研究中存在的障碍。我们将解决以下障碍：1)与有代表性的消费者合作，而不是代表消费者进行评估的专家；2)使用有数千名参与者在他们自己的环境中工作的大样本，而不是在人工环境中几乎没有参与者的实验室研究；以及3)评估文本的感知和实际难度，这是研究中经常混淆的两个变量，4)发现可以在文本中自动发现的特征，以便可以开发允许临床医生高效和有效地优化其文本的难度检查器，而不需要学习指南或语言学。设计本研究将利用现代技术和资源进行。在为期两年的时间里，硕士和博士生将与首席调查员一起设计和进行研究。这项研究汇集了应用于医学信息学的计算语言学和信息科学的见解。从语言学的角度出发，我们将系统地列出可能影响理解的文本特征的良好候选者。我们将研究文本的语法、语义和组成特征。通过使用一个现代化的市场，亚马逊的机械土耳其人，我们可以让数千名参与者参与这项研究。我们将每个人成对地比较这些特征，例如，主题密度高的句子和低主题密度的句子。使用受试者内设计，我们将通过多项选择问答任务来衡量每个特征的感知和实际难度。 与公共健康相关：目前的健康信息与消费者的阅读技能不一致，这种情况导致健康素养低，医疗成本高，因为代价高昂的错误和不明智的决定。拟议的项目将与数千名有代表性的消费者合作，发现影响文本感知和实际难度的文本特征。这些功能将带来更好的编写指南和自动化工具，以帮助临床医生编写更容易理解的健康教育材料，并基于这些功能对理解的演示影响。

DESCRIPTION (provided by applicant): With increasingly more medical tests and treatments being available, more patients being diagnosed with chronic diseases that require life-long management, and increasing pressure on clinicians to see more patients in a limited amount of time, it is essential that patients learn and understand how best to take care of their health. Unfortunately, an estimated 89 million people have insufficient health literacy to do just that and the associated costs are estimated to be into the billions of dollars each year. Although there are many exciting opportunities to educate consumers, ranging from visualization to virtual environments, text is still the most efficient and cost-effective medium available to all groups in society. Unfortunately, existing writing guidelines, relying heavily on readability formulas, have not been shown to impact text difficulty or consumer understanding. Objectives The objectives of this project are therefore to address and overcome existing barriers in current readability research. We address barriers as follows: we will work 1) with representative consumers, not experts evaluating on behalf of consumers, 2) using a large sample with thousands of participants working in their own settings, not a laboratory study with few participants in an artificial environment, and 3) evaluate both perceived and actual difficulty of text, two variables often confounded in studies, 4) to discover features that can be automatically discovered in text so that difficulty checkers can be developed that allow clinicians to efficiently and effectively optimize their text without requiring study of guidelines or linguistics. Design The study will be conducted using modern technology and resources. Over a time period of two years, master and doctoral level students will design and conduct the studies together with the principle investigator. The study brings together insights from computational linguistics and information science applied to medical informatics. Starting from a linguistic perspective, we will we systematically list good candidates of text features that may influence understanding. We will look at features of grammar, semantics, and compositions of text. By using a modern market place, Amazon's Mechanical Turk, we can involve thousands of participants in the study. We will each pair-wise comparisons of the features, e.g., sentences with high versus low topic density. Using a within-subjects design, we will measure perceived and actual difficulty of each feature with multiple choice question-answering tasks. PUBLIC HEALTH RELEVANCE: Current health information is not attuned to the reading skills of consumers a situation that contributes to low health literacy and higher healthcare costs because of costly mistakes and unwise decisions. The proposed project will work with thousands of representative consumers to discover text features that influence perceived and actual difficulty of text. These features will lead to better writing guidelines and automated tools to help clinicians write health educational materials that are easier to understand and that are based on demonstrated impacts of the features on understanding.