中文健康词汇用户熟悉度评估方法研究
|
摘要
【研究背景】
健康是人类永恒的追求。随着社会的文明进程的发展和人们生活水平的提高，大众对健康的重视程度亦在不断提高。人们对健康信息的需求在不断增长，需要丰富自身的健康知识，需要了解疾病诊疗知识，以期在参与医疗保健活动和进行医学决策中拥有积极的话语权。交流是人类的一种生活方式。维系健康，增进健康，既是社会个体的追求，更是作为整体的社会的一种发展目标。因此，交流健康知识、形成良好的健康知识氛围，有利于促进人类健康发展。语言作为交流思想和传递信息的工具，在健康信息交流中起着至关重要的媒介作用。而网络信息时代赋予了这种媒介更具时代性的新的寓意和作用这种媒介和作用。
然而，计算机、网络的应用，使人与人的交流增加了一个中间环节，人们在进行健康信息交流活动中经常会遇到诸多的问题和障碍，主要表现为普通大众（健康信息用户）的信息能力和健康知识与现有信息系统、信息资源间的“信息鸿沟”，信息获取、信息理解障碍导致用户信息需求得不到满足。用户健康信息学（CHI）就是从满足普通大众健康信息需求出发，以信息技术为支持，研究如何使健康信息的表达适应于用户理解力，以及如何使健康信息的传递方式适应用户的信息获取能力的一个新兴领域。作为健康信息交流的语言媒介，用户健康词表（CHV）则是一种被用于解决健康信息交流障碍的公认的、有价值的信息学工具，是CHI领域的研究热点之一。语言的基本单位是词汇，词汇熟悉度是指健康信息用户对健康词汇的熟悉程度，词汇熟悉度研究也就自然成为推动CHV智能化、个性化发展的重要环节，它涉及用户认知、用户环境、熟悉度评价与预测等多个方面的研究。
在我国，CHI研究尚未引起充分的重视。本研究是基于CHI学科背景和研究视角，结合我国的健康信息环境和汉语语言特征，在梳理国外CHI理论、分析实践成果的前提下，尝试构建中文健康词汇用户熟悉度评估方法为核心内容的健康信息用户研究和相关方法学探索。
【研究目的】
以探索健康信息用户对健康词汇的熟悉度评估方法为研究目的，开展用户对中文用户词与医学专业词的理解差异、词汇熟悉度评价工具的设计、用户对健康词汇熟悉度的影响因素和熟悉度预测方法等方面的研究，进而得到一种评估中文健康词汇熟悉度的方法，为中文用户健康词表构建提供理论依据和实践原则，从而克服用户健康信息交流过程中出现的语言障碍。
【研究方法】
本研究运用了文献调研法、调查法、Logistic回归分析方法。
【研究过程和研究结果】
首先，对健康词汇熟悉度的测评方法进行了研究。主要方法是从用户搜索提问中提取用户词形成语料库，以筛选出的用户词以及与之匹配的医学专业词所形成的用户词-医学专业词概念对作为评价对象，设计、编制评判健康词汇熟悉度测评工具，设计调查问卷。
其次，分析用户对医学专业词和用户词的熟悉度差异，用以验证健康信息用户对医学专业词和用户词的认知差异。
调查问卷采用现场发放和网上调查两种方式。研究对象为非医学教育背景的人员，即没有接受过正规的医学专业的教育与培训的普通大众。每名被调查者随机完成两版问卷中的任意一个。共收回532份问卷，其中有效问卷503份（有效率为94.55%）。熟悉度的计算方法是，对于每份问卷的每个词汇来说，如选择了正确的选项即该用户熟悉该词汇，得1分，否则0分。而对于总体调查样本来说，词汇熟悉度分数=该词汇所得总分数/回答含有该词汇问卷数。结果显示，A、B问卷中的所有词汇的熟悉度平均值分别为0.59，0.61，两者无显著性差异。说明问卷的用户词与专业词交叉设计合理。对用户词和专业词的熟悉度进行分析，整体用户词平均熟悉度为0.64，整体专业词平均熟悉度为0.57，配对秩和检验显示，两组平均熟悉度的差异有统计学意义（P=0.003），说明用户词的熟悉度高于专业词的熟悉度。从而验证了用户对医学专业词和用户词在认知上存在差异。
再次，在前期研究的基础上，为了实现熟悉度自动评估，从而筛选、推荐易于用户理解的健康词汇，本研究对健康信息用户词汇熟悉度的影响因素进行了筛选的分析，并运用Logistic回归分析方法进行了用户词熟悉度的多因素分析，并建立熟悉度预测模型。通过现场和网上发放问卷的方式对健康信息用户进行两次调查，共回收580份问卷，其中有效问卷545份（有效率93.97%），将大样本组（503份）用于影响因素的分析和预测模型的建立。小样本组（42份）用于验证预测模型的稳定性。对503份有效调查问卷进行单因素分析，分析其熟悉度影响因素，结果表明，用户对用户词的熟悉度受人口学因素（性别、年龄、文化程度和职业）、健康相关因素（健康状况、健康信息关注度、健康素养）和词汇因素（lg词频）三个方面共8个因素的影响。上述影响因素，除文化程度外，也均是医学专业词的熟悉度影响因素（不包括词频因素）。
另外，运用Logistic回归分析方法构建了用户词熟悉度预测模型，经小样本调查数据进行的信度检验，结果表明Logistic预测模型具有一定的信度，可以使用此方法预测健康词汇的熟悉度。
【研究结论】
⑴中文健康词汇用户熟悉度评估方法具有可行性
基于中文语言学特点的分析和对熟悉度概念的界定，制定并设计了熟悉度测评工具的编制原则和方法，尝试编制了适于中文语境的健康词汇熟悉度测评工具，从而实现了对特定用户词与专业词熟悉度测评的客观评价方法的尝试。
在编制熟悉度测评工具作为评价中文健康词汇用户熟悉度方法的基础上，验证了Logistic回归方法用于中文词汇熟悉度预测的可行性。
⑵中文健康信息用户对医学专业词和用户词存在认知差异
以熟悉度测评工具作为熟悉度评价方法，对中文健康信息用户进行了熟悉度评价，结果表明用户对熟悉度测评工具中的医学专业词和用户词的熟悉度存在差异，以此证明了中文健康信息用户在对健康相关语言的理解和认知上存在差异，且具有独特性。
⑶中文健康词汇熟悉度受多种因素影响
本研究对用户进行了用户词和医学专业词两类词汇熟悉度的测试，并筛定中文健康词汇熟悉度的影响因素。虽然在诸多的中文健康词汇用户熟悉度影响因素中，多数与国外研究结果一致，但本研究将健康信息用户对健康信息的关注度、健康素养纳入并确定为熟悉度影响因素，丰富了熟悉度影响因素。
【Background】
Health is the eternal persuit in humans. With the development of society and theimprovement of attention to health, the need of healthy information is increasingaccordingly. People need to enrich their knowledge of health, understand theknowledge of disease diagnosis and treatment, take part in medical activities, and makemedical decisions. However, they have met many problems and obstacles in the realhealth communication activities, including obstacles of information acquisition andunderstanding mainly because of "information gap" between health informationconsumers and the existing information system, and the consequent unsatisfiedinformation needs. Supported with information technology, Consumer HealthInformatic (CHI) is an emerging field to meet the demand of the general public healthinformation and study how to adapt the expression of health information to theconsumer's understanding, and how to adapt health information transfer mode to theconsumer's information acquisition capability. Consumer Health Vocabulary (CHV) isused as recognized informatics tools to solve health communication disorders, and isone of the hot research topic in the field of CHI. The study of vocabulary familiarity isan important step to promote the development of intelligence and personalization ofCHV, involving many other aspects of research such as consumer’s cognition,consumer environment, familiarity evaluation and prediction, etc. In our country, theresearch of CHI has not been attached importance extensively yet. This study is basedon the discipline and background of CHI, in combination with research status in Chinaand the Chinese language environment and foreign CHI theories. Under the premise ofanalyzing the practical results, the study focuses on the evaluation methodology ofhealth information consumer centered around Chinese healthy vocabulary familiarity.
【Objective】
Aiming for studying the estimation method of the familiarity for Chinese healthterms, the understanding difference between Chinese consumer words and medicalprofessional vocabulary and familiarity evaluation tool have been designed, and theinfluence factors of the familiarity for consumer health terms has been explored too.For overcoming the language barrier during the process of consumer healthcommunication, it would provide a way to estimate health vocabulary familiarity, andlay a research foundation for building the health vocabulary of Chinese consumer.
【Methods】
This study uses the literature research method, survey method, Logisticregressionanalysis method.
【Process&Results】
Firstly, evaluation method of the health terms familiarity is studied. Taking theconsumers' words from their searching questions as the corpus, and taking the conceptpair of consumers’words generated and its medical terms matched (consumers’words-medical concept pairs) as evaluating objects, the evaluation tool and questionnaire ofhealth terms familiarity are designed and edited.
Secondly, the study analyzes familiarity difference between the medical termsand consumers’words to verify the cognitive differences existing between them.
There are two forms of questionnaires: field distribution and online surveys. Theinvestigation respindent are people who have no formal medical professional educationand training. Every informant has completed any one of the two versions randomly.532questionnaires have been recycled, and503is valid (effective rate:94.55%).Thecomputing method of familiarity is that for each term of each questionnaire, if thechoice is the correct option then familiarity of the term is1, if not,0. For the overallsamples, familiarity of the term=total familiarity score of the term/number ofquestionnaires containing the term. The results showed that the average familiarity forall the terms in A and B questionnaire is0.59,0.61, with no difference between them.It indicated the questionnaire design in overlaps of consumer words and medicalterms is reasonable. When analyzing the familiarity for consumer words and medicalterms, the overall familiarity average for consumer words is0.64, and that for medicalterms is0.57. By means of Wilcoxcon signed-rank test, it is statistically significant(Z=2.937, P=0.003), the results indicated that the consumer words familiarity is higher than medical terms familiarity, and suggested that there is cognitive differences andfamiliarity differences between medical terms and consumers’ words for consumers(P=0.003).
Thirdly, on the basis of previous studies, in order to achieve the automatedestimation of familiarity, and screen and recommend Health vocabulary which is easyfor consumer to understand, factors influencing the familiarity for Chinese healthyvocabulary were analyzed, and a logistic regression was employed and the predictionmodel was established. Investigation for health consumers has been conductedthrough field questionnaire and online questionnaire, with recycled580questionnaires(545valid, effective rate:93.97%). Then the questionnaires weredivided into two groups, one group(503) was for factor analysis and prediction model,and the small sample groups (42) was mainly used for model validation.503validquestionnaires has been analyzed for familiarity influence factors, and the resultsshowed that word familiarity of users was influenced by three aspects including eightfactors, i.e. demographic factors (gender, age, cultural level and occupation), healthrelated factors (health, health care, health literacy) and vocabulary factors (lgfrequency). All the above influence factors except the cultural level were the influencefactors of medical professional word familiarity(not including word frequency). Bythe small sample reliability test, the reliability of the Logistic model was verified,showing that the logistic method used to predict familiarity of the method is feasible.
【Conclusions】
⑴Consumers' familiarity estimation method of Chinese health terms achievepreliminary success
Based on the analysis of the characteristics of the Chinese linguistics and thedefinition of familiarity, the study developed and designed the compiling principlesand methods of the familiarity estimation tools, and compiled the familiarityevaluation tools of Chinese health words, so as to explore the consumer researchmethodology of particular consumers and professional words familiarity evaluation.
After compiling the familiarity evaluation tools and determining the familiarityinfluence factors, Logistic regression method was utilized to build the logisticregression model of familiarity and influencing factors, so as to verify the feasibilityof the method of predict Chinese vocabulary familiarity.
⑵Screening the influence factors of Chinese health terms familiarity
The study has tested the familiarity for professional words and consumer words,screened and analyzed the influence factors of consumer familiarity for Chinesehealth terms. Although in the factors, the most are consistent with the foreign researchresults, bringing the health information attention, health literacy into familiarityinfluence factors and identifying them enriched the influence factors of familiarity.
⑶Confirming the cognitive difference between professional terms andconsumers' words
Using familiarity estimation tools, the author evaluated the familiarity of Chinesehealth information consumers for the professional terms and consumers' words. Theresults showed that there was cognitive difference between professional terms andconsumers' words, confirming the difference between the understanding cognition ofhealth language among Chinese health information consumers.
健康是人类永恒的追求。随着社会的文明进程的发展和人们生活水平的提高，大众对健康的重视程度亦在不断提高。人们对健康信息的需求在不断增长，需要丰富自身的健康知识，需要了解疾病诊疗知识，以期在参与医疗保健活动和进行医学决策中拥有积极的话语权。交流是人类的一种生活方式。维系健康，增进健康，既是社会个体的追求，更是作为整体的社会的一种发展目标。因此，交流健康知识、形成良好的健康知识氛围，有利于促进人类健康发展。语言作为交流思想和传递信息的工具，在健康信息交流中起着至关重要的媒介作用。而网络信息时代赋予了这种媒介更具时代性的新的寓意和作用这种媒介和作用。
然而，计算机、网络的应用，使人与人的交流增加了一个中间环节，人们在进行健康信息交流活动中经常会遇到诸多的问题和障碍，主要表现为普通大众（健康信息用户）的信息能力和健康知识与现有信息系统、信息资源间的“信息鸿沟”，信息获取、信息理解障碍导致用户信息需求得不到满足。用户健康信息学（CHI）就是从满足普通大众健康信息需求出发，以信息技术为支持，研究如何使健康信息的表达适应于用户理解力，以及如何使健康信息的传递方式适应用户的信息获取能力的一个新兴领域。作为健康信息交流的语言媒介，用户健康词表（CHV）则是一种被用于解决健康信息交流障碍的公认的、有价值的信息学工具，是CHI领域的研究热点之一。语言的基本单位是词汇，词汇熟悉度是指健康信息用户对健康词汇的熟悉程度，词汇熟悉度研究也就自然成为推动CHV智能化、个性化发展的重要环节，它涉及用户认知、用户环境、熟悉度评价与预测等多个方面的研究。
在我国，CHI研究尚未引起充分的重视。本研究是基于CHI学科背景和研究视角，结合我国的健康信息环境和汉语语言特征，在梳理国外CHI理论、分析实践成果的前提下，尝试构建中文健康词汇用户熟悉度评估方法为核心内容的健康信息用户研究和相关方法学探索。
【研究目的】
以探索健康信息用户对健康词汇的熟悉度评估方法为研究目的，开展用户对中文用户词与医学专业词的理解差异、词汇熟悉度评价工具的设计、用户对健康词汇熟悉度的影响因素和熟悉度预测方法等方面的研究，进而得到一种评估中文健康词汇熟悉度的方法，为中文用户健康词表构建提供理论依据和实践原则，从而克服用户健康信息交流过程中出现的语言障碍。
【研究方法】
本研究运用了文献调研法、调查法、Logistic回归分析方法。
【研究过程和研究结果】
首先，对健康词汇熟悉度的测评方法进行了研究。主要方法是从用户搜索提问中提取用户词形成语料库，以筛选出的用户词以及与之匹配的医学专业词所形成的用户词-医学专业词概念对作为评价对象，设计、编制评判健康词汇熟悉度测评工具，设计调查问卷。
其次，分析用户对医学专业词和用户词的熟悉度差异，用以验证健康信息用户对医学专业词和用户词的认知差异。
调查问卷采用现场发放和网上调查两种方式。研究对象为非医学教育背景的人员，即没有接受过正规的医学专业的教育与培训的普通大众。每名被调查者随机完成两版问卷中的任意一个。共收回532份问卷，其中有效问卷503份（有效率为94.55%）。熟悉度的计算方法是，对于每份问卷的每个词汇来说，如选择了正确的选项即该用户熟悉该词汇，得1分，否则0分。而对于总体调查样本来说，词汇熟悉度分数=该词汇所得总分数/回答含有该词汇问卷数。结果显示，A、B问卷中的所有词汇的熟悉度平均值分别为0.59，0.61，两者无显著性差异。说明问卷的用户词与专业词交叉设计合理。对用户词和专业词的熟悉度进行分析，整体用户词平均熟悉度为0.64，整体专业词平均熟悉度为0.57，配对秩和检验显示，两组平均熟悉度的差异有统计学意义（P=0.003），说明用户词的熟悉度高于专业词的熟悉度。从而验证了用户对医学专业词和用户词在认知上存在差异。
再次，在前期研究的基础上，为了实现熟悉度自动评估，从而筛选、推荐易于用户理解的健康词汇，本研究对健康信息用户词汇熟悉度的影响因素进行了筛选的分析，并运用Logistic回归分析方法进行了用户词熟悉度的多因素分析，并建立熟悉度预测模型。通过现场和网上发放问卷的方式对健康信息用户进行两次调查，共回收580份问卷，其中有效问卷545份（有效率93.97%），将大样本组（503份）用于影响因素的分析和预测模型的建立。小样本组（42份）用于验证预测模型的稳定性。对503份有效调查问卷进行单因素分析，分析其熟悉度影响因素，结果表明，用户对用户词的熟悉度受人口学因素（性别、年龄、文化程度和职业）、健康相关因素（健康状况、健康信息关注度、健康素养）和词汇因素（lg词频）三个方面共8个因素的影响。上述影响因素，除文化程度外，也均是医学专业词的熟悉度影响因素（不包括词频因素）。
另外，运用Logistic回归分析方法构建了用户词熟悉度预测模型，经小样本调查数据进行的信度检验，结果表明Logistic预测模型具有一定的信度，可以使用此方法预测健康词汇的熟悉度。
【研究结论】
⑴中文健康词汇用户熟悉度评估方法具有可行性
基于中文语言学特点的分析和对熟悉度概念的界定，制定并设计了熟悉度测评工具的编制原则和方法，尝试编制了适于中文语境的健康词汇熟悉度测评工具，从而实现了对特定用户词与专业词熟悉度测评的客观评价方法的尝试。
在编制熟悉度测评工具作为评价中文健康词汇用户熟悉度方法的基础上，验证了Logistic回归方法用于中文词汇熟悉度预测的可行性。
⑵中文健康信息用户对医学专业词和用户词存在认知差异
以熟悉度测评工具作为熟悉度评价方法，对中文健康信息用户进行了熟悉度评价，结果表明用户对熟悉度测评工具中的医学专业词和用户词的熟悉度存在差异，以此证明了中文健康信息用户在对健康相关语言的理解和认知上存在差异，且具有独特性。
⑶中文健康词汇熟悉度受多种因素影响
本研究对用户进行了用户词和医学专业词两类词汇熟悉度的测试，并筛定中文健康词汇熟悉度的影响因素。虽然在诸多的中文健康词汇用户熟悉度影响因素中，多数与国外研究结果一致，但本研究将健康信息用户对健康信息的关注度、健康素养纳入并确定为熟悉度影响因素，丰富了熟悉度影响因素。
【Background】
Health is the eternal persuit in humans. With the development of society and theimprovement of attention to health, the need of healthy information is increasingaccordingly. People need to enrich their knowledge of health, understand theknowledge of disease diagnosis and treatment, take part in medical activities, and makemedical decisions. However, they have met many problems and obstacles in the realhealth communication activities, including obstacles of information acquisition andunderstanding mainly because of "information gap" between health informationconsumers and the existing information system, and the consequent unsatisfiedinformation needs. Supported with information technology, Consumer HealthInformatic (CHI) is an emerging field to meet the demand of the general public healthinformation and study how to adapt the expression of health information to theconsumer's understanding, and how to adapt health information transfer mode to theconsumer's information acquisition capability. Consumer Health Vocabulary (CHV) isused as recognized informatics tools to solve health communication disorders, and isone of the hot research topic in the field of CHI. The study of vocabulary familiarity isan important step to promote the development of intelligence and personalization ofCHV, involving many other aspects of research such as consumer’s cognition,consumer environment, familiarity evaluation and prediction, etc. In our country, theresearch of CHI has not been attached importance extensively yet. This study is basedon the discipline and background of CHI, in combination with research status in Chinaand the Chinese language environment and foreign CHI theories. Under the premise ofanalyzing the practical results, the study focuses on the evaluation methodology ofhealth information consumer centered around Chinese healthy vocabulary familiarity.
【Objective】
Aiming for studying the estimation method of the familiarity for Chinese healthterms, the understanding difference between Chinese consumer words and medicalprofessional vocabulary and familiarity evaluation tool have been designed, and theinfluence factors of the familiarity for consumer health terms has been explored too.For overcoming the language barrier during the process of consumer healthcommunication, it would provide a way to estimate health vocabulary familiarity, andlay a research foundation for building the health vocabulary of Chinese consumer.
【Methods】
This study uses the literature research method, survey method, Logisticregressionanalysis method.
【Process&Results】
Firstly, evaluation method of the health terms familiarity is studied. Taking theconsumers' words from their searching questions as the corpus, and taking the conceptpair of consumers’words generated and its medical terms matched (consumers’words-medical concept pairs) as evaluating objects, the evaluation tool and questionnaire ofhealth terms familiarity are designed and edited.
Secondly, the study analyzes familiarity difference between the medical termsand consumers’words to verify the cognitive differences existing between them.
There are two forms of questionnaires: field distribution and online surveys. Theinvestigation respindent are people who have no formal medical professional educationand training. Every informant has completed any one of the two versions randomly.532questionnaires have been recycled, and503is valid (effective rate:94.55%).Thecomputing method of familiarity is that for each term of each questionnaire, if thechoice is the correct option then familiarity of the term is1, if not,0. For the overallsamples, familiarity of the term=total familiarity score of the term/number ofquestionnaires containing the term. The results showed that the average familiarity forall the terms in A and B questionnaire is0.59,0.61, with no difference between them.It indicated the questionnaire design in overlaps of consumer words and medicalterms is reasonable. When analyzing the familiarity for consumer words and medicalterms, the overall familiarity average for consumer words is0.64, and that for medicalterms is0.57. By means of Wilcoxcon signed-rank test, it is statistically significant(Z=2.937, P=0.003), the results indicated that the consumer words familiarity is higher than medical terms familiarity, and suggested that there is cognitive differences andfamiliarity differences between medical terms and consumers’ words for consumers(P=0.003).
Thirdly, on the basis of previous studies, in order to achieve the automatedestimation of familiarity, and screen and recommend Health vocabulary which is easyfor consumer to understand, factors influencing the familiarity for Chinese healthyvocabulary were analyzed, and a logistic regression was employed and the predictionmodel was established. Investigation for health consumers has been conductedthrough field questionnaire and online questionnaire, with recycled580questionnaires(545valid, effective rate:93.97%). Then the questionnaires weredivided into two groups, one group(503) was for factor analysis and prediction model,and the small sample groups (42) was mainly used for model validation.503validquestionnaires has been analyzed for familiarity influence factors, and the resultsshowed that word familiarity of users was influenced by three aspects including eightfactors, i.e. demographic factors (gender, age, cultural level and occupation), healthrelated factors (health, health care, health literacy) and vocabulary factors (lgfrequency). All the above influence factors except the cultural level were the influencefactors of medical professional word familiarity(not including word frequency). Bythe small sample reliability test, the reliability of the Logistic model was verified,showing that the logistic method used to predict familiarity of the method is feasible.
【Conclusions】
⑴Consumers' familiarity estimation method of Chinese health terms achievepreliminary success
Based on the analysis of the characteristics of the Chinese linguistics and thedefinition of familiarity, the study developed and designed the compiling principlesand methods of the familiarity estimation tools, and compiled the familiarityevaluation tools of Chinese health words, so as to explore the consumer researchmethodology of particular consumers and professional words familiarity evaluation.
After compiling the familiarity evaluation tools and determining the familiarityinfluence factors, Logistic regression method was utilized to build the logisticregression model of familiarity and influencing factors, so as to verify the feasibilityof the method of predict Chinese vocabulary familiarity.
⑵Screening the influence factors of Chinese health terms familiarity
The study has tested the familiarity for professional words and consumer words,screened and analyzed the influence factors of consumer familiarity for Chinesehealth terms. Although in the factors, the most are consistent with the foreign researchresults, bringing the health information attention, health literacy into familiarityinfluence factors and identifying them enriched the influence factors of familiarity.
⑶Confirming the cognitive difference between professional terms andconsumers' words
Using familiarity estimation tools, the author evaluated the familiarity of Chinesehealth information consumers for the professional terms and consumers' words. Theresults showed that there was cognitive difference between professional terms andconsumers' words, confirming the difference between the understanding cognition ofhealth language among Chinese health information consumers.
引文
[1] Pew Research Internet Project[EB/OL].http://www.pewinternet.org/fact-sheets/health-fact-sheet/.[2008-9-1].
[2] Kuehn BM. More than one-third of US individuals use the Internet toself-diagnose[J].JAMA,2013,309(8):756-757.
[3] Bottles K. Patients and doctors: some thoughts on an evolving relationship amidunprecedented change. Manag Care Q,2000,8(3):34-36.
[4]李玉玲,曹锦丹.信息焦虑的概念界定[J].图书馆学研究,2011,(2):2-4.
[5] Zeng Q, Kogan S, Ash N, et al. Patient and clinician vocabulary: how differentare they[J]? Medinfo,2001,10(Pt1):399-403.
[6]李玉玲,李亚峰,刘妍,杨光.吉林省大学生信息素养现状分析和对策[J].中华医学图书情报杂志,2012,21(11):23-27.
[7] Bader JL, Theofanos MF. Searching for cancer information on the internet:analyzing natural language search queries[J].J Med Internet Res,2003,5(4):e31.
[8] Zeng QT, Kogan S, Plovnick R,et al. Positive attitude and failed queries: anexploration of the conundrums of consumer health information retrieval[J]. Int JMed Inf,2004,73(1):45-55.
[9]田甜.文化与健康素养[N].大众科技报,2000-6-27(3).
[10]郭欣,王克安.健康素养研究进展[J].中国健康教育,2005,21(8):590-593.
[11]卫生部妇幼保健与社区卫生司.中国健康教育中心/卫生部新闻宣传中心.首次中国居民健康素养调查报告[EB/OL].[2009-12]. http://wenku.baidu.com/view/651453a5f524ccbff121840a.html.
[12]刘谦.在2010年全国社区卫生、妇幼保健和健康教育工作会议上的讲话[J/OL].《卫生政务通报》第8期.[2010.4.7] http://www.moh.gov.cn/liuq/pldjh/201004/46522.shtml2010.4.7.
[13] McCray A. Promoting health literacy[J]. JAMA,2005,12(2):152-163.
[14] Rudd R, Moeykens B, Colton T. Annual review of adult learning and literacy,chapter5[M]. Health and literacy: a review of medical and public healthliterature. Jossey Bass.1999.
[15] Gemoets D, Rosemblat G,Tse T, et al. Assessing Readability of ConsumerHealth Information: An Exploratory Study[J]. Medinfo,2004:868-873.
[16] Lindberg D. Making Health Information Available to the Public: The FederalOpportunity[M]. Proceedings of the second national conference on scientific andtechnical data: data for science and society. U.S. National Committee forCODATA, Office of International Affairs, National Research Council; NationalAcademy Press: Washington DC.2001.
[17]杜甲珺.中文用户健康词表构建方法的实证研究[D].长春：吉林大学，2009.
[18] Marshall PD.Bridging the terminlolgy gap between health care professionals andpatients with the Consumer Health Terminology(CHT)[J].Proc AMIA Symp,2000:1082.
[19] Lewis D, Brennan PF, McCray AT, et al. If we build it, they will come:standardized consumer vocabularies[J]. Medinfo,2001:1530.
[20] Patrick TB, Monga HK, Sievert MC, et al. Evaluation of controlled vocabularyresources for development of a consumer entry vocabulary for diabetes[J]. JMed Internet Res,2001,3(3):E24.
[21] Ferguson T. Consumer Health Informatics[J].Healthc Forum J,1995,(38):28-33.
[22] Eysenbach G. Consumer health informatics[J]. BMJ,2000,320(7251):1713-1716.
[23] Bass DM, McClendon MJ, Brennan PF, et al.The buffering effect of a computersupport network on caregiver strain[J]. J Aging Health,1998,10(1):20-43.
[24] Keselman A, Logan R, Smith CA,et al. Developing informatics tools andstrategies for consumer-centered health communication[J].J Am Med InformAssoc,2008,15(4):473-483.
[25] Consumer Health Informatics. AMIA. http://www.amia.org/applications-informatics/consumer-health-informatics.[2009-12-10]
[26]兰小筠,梁昌标.用户健康信息学的起源与概念[J].当代医学,2009,15(16):21-23.
[27] Logan RA, Tse T. A multidiscipline conceptual framework for consumer healthinformatics[J]. Stud Health Technol Inform,2007,129(Pt2):1169-1173.
[28] Spiro D, Heidrich F. Lay understanding of medical terminology[J]. J FamPract,1983,17(2):277-279.
[29] Lerner EB, Jehle DV, Janicke DM, et al. Medical communication: do ourpatients understand?[J]. Am J Emerg Med,2000,18(7):764-766.
[30] Kogan S, Zeng Q, Ash N, et al.Problems and challenges in patient informationretrieval: a descriptive study[J]. Proc AMIA Symp,2001:329-333.
[31] Scott N, Weiner MF.―Patientspeak‖: An Exercise in Communication[J]. J MedEduc,1984,59(11):890-893.
[32] Spees CM. Knowledge of medical terminology among clients and families[J].Image J Nurs Sch,1991,23(4):225-229.
[33] Hadlow J, Pitts M. The understanding of common health terms by doctors,nurses and patients[J]. Soc Sci Med,1991,32(2):193-196.
[34] Zeng Q,Kogan S, Ash N,et al. Patient and clinician vocabulary: How Differentare they?[J] MedInfo,2001,10(Pt1):399-403.
[35] Young CG, Barger JD. Learning medical terminology step by step[M]. SaintLouis: C.V. Mosby.1975.
[36] Catherine A, Smith MA. Consumer language, patient language, and thesauri: areview of the literature[J]. J Med Libr Assoc,2011,99(2):135-144.
[37] Keselman A, Logan R, Smith CA,et al. Developing informatics tools andstrategies for consumer-centered health communication[J].J Am Med InformAssoc,2008,15(4):473-483.
[38] Scott N, Weiner MF.―Patientspeak‖: An Exercise in Communication[J]. J MedEduc,1984;59(11):890-893.
[39] Patrick TB, Monga HK, Sievert ME, et al. Evaluation of controlled vocabularyresources for development of a consumer entry vocabulary for diabetes[J].J MedInternet Res,2001,3(3):E24.
[40] McCray AT, Loane RF, Browne AC, et al. Terminology issues in user access toWeb-based medical information[J].Proc AMIA Symp,1999:107–111.
[41] United States National Library of Medicine, National Institutes of Health: LongRange Plan2000-2005. GOALS AND OBJECTIVES. http://www.nlm.nih.gov/archive/20120607/pubs/plan/nlm_lrp_2000.pdf
[42] Shiri AA, Revie C. Thesauri on the Web: current developments and trends[J].Online Inf Rev,2000,24(4):273-279.
[43] Lewis D, Brennan PF, McCray AT, et al. If we build it, they will come:standardized consumer vocabularies[J].Medinfo,2001:1530.
[44] Patrick TB, Monga HK, Sievert ME, et al. Evaluation of controlled vocabularyresources for development of a consumer entry vocabulary for diabetes[J].J MedInternet Res,2001,3(3):E24.
[45] Zeng QT, Tse T, Crowell J,et al. Identifying Consumer-Friendly Display (CFD)Names for Health Concepts[J]. Proc AMIA Symp,2005,(1):859-863.
[46] Zeng QT, Tse T. Exploring and developing consumer health vocabularies[J]. JAm Med Inf Assoc,2006,13(1):24-29.
[47] Zeng QT, Tse T, Divita G, et al. Term Identification Methods for ConsumerHealth Vocabulary Development[J]. J Med Internet Res,2007,9(1):29-33.
[48]杜甲珺.中文用户健康词表构建方法的实证研究[D].长春：吉林大学，2009.
[49]曹锦丹，杜甲珺.中文用户健康词表构建及其实证研究[J].情报学报,2010,(5):841-846.
[50]罗伯特.索尔索著.黄希庭等译.认知心理学[M].教育科学出版社,1990年.第1版.
[51]褚凌云.主题熟悉度对词汇附带习得影响的实验分析[J].边疆经济与文化,2012,(7):140-141.
[52]张萌，张积家.熟悉性和语境对中学生成语理解的影响[J].中国科教创新导刊,2008,(30):192-193.
[53]郑剑虹，严丽芬.自我人格形容词好恶度、熟悉度和意义度的初步测定[J].宁波大学学报（教育科学版）,2010,32(1):60-63.
[54]罗家平.熟悉度和预测性对汉语成语理解影响的实验研究[D].长沙.湖南师范大学.2010.
[55] Libben MR,Titone DA.The multidetermined nature of idiom processing[J].Mem Cognit,2008,36(6):1103-1112.
[56] B1asko DG, Connine CM. Effects of familiarity and aptness on the processingof metaphor[J]. J Exp Psychol Learn Mem Cogn,1993,19(2):295-308.
[57] Zeng Q, Tse T, Crowell J, et al. Identifying consumer friendly display (CFD)names for health concepts[J]. AMIA Annu Symp Proc2005,2005:859-863.
[58] Zeng Q, Kim E, Crowell J,et al. A Text Corpora-Based Estimation of theFamiliarity of Health Terminology[C]. In: Oliveira JL, Maojo V, Martin-SanchezF, Pereira AS, editors. Biological and Medical Data Analysis:6th InternationalSymposium (ISBMDA2005) Proceedings;2005Nov10-11; Aveiro, Portugal.Berlin: Springer;2005. p.184-92.
[59] Keselman1A, Tse T, Crowell J, etal. Assessing Consumer Health VocabularyFamiliarity: An Exploratory Study[J]. J Med Internet Res,2007,9(1): e5.
[60] Ingwersen P. Toward a new research paradigm in information retrieval. In IreneWormell(eds.). Knowledge engineering: expert systems and informationretrieval[M]. London: Taylor Graham Publishing,1987:154-155.
[61] Dervin B. Communication gaps and inequities: Moving toward areconceptualization[J]. Prog Commun,1980,(2):73-112.
[62]柯青,王秀峰,孙建军.以用户为中心的研究范式——理论起源[J].情报资料工作,2008,(4):51-55.
[63] Kathleen D, Dervin B. Examplars of use of sence-making methodolog.Electronic journal of communication[EB/OL].1999,9(2-4) http://www.cios.org/www/ejc/v9n23499.htm.
[64] Belkin NJ, Oddy RN, Brooks HM. Ask for information retrieval: Part I,background and theory[J]. J Doc,1982,38(2):61-71.
[65] Belkin NJ, Oddy RN, Brooks HM. Ask for information retrieval: Part I, resultsof a design study[J]. J Doc,1982,38(3):145-164.
[66]颜端武，王日芬.信息获取与用户服务[M].北京：科学出版社,2010.1.P59.
[67]荣毅虹，田也壮.论信息的可理解[J].情报学报,2006,(4):393-398.
[68] Wurman RS. Information anxiety[M].New York:Doubleday.1989：1
[69] Belkin NJ. Information concepts for information science[J]. J Doc,1978,34(1):55-85.
[70] Belkin NJ, Orrd RN, Brooks HM. ASK for information retrieval[J]. J Doc,1982,38(2):61-71.
[71]胡昌平.信息服务与用户[M].武汉:武汉大学出版社,2008.
[72] Bodenreider O. The Unified Medical Language System(UMLS):IntegratingBiomedical Terminology[J]. Nucleic Acids Res,2004,32(databaseissue):D267-270.
[73] Rosse C, Mejino JVL. A reference ontology for biomedical informatics: theFoundational Model of Anatomy[J]. J Biomed Inform,2003,36(6):478-500.
[74] Burgun A, Bodenreider O. Comparing terms, concepts and semantic classes inWordNet and unified medical language system[C]. In proceedings of theWorkshop on WordNet and other Lexical Resources: Applications, Extensionsand Customizations. Pittsburgh, PA.2001.P77-82.
[75] Keselman A, Smith CA, Divita G, et al. Consumer health concepts that do notmap to the UMLS: where do they fit?[J]. J AM Med InformAssoc,2008,15(4):496-505.
[76] Smith B, Fellbaum C. Medical WordNet: A New Methodology for theConstruction and Validation of Information Resources for Consumer Health[C].Proceedings of Coling: The20th International Conference on ComputationalLinguistics, Geneva23-27. August2004, P31-38. http://ontology.buffalo.edu/medo/MedicalWordNet_Coling.pdf.
[77] Cardillo E. A lexi-ontological resource for consumer healthcare: The Italianconsumer medical vocabulary[D]. University of Trento, Italy,2011.
[78] Elhadad N, Sutaria K. Mining a lexicon of technical terms and layequivalents[C]. BioNLP '07Proceedings of the Workshop on BioNLP2007:Biological, Translational, and Clinical Language Processing.2007,P49-56.http://dl.acm.org/citation.cfm?id=1572402.
[79] Teufel，Elhadad N. Collection and Linguistic Processing of a Large-scaleCorpus of Medical Articles[C]. In Proc.LREC’02,2002. P1214-1218.
[80] Muresan S，Klavans JL.Inducing Terminologies From Text: A Case Study for theConsumer Health Domain[J]. J Am Soc Inf Sci Technol,2013,64(4):727-744.
[81] Doing-Hrris KM, Zeng-Treitler Q.Computer-assisted update of a consumerhealth vocabulary through mining of social network data[J]. J Med Internet Res,2011,13(2):e37.
[82] Keselman A, Tse T, Crowell J,et al. Assessing Consumer Health VocabularyFamiliarity: An Exploratory Study[J]. J Med Internet Res,2007,9(1): e5.
[83] Zeng Q, Kim E, Crowell J, et al. A Text Corpora-Based Estimation of theFamiliarity of Health Terminology[C]. Biological and Medical Data Analysis:6th International Symposium (ISBMDA2005) Proceedings;2005Nov10-11;Aveiro, Portugal. Berlin: Springer;2005.184-192.
[84] Keselman1A, Tse T, Crowell J, et al. Assessing Consumer Health VocabularyFamiliarity: An Exploratory Study[J]. J Med Internet Res,2007,9(1)：e5.
[85] Sun XY, Shi YH,Zeng Q. Determinants of health literacy and health behaviorregarding infectious respiratory diseases: a pathway model[J].BMC PublicHealth,2013,13:261.
[86] Berkman ND, Davis TC, McCormack L. Health Literacy: What Is It?[J]. JHealth Commun,2010,13(Suppl2):9-19.
[87] Columbia University School of Nursing.Health Literacy Assessment Tool[EB/OL].[2012-8-8]. http://cumc.columbia.edu/dept/nursing/ebp/HealthLitRes/assessTool.html..
[88] Parker RM, Baker DW,Williams MV et al. The test of functional health literacyin adults: a new instrument for measuring patients’literacy skills[J]. J Gen InternMed,1995,10(10):537-541.
[89]中国公民健康素养：基本知识与技能（试行）[EB/OL].(2008-01-04)[2013-11-04]. http://www.gov.cn/gzdt/2008-02/05/content_884068.htm.
[90] Fishel CC, Graham KE, Greer DM, et al. CINAHL list of subject headings: anursing thesaurus revised[J]. Bull Med Libr Assoc,1985,73(2):153-159.
[91] Marshall J. Controlled vocabularies: a primer[J]. KeyWords,2005,13(4):120-124.
[92] Zeng Q, Kim E, Crowell J，et al. A Text Corpora-Based Estimation of theFamiliarity of Health Terminology[C]. In: Oliveira JL, Maojo V,Martin-Sanchez F, Pereira AS, editors. Biological and Medical Data Analysis:6th International Symposium (ISBMDA2005) Proceedings;2005Nov10-11;Aveiro, Portugal. Berlin: Springer;2005. p184-192.
[93] Lee SD, Bender DE, Ruiz RE, et al. Development of an easy-to-use SpanishHealth Literacy test[J]. Health Serv Res,2006,41(4Pt1):1392-1412.
[94] Furnas GW, Landauer TK, Gomez LM, et al. The vocabulary problem inhuman-system communication[J].Commun of the ACM,1987,30(11):964-971.
[95] Keselman1A，Tse T，Crowell J，et al. Assessing Consumer Health VocabularyFamiliarity：An Exploratory Study[J]. J Med Internet Res,2007，9(1):e5.
[96] Zeng Q, Tse T, Crowell J, et al. Identifying consumer friendly display (CFD)names for health concepts[J]. AMIA Annu Symp Proc2005,2005:859-863.
[2] Kuehn BM. More than one-third of US individuals use the Internet toself-diagnose[J].JAMA,2013,309(8):756-757.
[3] Bottles K. Patients and doctors: some thoughts on an evolving relationship amidunprecedented change. Manag Care Q,2000,8(3):34-36.
[4]李玉玲,曹锦丹.信息焦虑的概念界定[J].图书馆学研究,2011,(2):2-4.
[5] Zeng Q, Kogan S, Ash N, et al. Patient and clinician vocabulary: how differentare they[J]? Medinfo,2001,10(Pt1):399-403.
[6]李玉玲,李亚峰,刘妍,杨光.吉林省大学生信息素养现状分析和对策[J].中华医学图书情报杂志,2012,21(11):23-27.
[7] Bader JL, Theofanos MF. Searching for cancer information on the internet:analyzing natural language search queries[J].J Med Internet Res,2003,5(4):e31.
[8] Zeng QT, Kogan S, Plovnick R,et al. Positive attitude and failed queries: anexploration of the conundrums of consumer health information retrieval[J]. Int JMed Inf,2004,73(1):45-55.
[9]田甜.文化与健康素养[N].大众科技报,2000-6-27(3).
[10]郭欣,王克安.健康素养研究进展[J].中国健康教育,2005,21(8):590-593.
[11]卫生部妇幼保健与社区卫生司.中国健康教育中心/卫生部新闻宣传中心.首次中国居民健康素养调查报告[EB/OL].[2009-12]. http://wenku.baidu.com/view/651453a5f524ccbff121840a.html.
[12]刘谦.在2010年全国社区卫生、妇幼保健和健康教育工作会议上的讲话[J/OL].《卫生政务通报》第8期.[2010.4.7] http://www.moh.gov.cn/liuq/pldjh/201004/46522.shtml2010.4.7.
[13] McCray A. Promoting health literacy[J]. JAMA,2005,12(2):152-163.
[14] Rudd R, Moeykens B, Colton T. Annual review of adult learning and literacy,chapter5[M]. Health and literacy: a review of medical and public healthliterature. Jossey Bass.1999.
[15] Gemoets D, Rosemblat G,Tse T, et al. Assessing Readability of ConsumerHealth Information: An Exploratory Study[J]. Medinfo,2004:868-873.
[16] Lindberg D. Making Health Information Available to the Public: The FederalOpportunity[M]. Proceedings of the second national conference on scientific andtechnical data: data for science and society. U.S. National Committee forCODATA, Office of International Affairs, National Research Council; NationalAcademy Press: Washington DC.2001.
[17]杜甲珺.中文用户健康词表构建方法的实证研究[D].长春：吉林大学，2009.
[18] Marshall PD.Bridging the terminlolgy gap between health care professionals andpatients with the Consumer Health Terminology(CHT)[J].Proc AMIA Symp,2000:1082.
[19] Lewis D, Brennan PF, McCray AT, et al. If we build it, they will come:standardized consumer vocabularies[J]. Medinfo,2001:1530.
[20] Patrick TB, Monga HK, Sievert MC, et al. Evaluation of controlled vocabularyresources for development of a consumer entry vocabulary for diabetes[J]. JMed Internet Res,2001,3(3):E24.
[21] Ferguson T. Consumer Health Informatics[J].Healthc Forum J,1995,(38):28-33.
[22] Eysenbach G. Consumer health informatics[J]. BMJ,2000,320(7251):1713-1716.
[23] Bass DM, McClendon MJ, Brennan PF, et al.The buffering effect of a computersupport network on caregiver strain[J]. J Aging Health,1998,10(1):20-43.
[24] Keselman A, Logan R, Smith CA,et al. Developing informatics tools andstrategies for consumer-centered health communication[J].J Am Med InformAssoc,2008,15(4):473-483.
[25] Consumer Health Informatics. AMIA. http://www.amia.org/applications-informatics/consumer-health-informatics.[2009-12-10]
[26]兰小筠,梁昌标.用户健康信息学的起源与概念[J].当代医学,2009,15(16):21-23.
[27] Logan RA, Tse T. A multidiscipline conceptual framework for consumer healthinformatics[J]. Stud Health Technol Inform,2007,129(Pt2):1169-1173.
[28] Spiro D, Heidrich F. Lay understanding of medical terminology[J]. J FamPract,1983,17(2):277-279.
[29] Lerner EB, Jehle DV, Janicke DM, et al. Medical communication: do ourpatients understand?[J]. Am J Emerg Med,2000,18(7):764-766.
[30] Kogan S, Zeng Q, Ash N, et al.Problems and challenges in patient informationretrieval: a descriptive study[J]. Proc AMIA Symp,2001:329-333.
[31] Scott N, Weiner MF.―Patientspeak‖: An Exercise in Communication[J]. J MedEduc,1984,59(11):890-893.
[32] Spees CM. Knowledge of medical terminology among clients and families[J].Image J Nurs Sch,1991,23(4):225-229.
[33] Hadlow J, Pitts M. The understanding of common health terms by doctors,nurses and patients[J]. Soc Sci Med,1991,32(2):193-196.
[34] Zeng Q,Kogan S, Ash N,et al. Patient and clinician vocabulary: How Differentare they?[J] MedInfo,2001,10(Pt1):399-403.
[35] Young CG, Barger JD. Learning medical terminology step by step[M]. SaintLouis: C.V. Mosby.1975.
[36] Catherine A, Smith MA. Consumer language, patient language, and thesauri: areview of the literature[J]. J Med Libr Assoc,2011,99(2):135-144.
[37] Keselman A, Logan R, Smith CA,et al. Developing informatics tools andstrategies for consumer-centered health communication[J].J Am Med InformAssoc,2008,15(4):473-483.
[38] Scott N, Weiner MF.―Patientspeak‖: An Exercise in Communication[J]. J MedEduc,1984;59(11):890-893.
[39] Patrick TB, Monga HK, Sievert ME, et al. Evaluation of controlled vocabularyresources for development of a consumer entry vocabulary for diabetes[J].J MedInternet Res,2001,3(3):E24.
[40] McCray AT, Loane RF, Browne AC, et al. Terminology issues in user access toWeb-based medical information[J].Proc AMIA Symp,1999:107–111.
[41] United States National Library of Medicine, National Institutes of Health: LongRange Plan2000-2005. GOALS AND OBJECTIVES. http://www.nlm.nih.gov/archive/20120607/pubs/plan/nlm_lrp_2000.pdf
[42] Shiri AA, Revie C. Thesauri on the Web: current developments and trends[J].Online Inf Rev,2000,24(4):273-279.
[43] Lewis D, Brennan PF, McCray AT, et al. If we build it, they will come:standardized consumer vocabularies[J].Medinfo,2001:1530.
[44] Patrick TB, Monga HK, Sievert ME, et al. Evaluation of controlled vocabularyresources for development of a consumer entry vocabulary for diabetes[J].J MedInternet Res,2001,3(3):E24.
[45] Zeng QT, Tse T, Crowell J,et al. Identifying Consumer-Friendly Display (CFD)Names for Health Concepts[J]. Proc AMIA Symp,2005,(1):859-863.
[46] Zeng QT, Tse T. Exploring and developing consumer health vocabularies[J]. JAm Med Inf Assoc,2006,13(1):24-29.
[47] Zeng QT, Tse T, Divita G, et al. Term Identification Methods for ConsumerHealth Vocabulary Development[J]. J Med Internet Res,2007,9(1):29-33.
[48]杜甲珺.中文用户健康词表构建方法的实证研究[D].长春：吉林大学，2009.
[49]曹锦丹，杜甲珺.中文用户健康词表构建及其实证研究[J].情报学报,2010,(5):841-846.
[50]罗伯特.索尔索著.黄希庭等译.认知心理学[M].教育科学出版社,1990年.第1版.
[51]褚凌云.主题熟悉度对词汇附带习得影响的实验分析[J].边疆经济与文化,2012,(7):140-141.
[52]张萌，张积家.熟悉性和语境对中学生成语理解的影响[J].中国科教创新导刊,2008,(30):192-193.
[53]郑剑虹，严丽芬.自我人格形容词好恶度、熟悉度和意义度的初步测定[J].宁波大学学报（教育科学版）,2010,32(1):60-63.
[54]罗家平.熟悉度和预测性对汉语成语理解影响的实验研究[D].长沙.湖南师范大学.2010.
[55] Libben MR,Titone DA.The multidetermined nature of idiom processing[J].Mem Cognit,2008,36(6):1103-1112.
[56] B1asko DG, Connine CM. Effects of familiarity and aptness on the processingof metaphor[J]. J Exp Psychol Learn Mem Cogn,1993,19(2):295-308.
[57] Zeng Q, Tse T, Crowell J, et al. Identifying consumer friendly display (CFD)names for health concepts[J]. AMIA Annu Symp Proc2005,2005:859-863.
[58] Zeng Q, Kim E, Crowell J,et al. A Text Corpora-Based Estimation of theFamiliarity of Health Terminology[C]. In: Oliveira JL, Maojo V, Martin-SanchezF, Pereira AS, editors. Biological and Medical Data Analysis:6th InternationalSymposium (ISBMDA2005) Proceedings;2005Nov10-11; Aveiro, Portugal.Berlin: Springer;2005. p.184-92.
[59] Keselman1A, Tse T, Crowell J, etal. Assessing Consumer Health VocabularyFamiliarity: An Exploratory Study[J]. J Med Internet Res,2007,9(1): e5.
[60] Ingwersen P. Toward a new research paradigm in information retrieval. In IreneWormell(eds.). Knowledge engineering: expert systems and informationretrieval[M]. London: Taylor Graham Publishing,1987:154-155.
[61] Dervin B. Communication gaps and inequities: Moving toward areconceptualization[J]. Prog Commun,1980,(2):73-112.
[62]柯青,王秀峰,孙建军.以用户为中心的研究范式——理论起源[J].情报资料工作,2008,(4):51-55.
[63] Kathleen D, Dervin B. Examplars of use of sence-making methodolog.Electronic journal of communication[EB/OL].1999,9(2-4) http://www.cios.org/www/ejc/v9n23499.htm.
[64] Belkin NJ, Oddy RN, Brooks HM. Ask for information retrieval: Part I,background and theory[J]. J Doc,1982,38(2):61-71.
[65] Belkin NJ, Oddy RN, Brooks HM. Ask for information retrieval: Part I, resultsof a design study[J]. J Doc,1982,38(3):145-164.
[66]颜端武，王日芬.信息获取与用户服务[M].北京：科学出版社,2010.1.P59.
[67]荣毅虹，田也壮.论信息的可理解[J].情报学报,2006,(4):393-398.
[68] Wurman RS. Information anxiety[M].New York:Doubleday.1989：1
[69] Belkin NJ. Information concepts for information science[J]. J Doc,1978,34(1):55-85.
[70] Belkin NJ, Orrd RN, Brooks HM. ASK for information retrieval[J]. J Doc,1982,38(2):61-71.
[71]胡昌平.信息服务与用户[M].武汉:武汉大学出版社,2008.
[72] Bodenreider O. The Unified Medical Language System(UMLS):IntegratingBiomedical Terminology[J]. Nucleic Acids Res,2004,32(databaseissue):D267-270.
[73] Rosse C, Mejino JVL. A reference ontology for biomedical informatics: theFoundational Model of Anatomy[J]. J Biomed Inform,2003,36(6):478-500.
[74] Burgun A, Bodenreider O. Comparing terms, concepts and semantic classes inWordNet and unified medical language system[C]. In proceedings of theWorkshop on WordNet and other Lexical Resources: Applications, Extensionsand Customizations. Pittsburgh, PA.2001.P77-82.
[75] Keselman A, Smith CA, Divita G, et al. Consumer health concepts that do notmap to the UMLS: where do they fit?[J]. J AM Med InformAssoc,2008,15(4):496-505.
[76] Smith B, Fellbaum C. Medical WordNet: A New Methodology for theConstruction and Validation of Information Resources for Consumer Health[C].Proceedings of Coling: The20th International Conference on ComputationalLinguistics, Geneva23-27. August2004, P31-38. http://ontology.buffalo.edu/medo/MedicalWordNet_Coling.pdf.
[77] Cardillo E. A lexi-ontological resource for consumer healthcare: The Italianconsumer medical vocabulary[D]. University of Trento, Italy,2011.
[78] Elhadad N, Sutaria K. Mining a lexicon of technical terms and layequivalents[C]. BioNLP '07Proceedings of the Workshop on BioNLP2007:Biological, Translational, and Clinical Language Processing.2007,P49-56.http://dl.acm.org/citation.cfm?id=1572402.
[79] Teufel，Elhadad N. Collection and Linguistic Processing of a Large-scaleCorpus of Medical Articles[C]. In Proc.LREC’02,2002. P1214-1218.
[80] Muresan S，Klavans JL.Inducing Terminologies From Text: A Case Study for theConsumer Health Domain[J]. J Am Soc Inf Sci Technol,2013,64(4):727-744.
[81] Doing-Hrris KM, Zeng-Treitler Q.Computer-assisted update of a consumerhealth vocabulary through mining of social network data[J]. J Med Internet Res,2011,13(2):e37.
[82] Keselman A, Tse T, Crowell J,et al. Assessing Consumer Health VocabularyFamiliarity: An Exploratory Study[J]. J Med Internet Res,2007,9(1): e5.
[83] Zeng Q, Kim E, Crowell J, et al. A Text Corpora-Based Estimation of theFamiliarity of Health Terminology[C]. Biological and Medical Data Analysis:6th International Symposium (ISBMDA2005) Proceedings;2005Nov10-11;Aveiro, Portugal. Berlin: Springer;2005.184-192.
[84] Keselman1A, Tse T, Crowell J, et al. Assessing Consumer Health VocabularyFamiliarity: An Exploratory Study[J]. J Med Internet Res,2007,9(1)：e5.
[85] Sun XY, Shi YH,Zeng Q. Determinants of health literacy and health behaviorregarding infectious respiratory diseases: a pathway model[J].BMC PublicHealth,2013,13:261.
[86] Berkman ND, Davis TC, McCormack L. Health Literacy: What Is It?[J]. JHealth Commun,2010,13(Suppl2):9-19.
[87] Columbia University School of Nursing.Health Literacy Assessment Tool[EB/OL].[2012-8-8]. http://cumc.columbia.edu/dept/nursing/ebp/HealthLitRes/assessTool.html..
[88] Parker RM, Baker DW,Williams MV et al. The test of functional health literacyin adults: a new instrument for measuring patients’literacy skills[J]. J Gen InternMed,1995,10(10):537-541.
[89]中国公民健康素养：基本知识与技能（试行）[EB/OL].(2008-01-04)[2013-11-04]. http://www.gov.cn/gzdt/2008-02/05/content_884068.htm.
[90] Fishel CC, Graham KE, Greer DM, et al. CINAHL list of subject headings: anursing thesaurus revised[J]. Bull Med Libr Assoc,1985,73(2):153-159.
[91] Marshall J. Controlled vocabularies: a primer[J]. KeyWords,2005,13(4):120-124.
[92] Zeng Q, Kim E, Crowell J，et al. A Text Corpora-Based Estimation of theFamiliarity of Health Terminology[C]. In: Oliveira JL, Maojo V,Martin-Sanchez F, Pereira AS, editors. Biological and Medical Data Analysis:6th International Symposium (ISBMDA2005) Proceedings;2005Nov10-11;Aveiro, Portugal. Berlin: Springer;2005. p184-192.
[93] Lee SD, Bender DE, Ruiz RE, et al. Development of an easy-to-use SpanishHealth Literacy test[J]. Health Serv Res,2006,41(4Pt1):1392-1412.
[94] Furnas GW, Landauer TK, Gomez LM, et al. The vocabulary problem inhuman-system communication[J].Commun of the ACM,1987,30(11):964-971.
[95] Keselman1A，Tse T，Crowell J，et al. Assessing Consumer Health VocabularyFamiliarity：An Exploratory Study[J]. J Med Internet Res,2007，9(1):e5.
[96] Zeng Q, Tse T, Crowell J, et al. Identifying consumer friendly display (CFD)names for health concepts[J]. AMIA Annu Symp Proc2005,2005:859-863.