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Journal IssueDOI

A model for online consumer health information quality

01 Sep 2009-Journal of the Association for Information Science and Technology (John Wiley & Sons, Ltd)-Vol. 60, Iss: 9, pp 1781-1791
TL;DR: It was showed that consumers may lack the motivation or literacy skills to evaluate the information quality of health Web pages, which suggests the need to develop accessible automatic information quality evaluation tools and ontologies.
Abstract: This article describes a model for online consumer health information consisting of five quality criteria constructs. These constructs are grounded in empirical data from the perspectives of the three main sources in the communication process: health information providers, consumers, and intermediaries, such as Web directory creators and librarians, who assist consumers in finding healthcare information. The article also defines five constructs of Web page structural markers that could be used in information quality evaluation and maps these markers to the quality criteria. Findings from correlation analysis and multinomial logistic tests indicate that use of the structural markers depended significantly on the type of Web page and type of information provider. The findings suggest the need to define genre-specific templates for quality evaluation and the need to develop models for an automatic genre-based classification of health information Web pages. In addition, the study showed that consumers may lack the motivation or literacy skills to evaluate the information quality of health Web pages, which suggests the need to develop accessible automatic information quality evaluation tools and ontologies. © 2009 Wiley Periodicals, Inc.

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Citations
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Journal ArticleDOI
TL;DR: Current evidence indicates that low health literacy (and related skills) play a role in the evaluation of online health information, and future research in this field should specifically focus on health literacy.
Abstract: Background: Recent years have witnessed a dramatic increase in consumer online health information seeking. The quality of online health information, however, remains questionable. The issue of information evaluation has become a hot topic, leading to the development of guidelines and checklists to design high-quality online health information. However, little attention has been devoted to how consumers, in particular people with low health literacy, evaluate online health information. Objective: The main aim of this study was to review existing evidence on the association between low health literacy and (1) people’s ability to evaluate online health information, (2) perceived quality of online health information, (3) trust in online health information, and (4) use of evaluation criteria for online health information. Methods: Five academic databases (MEDLINE, PsycINFO, Web of Science, CINAHL, and Communication and Mass-media Complete) were systematically searched. We included peer-reviewed publications investigating differences in the evaluation of online information between people with different health literacy levels. Results: After abstract and full-text screening, 38 articles were included in the review. Only four studies investigated the specific role of low health literacy in the evaluation of online health information. The other studies examined the association between educational level or other skills-based proxies for health literacy, such as general literacy, and outcomes. Results indicate that low health literacy (and related skills) are negatively related to the ability to evaluate online health information and trust in online health information. Evidence on the association with perceived quality of online health information and use of evaluation criteria is inconclusive. Conclusions: The findings indicate that low health literacy (and related skills) play a role in the evaluation of online health information. This topic is therefore worth more scholarly attention. Based on the results of this review, future research in this field should (1) specifically focus on health literacy, (2) devote more attention to the identification of the different criteria people use to evaluate online health information, (3) develop shared definitions and measures for the most commonly used outcomes in the field of evaluation of online health information, and (4) assess the relationship between the different evaluative dimensions and the role played by health literacy in shaping their interplay.

363 citations

Journal ArticleDOI
01 Apr 2013
TL;DR: The results largely support the proposed model of initial trust formation in Web-based health information, explaining substantial variance in trust and highlighting the important but distinct roles that argument quality, source expertise, and user perceptions of information quality and risk play in determining an individual's decision to trust health information online.
Abstract: As the Internet develops as a medium for disseminating health-related information, research on Web-based health information consumption grows increasingly important to academics and practitioners. Building on the current research in this area, our study proposes a model of initial trust formation in Web-based health information, rooted in the elaboration likelihood model (ELM) and Toulmin's model of argumentation. The proposed model theorizes trust as a function of perceived information quality and perceived risk, which are in turn determined by the structural quality of the message (argument quality) and the expertise of the message source (source expertise). Testing of the research model was accomplished via a field experiment involving 300 online users who had searched for health information on the Web. Overall, the results largely support the proposed model, explaining substantial variance in trust and highlighting the important but distinct roles that argument quality, source expertise, and user perceptions of information quality and risk play in determining an individual's decision to trust health information online.

191 citations

Journal ArticleDOI
TL;DR: This work advocates moving to ontology-based design of information systems to enable more reliable use of routine data to measure health mechanisms and impacts and identifies mechanisms to manage DQ in integrated CDM.

151 citations

Journal ArticleDOI
TL;DR: The existence of dangerous health literacy in connection with searching and using health information on the Internet is verified by exploring the effect of 2 manipulated search engines that yielded either pro or con vaccination sites only.
Abstract: Background: During the past 2 decades, the Internet has evolved to become a necessity in our daily lives. The selection and sorting algorithms of search engines exert tremendous influence over the global spread of information and other communication processes. Objective: This study is concerned with demonstrating the influence of selection and sorting/ranking criteria operating in search engines on users’ knowledge, beliefs, and attitudes of websites about vaccination. In particular, it is to compare the effects of search engines that deliver websites emphasizing on the pro side of vaccination with those focusing on the con side and with normal Google as a control group. Method: We conducted 2 online experiments using manipulated search engines. A pilot study was to verify the existence of dangerous health literacy in connection with searching and using health information on the Internet by exploring the effect of 2 manipulated search engines that yielded either pro or con vaccination sites only, with a group receiving normal Google as control. A pre-post test design was used; participants were American marketing students enrolled in a study-abroad program in Lugano, Switzerland. The second experiment manipulated the search engine by applying different ratios of con versus pro vaccination webpages displayed in the search results. Participants were recruited from Amazon’s Mechanical Turk platform where it was published as a human intelligence task (HIT). Results: Both experiments showed knowledge highest in the group offered only pro vaccination sites ( Z =–2.088, P =.03; Kruskal-Wallis H test [H 5 ]=11.30, P =.04). They acknowledged the importance/benefits ( Z =–2.326, P =.02; H 5 =11.34, P =.04) and effectiveness ( Z =–2.230, P =.03) of vaccination more, whereas groups offered antivaccination sites only showed increased concern about effects ( Z =–2.582, P =.01; H 5 =16.88, P =.005) and harmful health outcomes ( Z =–2.200, P =.02) of vaccination. Normal Google users perceived information quality to be positive despite a small effect on knowledge and a negative effect on their beliefs and attitudes toward vaccination and willingness to recommend the information (χ 2 5 =14.1, P =.01). More exposure to antivaccination websites lowered participants’ knowledge ( J =4783.5, z =−2.142, P =.03) increased their fear of side effects ( J =6496, z =2.724, P =.006), and lowered their acknowledgment of benefits ( J =4805, z =–2.067, P =.03). Conclusion: The selection and sorting/ranking criteria of search engines play a vital role in online health information seeking. Search engines delivering websites containing credible and evidence-based medical information impact positively Internet users seeking health information. Whereas sites retrieved by biased search engines create some opinion change in users. These effects are apparently independent of users’ site credibility and evaluation judgments. Users are affected beneficially or detrimentally but are unaware, suggesting they are not consciously perceptive of indicators that steer them toward the credible sources or away from the dangerous ones. In this sense, the online health information seeker is flying blind. [J Med Internet Res 2014;16(4):e100]

95 citations

Journal ArticleDOI
TL;DR: A moderator analysis involving website type, sample characteristics, and the IQ categories used in articles revealed that whereas website type and IQ categories moderate the relationship between perceived online IQ and consumer satisfaction, sample details do not.

94 citations

References
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Journal ArticleDOI
01 Jan 1973
TL;DR: In this paper, a six-step framework for organizing and discussing multivariate data analysis techniques with flowcharts for each is presented, focusing on the use of each technique, rather than its mathematical derivation.
Abstract: Offers an applications-oriented approach to multivariate data analysis, focusing on the use of each technique, rather than its mathematical derivation. The text introduces a six-step framework for organizing and discussing techniques with flowcharts for each. Well-suited for the non-statistician, this applications-oriented introduction to multivariate analysis focuses on the fundamental concepts that affect the use of specific techniques rather than the mathematical derivation of the technique. Provides an overview of several techniques and approaches that are available to analysts today - e.g., data warehousing and data mining, neural networks and resampling/bootstrapping. Chapters are organized to provide a practical, logical progression of the phases of analysis and to group similar types of techniques applicable to most situations. Table of Contents 1. Introduction. I. PREPARING FOR A MULTIVARIATE ANALYSIS. 2. Examining Your Data. 3. Factor Analysis. II. DEPENDENCE TECHNIQUES. 4. Multiple Regression. 5. Multiple Discriminant Analysis and Logistic Regression. 6. Multivariate Analysis of Variance. 7. Conjoint Analysis. 8. Canonical Correlation Analysis. III. INTERDEPENDENCE TECHNIQUES. 9. Cluster Analysis. 10. Multidimensional Scaling. IV. ADVANCED AND EMERGING TECHNIQUES. 11. Structural Equation Modeling. 12. Emerging Techniques in Multivariate Analysis. Appendix A: Applications of Multivariate Data Analysis. Index.

37,124 citations

Journal ArticleDOI
TL;DR: This chapter discusses Structural Equation Modeling: An Introduction, and SEM: Confirmatory Factor Analysis, and Testing A Structural Model, which shows how the model can be modified for different data types.
Abstract: I Introduction 1 Introduction II Preparing For a MV Analysis 2 Examining Your Data 3 Factor Analysis III Dependence Techniques 4 Multiple Regression Analysis 5 Multiple Discriminate Analysis and Logistic Regression 6 Multivariate Analysis of Variance 7 Conjoint Analysis IV Interdependence Techniques 8 Cluster Analysis 9 Multidimensional Scaling and Correspondence Analysis V Moving Beyond the Basic Techniques 10 Structural Equation Modeling: Overview 10a Appendix -- SEM 11 CFA: Confirmatory Factor Analysis 11a Appendix -- CFA 12 SEM: Testing A Structural Model 12a Appendix -- SEM APPENDIX A Basic Stats

23,353 citations


"A model for online consumer health ..." refers background in this paper

  • ...4, the recommended value for the size of the sample (80 surveys; Hair et al., 2005)....

    [...]

Journal ArticleDOI

8,493 citations


"A model for online consumer health ..." refers methods in this paper

  • ...In the interviews, the critical incident technique (Flanagan, 1954) was used, in which participants were asked to recall a specific incident in which they had sought healthcare information, and to describe their judgments of the information found....

    [...]

Journal ArticleDOI
TL;DR: This article reviewed major advances in verbal reports over the past decade, including new evidence on how giving verbal reports affects subjects' cognitive processes, and on the validity and completeness of such reports.
Abstract: Since the publication of Ericsson and Simon's work in the early 1980s, verbal data has been used increasingly to study cognitive processes in many areas of psychology, and concurrent and retrospective verbal reports are now generally accepted as important sources of data on subjects' cognitive processes in specific tasks. In this revised edition of the book that put protocol analysis on firm theoretical ground, the authors review major advances in verbal reports over the past decade, including new evidence on how giving verbal reports affects subjects' cognitive processes, and on the validity and completeness of such reports. In a new preface Ericsson and Simon summarize the central issues covered in the book and provide an updated version of their information-processing model, which explains verbalization and verbal reports. They describe new studies on the effects of verbalization, interpreting the results of these studies and showing how their theory can be extended to account for them. Next, they address the issue of completeness of verbally reported information, reviewing the new evidence in three particularly active task domains. They conclude by citing recent contributions to the techniques for encoding protocols, raising general issues, and proposing directions for future research.

6,689 citations