An Empirical Note on Health Information Digital Divide

2017 ◽  
Vol 8 (2) ◽  
pp. 15-34 ◽  
Author(s):  
Ajeya Jha ◽  
Jaya Rani Pandey
Keyword(s):  
Discriminant Analysis ◽  
Digital Divide ◽  
Health Information ◽  
Information Search ◽  
Geographical Location ◽  
Family Type ◽  
Online Health Information ◽  
Chi Square ◽  
And Gender ◽  
E Mail

Availability of healthcare information on internet has made it possible for patients or their relatives to search for such information. This study was undertaken to find answers to such questions. In all 754 respondents participated in the survey. The variables selected from literature survey and exploratory study are Health Information Digital Divide, Income, Having E-mail id, access to internet, geographical location, Education, family-type, age and gender. As the data is categorical the significance of difference has been calculated using Chi-square test. Later discriminant analysis was conducted to predict patients who make online health information search and the ones who do not. The result show that Income, Having E-mail id, access to internet, geographical location, Education and gender are significant factors that determine the propensity of people for online healthcare search. Age and family-type, however, were found to have no impact on such a behavior. Using discriminant analysis 94.5 percent patients who make online health information search could be correctly predicted.

An Empirical Note on Health Information Digital Divide

2020 ◽  
pp. 191-212
Author(s):  
Ajeya Jha ◽  
Jaya Rani Pandey
Keyword(s):  
Discriminant Analysis ◽  
Digital Divide ◽  
Health Information ◽  
Information Search ◽  
Geographical Location ◽  
Family Type ◽  
Online Health Information ◽  
Result Show ◽  
And Gender ◽  
E Mail

Availability of healthcare information on internet has made it possible for patients or their relatives to search for such information. This study was undertaken to find answers to such questions. In all 754 respondents participated in the survey. The variables selected from literature survey and exploratory study are Health Information Digital Divide, Income, Having E-mail id, access to internet, geographical location, Education, family-type, age and gender. As the data is categorical the significance of difference has been calculated using Chi-square test. Later discriminant analysis was conducted to predict patients who make online health information search and the ones who do not. The result show that Income, Having E-mail id, access to internet, geographical location, Education and gender are significant factors that determine the propensity of people for online healthcare search. Age and family-type, however, were found to have no impact on such a behavior. Using discriminant analysis 94.5 percent patients who make online health information search could be correctly predicted.

An Empirical Note on Health Vulnerability and Health Information Digital Divide

2020 ◽  
pp. 26-48
Author(s):  
Jaya Rani ◽  
Ajeya Jha ◽  
Jitendra Kumar ◽  
Samrat Kumar Mukherjee ◽  
Saibal Kumar Saha
Keyword(s):  
Discriminant Analysis ◽  
Digital Divide ◽  
Health Information ◽  
Information Search ◽  
Geographical Location ◽  
Online Health Information ◽  
Chi Square ◽  
Healthcare Information ◽  
Information Searches ◽  
And Gender

Availability of healthcare information on the Internet has made it possible for patients or their relatives to search for such information. Considering the delicate nature of such information as well as its great need felt by the society, it is important to know who are these people who actively search for online healthcare information and also those who are unable to do so. In all, 754 respondents participated in the survey. The variables selected from literature survey and exploratory study are Health Information Digital Divide, Income, Having E-mail id, access to Internet, geographical location, Education, family-type, age, and gender. As the data is categorical, the significance of difference has been calculated using Chi-square test. Later discriminant analysis was conducted to predict patients who make online health information searches and the ones who do not. Using discriminant analysis, 94.5 percent of patients who make online health information searches could be correctly predicted. Prediction is 99.7% for the patients who do not indulge in online health information search.

Online Health Information Consumers’ Learning across Health-Related Search Tasks from the Perspective of Retrieval Platform Switching (Preprint)

2021 ◽  
Author(s):  
Yijing Chen ◽  
Hanming Lin ◽  
Jin Zhang ◽  
Yiming Zhao
Keyword(s):  
Information Retrieval ◽  
Health Information ◽  
Information Search ◽  
Health Knowledge ◽  
Online Health Information ◽  
Related Information ◽  
Search Tasks ◽  
Health Related ◽  
Platform Switching ◽  
Information Consumers

BACKGROUND Online health information retrieval has been a top choice for acquiring health information and knowledge by millions worldwide. OBJECTIVE This study aims to investigate consumers’ modification of retrieval platform switch paths across health-related search tasks and learning via such a change. METHODS A lab user experiment was designed to obtain data on consumers’ health information search behavior. Participants accomplished health-related information search tasks. Screen movements were recorded by EV screen-recording software. The participants underwent in-depth interviews immediately after finishing the tasks. Screen recordings and interview data were both coded and analyzed. RESULTS Three types of learning, including the similar transfer learning, optimizing learning, and SERP-guided learning were identified based on five change patterns of retrieval platform switch paths adopted by health information consumers from task 1 to task 2. Health information consumers’ retrieval platform switch based on information usefulness evaluation. And they accessed different amounts and types of health knowledge from different retrieval platforms. CONCLUSIONS The results suggest that health information consumers exhibit learning both through retrieval platform switching and the knowledge they consume during the search process. This facilitates the assessment of a certain retrieval platform’s usefulness by measuring the amount and types of health knowledge in each search result. This study also contributes to the enhancement of consumers’ health information retrieval abilities, and to helping optimize health information retrieval platforms by increasing their exposure to consumers and increasing the matching degree between knowledge types and consumer needs.

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