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Characterizing the Processes for Navigating Internet Health Information Using Real-Time Observations: A Mixed-Methods Approach

Overview of attention for article published in Journal of Medical Internet Research, July 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (83rd percentile)

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Article details
Title
Characterizing the Processes for Navigating Internet Health Information Using Real-Time Observations: A Mixed-Methods Approach
Published in
Journal of Medical Internet Research, July 2015
DOI 10.2196/jmir.3945
Pubmed ID
Authors
Abstract

Little is known about the processes people use to find health-related information on the Internet or the individual characteristics that shape selection of information-seeking approaches. Our aim was to describe the processes by which users navigate the Internet for information about a hypothetical acute illness and to identify individual characteristics predictive of their information-seeking strategies. Study participants were recruited from public settings and agencies. Interested individuals were screened for eligibility using an online questionnaire. Participants listened to one of two clinical scenarios-consistent with influenza or bacterial meningitis-and then conducted an Internet search. Screen-capture video software captured Internet search mouse clicks and keystrokes. Each step of the search was coded as hypothesis testing (etiology), evidence gathering (symptoms), or action/treatment seeking (behavior). The coded steps were used to form a step-by-step pattern of each participant's information-seeking process. A total of 78 Internet health information seekers ranging from 21-35 years of age and who experienced barriers to accessing health care services participated. We identified 27 unique patterns of information seeking, which were grouped into four overarching classifications based on the number of steps taken during the search, whether a pattern consisted of developing a hypothesis and exploring symptoms before ending the search or searching an action/treatment, and whether a pattern ended with action/treatment seeking. Applying dual-processing theory, we categorized the four overarching pattern classifications as either System 1 (41%, 32/78), unconscious, rapid, automatic, and high capacity processing; or System 2 (59%, 46/78), conscious, slow, and deliberative processing. Using multivariate regression, we found that System 2 processing was associated with higher education and younger age. We identified and classified two approaches to processing Internet health information. System 2 processing, a methodical approach, most resembles the strategies for information processing that have been found in other studies to be associated with higher-quality decisions. We conclude that the quality of Internet health-information seeking could be improved through consumer education on methodical Internet navigation strategies and the incorporation of decision aids into health information websites.

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X Demographics

X Demographics

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Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 83 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 83 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Master 21 25%
Student > Ph. D. Student 12 14%
Researcher 9 11%
Student > Doctoral Student 5 6%
Student > Bachelor 5 6%
Other 13 16%
Unknown 18 22%
Readers by discipline
Readers by discipline Count As %
Medicine and Dentistry 11 13%
Nursing and Health Professions 9 11%
Business, Management and Accounting 8 10%
Psychology 8 10%
Social Sciences 8 10%
Other 17 20%
Unknown 22 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 February 2016.
All research outputs
#4,871,692
of 33,117,993 outputs
Outputs from Journal of Medical Internet Research
#3,503
of 10,557 outputs
Outputs of similar age
#49,688
of 292,708 outputs
Outputs of similar age from Journal of Medical Internet Research
#58
of 78 outputs
Altmetric has tracked 33,117,993 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,557 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 17.1. This one has gotten more attention than average, scoring higher than 66% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 292,708 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 83% of its contemporaries.
We're also able to compare this research output to 78 others from the same source and published within six weeks on either side of this one. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.