Evidence map›Paper›PMID 30735144›Full record

ArticleJournal of medical Internet research2019

Evaluating the Quality of Health Information in a Changing Digital Ecosystem.

Alla Keselman, Catherine Arnott Smith, Anita C Murcko, David R Kaufman

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
47citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

47 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  4. Rewriting Doom: Serial Pathways from eHealth Use to Reduced Cancer Fatalism in Family Cancer History Groups.Journal of cancer education : the official journal of the American Association for Cancer Education · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Alla KeselmanDivision of Specialized Information Services, National Library of Medicine, National Institutes of Health, Bethesda, MD, United States.ORCID 0000-0002-3267-761X
Catherine Arnott SmithUniversity of Wisconsin-Madison, Madison, WI, United States.ORCID 0000-0002-3683-2074
Anita C MurckoArizona State University, Scottsdale, AZ, United States.ORCID 0000-0002-2424-0987
David R KaufmanArizona State University, Scottsdale, AZ, United States.ORCID 0000-0001-9968-9968

Funding

National Biomedical Information ServicesZIHLM200888 · NLM · NATIONAL LIBRARY OF MEDICINE · PI PALTOO, DINA · 2009 to 2025
$5200.7M
6 · The paper itself

Abstract

backgroundCritical evaluation of online health information has always been central to consumer health informatics. However, with the emergence of new Web media platforms and the ubiquity of social media, the issue has taken on a new dimension and urgency. At the same time, many established existing information quality evaluation guidelines address information characteristics other than the content (eg, authority and currency), target information creators rather than users as their main audience, or do not address information presented via novel Web technologies.

objectiveThe aim of this formative study was to (1) develop a methodological approach for analyzing health-related Web pages and (2) apply it to a set of relevant Web pages.

methodsThis qualitative study analyzed 25 type 2 diabetes pages, which were derived from the results of a Google search with the keywords "diabetes," "reversal," and "natural." The coding scheme, developed via a combination of theory- and data-driven approaches, includes 5 categories from existing guidelines (resource type, information authority, validity of background information sources, objectivity, and currency) and 7 novel categories (treatment or reversal method, promises and certainty, criticisms of establishment, emotional appeal, vocabulary, rhetoric and presentation, and use of science in argumentation). The coding involves both categorical judgment and in-depth narrative characterization. On establishing satisfactory level of agreement on the narrative coding, the team coded the complete dataset of 25 pages.

resultsThe results set included "traditional" static pages, videos, and digitized versions of printed newspapers or magazine articles. Treatments proposed by the pages included a mixture of conventional evidence-based treatments (eg, healthy balanced diet exercise) and unconventional treatments (eg, dietary supplements, optimizing gut flora). Most pages either promised or strongly implied high likelihood of complete recovery. Pages varied greatly with respect to the authors' stated background and credentials as well as the information sources they referenced or mentioned. The majority included criticisms of the traditional health care establishment. Many sold commercial products ranging from dietary supplements to books. The pages frequently used colloquial language. A significant number included emotional personal anecdotes, made positive mentions of the word cure, and included references to nature as a positive healing force. Most pages presented some biological explanations of their proposed treatments. Some of the explanations involved the level of complexity well beyond the level of an educated layperson.

conclusionsBoth traditional and data-driven categories of codes used in this work yielded insights about the resources and highlighted challenges faced by their users. This exploratory study underscores the challenges of consumer health information seeking and the importance of developing support tools that would help users seek, evaluate, and analyze information in the changing digital ecosystem.

Indexed as

EcosystemHealth Information ExchangeHumansInternetconsumer health informationeHealtheHealth literacyhealth literacyinformation evaluationinformation literacyinformation qualitytype 2 diabetes mellitus

Identifiers

PMID30735144
PMCPMC6384537

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.