Assessing COVID-19 Health Information on Google Using the Quality Evaluation Scoring Tool (QUEST): Cross-sectional and Readability Analysis
AuthorsBachu, Vismaya S.
Holler, Albert E.
Bachu, Dheevena M.
Parker, Pearman D.
Prabhu, Arpan V.
UMass Chan AffiliationsDepartment of Internal Medicine
Document TypeJournal Article
online health information
Public Health Education and Promotion
MetadataShow full item record
AbstractBackground: The COVID-19 pandemic spurred an increase in online information regarding disease spread and symptomatology. Objective: Our purpose is to systematically assess the quality and readability of articles resulting from frequently Google-searched COVID-19 terms in the United States. Methods: We used Google Trends to determine the 25 most commonly searched health-related phrases between February 29 and April 30, 2020. The first 30 search results for each term were collected, and articles were analyzed using the Quality Evaluation Scoring Tool (QUEST). Three raters scored each article in authorship, attribution, conflict of interest, currency, complementarity, and tone. A readability analysis was conducted. Results: Exactly 709 articles were screened, and 195 fulfilled inclusion criteria. The mean article score was 18.4 (SD 2.6) of 28, with 7% (14/189) scoring in the top quartile. National news outlets published the largest share (70/189, 36%) of articles. Peer-reviewed journals attained the highest average QUEST score compared to national/regional news outlets, national/state government sites, and global health organizations (all P<.05). The average reading level was 11.7 (SD 1.9, range 5.4-16.9). Only 3 (1.6%) articles were written at the recommended sixth grade level. Conclusions: COVID-19-related articles are vastly varied in their attributes and levels of bias, and would benefit from revisions for increased readability.
Bachu VS, Mahjoub H, Holler AE, Crihalmeanu T, Bachu DM, Ayyaswami V, Parker PD, Prabhu AV. Assessing COVID-19 Health Information on Google Using the Quality Evaluation Scoring Tool (QUEST): Cross-sectional and Readability Analysis. JMIR Form Res. 2022 Feb 11;6(2):e32443. doi: 10.2196/32443. PMID: 34995206; PMCID: PMC8843082. Link to article on publisher's site
Permanent Link to this Itemhttp://hdl.handle.net/20.500.14038/27542
RightsCopyright ©Vismaya S Bachu, Heba Mahjoub, Albert E Holler, Tudor Crihalmeanu, Dheevena M Bachu, Varun Ayyaswami, Pearman D Parker, Arpan V Prabhu. Originally published in JMIR Formative Research (https://formative.jmir.org), 11.02.2022. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included.
Except where otherwise noted, this item's license is described as Copyright ©Vismaya S Bachu, Heba Mahjoub, Albert E Holler, Tudor Crihalmeanu, Dheevena M Bachu, Varun Ayyaswami, Pearman D Parker, Arpan V Prabhu. Originally published in JMIR Formative Research (https://formative.jmir.org), 11.02.2022. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included.
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