An evaluation of the quality of COVID-19 websites in terms of HON principles and using DISCERN tool.

  • Published In: Health Information & Libraries Journal, 2023, v. 40, n. 4. P. 371 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Safdari, Reza; Gholamzadeh, Marsa; Saeedi, Soheila; Tanhapour, Mozhgan; Rezayi, Sorayya 3 of 3

Abstract

Background: As many people relied on information from the Internet for official scientific or academically affiliated information during the COVID-19 pandemic, the quality of information on those websites should be good. Objective: The main purpose of this study was to evaluate a selection of COVID-19-related websites for the quality of health information provided. Method: Using Google and Yahoo, 36 English language websites were selected, in accordance with the inclusion criteria. The two tools were selected for evaluation were the Health on the Net (HON) Code and the 16-item DISCERN tool. Results: Most websites (39%) were related to information for the public, and a small number of them (3%) concerned screening websites in which people could be informed of their possible condition by entering their symptoms. The result of the evaluation by the HON tool showed that most websites were reliable (53%), and 44% of them were very reliable. Based on the assessment results of the Likert-based 16-item DISCERN tool, the maximum and minimum values for the average scores of each website were calculated as 2.44 and 4.25, respectively. Conclusion: Evaluation using two widely accepted tools shows that most websites related to COVID-19 are reliable and useful for physicians, researchers and the public. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Health Information & Libraries Journal. 2023/12, Vol. 40, Issue 4, p371
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2023
  • ISSN:1471-1834
  • DOI:10.1111/hir.12454
  • Accession Number:174591283
  • Copyright Statement:Copyright of Health Information & Libraries Journal is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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