Background: Halitosis, characterized by an undesirable mouth odor, represents a common concern.
Objective: This study aims to assess the quality and readability of web-based Arabic health information on halitosis as the internet is becoming a prominent global source of medical information.
Methods: A total of 300 Arabic websites were retrieved from Google using 3 commonly used phrases for halitosis in Arabic. The quality of the websites was assessed using benchmark criteria established by the Journal of the American Medical Association, the DISCERN tool, and the presence of the Health on the Net Foundation Code of Conduct (HONcode). The assessment of readability (Flesch Reading Ease [FRE], Simple Measure of Gobbledygook, and Flesch-Kincaid Grade Level [FKGL]) was conducted using web-based readability indexes.
Results: A total of 127 websites were examined. Regarding quality assessment, 87.4% (n=111) of websites failed to fulfill any Journal of the American Medical Association requirements, highlighting a lack of authorship (authors' contributions), attribution (references), disclosure (sponsorship), and currency (publication date). The DISCERN tool had a mean score of 34.55 (SD 7.46), with the majority (n=72, 56.6%) rated as moderate quality, 43.3% (n=55) as having a low score, and none receiving a high DISCERN score, indicating a general inadequacy in providing quality health information to make decisions and treatment choices. No website had HONcode certification, emphasizing the concern over the credibility and trustworthiness of these resources. Regarding readability assessment, Arabic halitosis websites had high readability scores, with 90.5% (n=115) receiving an FRE score ≥80, 98.4% (n=125) receiving a Simple Measure of Gobbledygook score <7, and 67.7% (n=86) receiving an FKGL score <7. There were significant correlations between the DISCERN scores and the quantity of words (P<.001) and sentences (P<.001) on the websites. Additionally, there was a significant relationship (P<.001) between the number of sentences and FKGL and FRE scores.
Conclusions: While readability was found to be very good, indicating that the information is accessible to the public, the quality of Arabic halitosis websites was poor, reflecting a significant gap in providing reliable and comprehensive health information. This highlights the need for improving the availability of high-quality materials to ensure Arabic-speaking populations have access to reliable information about halitosis and its treatment options, tying quality and availability together as critical for effective health communication.
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http://dx.doi.org/10.2196/54072 | DOI Listing |
J Med Internet Res
December 2024
University Clinic for Interdisciplinary Orthopedic Pathways (UCOP), Elective Surgery Center, Silkeborg Regional Hospital, Silkeborg, Denmark.
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Department of Anesthesia, Intensive Care and Emergency, A.O.U. Città della Salute e della Scienza di Torino, Torino, Italy.
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January 2025
School of Economics and Management, Beijing jiaotong University, Shandong, 264401, China.
Text Graph Representation Learning through Graph Neural Networks (TG-GNN) is a powerful approach in natural language processing and information retrieval. However, it faces challenges in computational complexity and interpretability. In this work, we propose CoGraphNet, a novel graph-based model for text classification, addressing key issues.
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