Evaluation of the quality of depression-related information on Chinese websites and video platforms: a cross-sectional comparative analysis

Front Psychiatry. 2024 Dec 12;15:1408384. doi: 10.3389/fpsyt.2024.1408384. eCollection 2024.

ABSTRACT

OBJECTIVES: We aimed to assess the quality of information regarding depression on Chinese websites and popular video platforms.

METHODS: We conducted searches on website platforms (Baidu, Bing) and video platforms (Bilibili, Douyin) using search terms “depression”, “depressive disorder”, “depression treatment”, “depressive anxiety”, “depressed patient”, and “depressive symptoms”. We collected the first 50 results with each search term in each platform. Each website and video included in this study was assessed using The DISCERN instrument (DISCERN), Journal of American Medical Association benchmark criteria (JAMA), Hexagonal Radar Schema (HRS), and Global Quality Scores (GQS).

RESULTS: A total of 177 websites, 216 Bilibili videos, and 244 Douyin videos were included. Among all the platforms, websites had the highest median scores on DISCERN and HRS, at 33 and 2 respectively, but were still classified as “poor” and “very poor” according to the classification. Bilibili, Douyin, and websites had median scores of 3, 2, and 2 respectively in JAMA, indicating a moderate level of quality. Bilibili, Douyin, and websites all had a median score of 2 for GQS, and were of poor quality. Only the percentage score for JAMA was more than half of the weighted score, while none of the other scales reached half of the score. The median percentage scores of the websites in DISCERN, HRS, and GQS were higher than those of Bilibili and Douyin (P < 0.001). Compared to other sources, news media on websites and medical organizations on videos demonstrated higher quality (all P values < 0.05).

CONCLUSIONS: The findings of the study indicated an overall low quality of online depression information. Collaborative efforts between platforms and professionals are necessary to improve the comprehensiveness and quality of depression-related information available online. In addition, the platform needs to prioritize optimizing the algorithm of recommendations and present real high-quality health information to the audience.

PMID:39726915 | PMC:PMC11669601 | DOI:10.3389/fpsyt.2024.1408384