Evidence map›Paper›PMID 42027711›Full record

ArticleDigital health

Quality of ovarian cancer videos on TikTok, Rednote, and WeChat: A public health perspective.

Xuan Zhou, YunTao Hao, ChunHui Wang, ShuYu Han, Xin Sun, YaQian Lin, XiaoHua Wang

Abstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Xuan ZhouDepartment of Gynecology, Affiliated Hospital of Chengde Medical University, Chengde, Hebei, China.
YunTao HaoDepartment of Gynecology, Affiliated Hospital of Chengde Medical University, Chengde, Hebei, China.
ChunHui WangDepartment of Gynecology, Affiliated Hospital of Chengde Medical University, Chengde, Hebei, China.
ShuYu HanDepartment of Gynecology, Affiliated Hospital of Chengde Medical University, Chengde, Hebei, China.
Xin SunDepartment of Gynecology, Affiliated Hospital of Chengde Medical University, Chengde, Hebei, China.
YaQian LinDepartment of Gynecology, Affiliated Hospital of Chengde Medical University, Chengde, Hebei, China.
XiaoHua WangDepartment of Gynecology, Affiliated Hospital of Chengde Medical University, Chengde, Hebei, China.ORCID https://orcid.org/0009-0001-9991-7462

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Ovarian cancer is one of the most common and lethal gynecological malignancies, with high mortality rates due to late-stage diagnoses and frequent recurrence. Despite its significant global health impact, public awareness of ovarian cancer remains low, contributing to delayed diagnosis and treatment. The rapid rise of short-video platforms, such as TikTok, Rednote, and WeChat, presents an opportunity to enhance public knowledge and early detection of ovarian cancer. Method: This study analyzed ovarian cancer-related videos from TikTok, Rednote, and WeChat using two validated quality assessment tools: the Global Quality Scale (GQS) and the modified DISCERN (mDISCERN) tool. A total of 220 videos were examined for content quality, thematic coverage, and engagement metrics. The videos were categorized into themes such as epidemiology, etiology, symptoms, diagnosis, treatment, complications, and maintenance therapy. Additionally, user comments were analyzed to assess public sentiment toward the videos. The study also examined the correlation between video quality and audience engagement. Results: Videos on TikTok demonstrated the highest quality in terms of accuracy and engagement, followed by Rednote, with WeChat videos showing the lowest quality. A significant gap was observed in the coverage of critical topics such as complications and maintenance therapy, with many videos providing only partial information. Specialist-created videos scored higher in quality compared to those created by non-specialists and individual users. Moreover, higher-quality videos were associated with greater audience engagement, including more likes, shares, comments, and collections. Positive sentiment in user comments was most strongly correlated with videos focusing on treatment and maintenance therapy. Conclusion: This study reveals significant quality variation in ovarian cancer videos across Chinese platforms. To enhance public health communication, content accuracy must improve, with a focus on professional creators and key topics like complications and maintenance therapy.

Indexed as

information qualityovarian cancerRednoteshort videos platformsTikTokWeChat

Identifiers

PMID42027711
PMCPMC13100421

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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.