Quality and Dissemination of Uterine Fibroid Health Information on TikTok and Bilibili: Cross-Sectional Study
Notice bibliographique
Résumé
Background: The rise of short-video platforms, such as TikTok (Douyin in China) and Bilibili, has significantly influenced how health information is disseminated to the public. However, the quality, reliability, and effectiveness of health-related content on these platforms, particularly regarding uterine fibroids, remain underexplored. Uterine fibroids are a common medical condition that affects a substantial proportion of women worldwide. While these platforms have become vital sources of health education, misinformation and incomplete content may undermine their efficacy. Objective: This study aims to address these gaps by evaluating the quality and dissemination effectiveness of uterine fibroid-related health information on TikTok and Bilibili. Methods: A total of 200 uterine fibroid-related videos (100 from TikTok and 100 from Bilibili) were selected through a keyword search. The videos were evaluated by 2 trained gynecological experts using the Global Quality Score (GQS) and a modified DISCERN (mDISCERN) tool. In addition, the Patient Education Materials Assessment Tool for Audio and Visual Materials was used to assess the understandability and actionability of the videos. Statistical analyses, including the Mann-Whitney U test, Spearman rank correlation, and stepwise regression analysis, were used to assess differences between platforms and identify predictors of video quality. Results: The results indicated that TikTok outperformed Bilibili in terms of user engagement metrics, such as likes, comments, shares, and followers (all P<.001). However, Bilibili videos were generally longer than those on TikTok (P<.001). The videos on both platforms demonstrated suboptimal overall quality and reliability, reflected by median GQS score of 3 (IQR 3-4) for TikTok and the median GQS score of Bilibili is 3 (IQR 2-4). The median modified DISCERN scores were also low: 2 (IQR 2-2) for TikTok and 2 (IQR 2-2) for Bilibili, with no significant differences between the 2 platforms (P=.62 for GQS; P=.18 for mDISCERN). The videos on both platforms yielded comparable median scores for Patient Education Materials Assessment Tool-Understandability (PEMAT-U) and Patient Education Materials Assessment Tool-Actionability (PEMAT-A). The median score of PEMAT-U was 77% (IQR 69%-83%) for TikTok and 77% (IQR 69%-85%) for Bilibili. The PEMAT-A yielded a median score of 67% (IQR 33%-67%) for TikTok and 67% (IQR 0-67%) for Bilibili. Videos uploaded by medical professionals on TikTok had significantly higher quality scores compared to those uploaded by nonprofessionals. A moderate positive correlation was observed between the GQS and mDISCERN scores (r=0.41, P<.01), indicating an interrelationship between quality and reliability. Stepwise regression analysis identified "completeness score," "source," and "PEMAT scores" as significant predictors of video quality. Conclusions: This study highlights the generally low quality of uterine fibroid-related health information on short-video platforms, although TikTok showed better performance in terms of engagement and quality. The involvement of medical professionals was found to enhance video quality. These findings underscore the need for improved oversight of health content on social media platforms and greater involvement of health care professionals to ensure the dissemination of accurate and reliable health information.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».