Quality Assessment of Radiotherapy Health Information on Short-Form Video Platforms of TikTok and Bilibili: Cross-Sectional Study
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Notice bibliographique
Résumé
Background: Radiotherapy (RT) is a crucial modality in cancer treatment. In recent years, the rise of short-form video platforms has transformed how the public accesses medical information. TikTok and Bilibili, as leading short-video platforms, have emerged as significant channels for disseminating health information. However, there is an urgent need to evaluate the quality and reliability of the information related to RT available on these platforms. Objective: This study aims to systematically assess the information quality and reliability of RT-related short-form videos on TikTok and Bilibili platforms using the Global Quality Score (GQS) and a modified DISCERN (mDISCERN) evaluation tool, thereby elucidating the current landscape and challenges of digital health communication. Methods: This study systematically retrieved the top 100 RT-related videos on TikTok and Bilibili as of February 25, 2025. The quality of the videos was assessed using the GQS (1-5 points) and an mDISCERN scoring system (1-5 points). Statistical analyses were conducted using the Mann-Whitney U test, as well as Spearman and Pearson correlation analyses, to ensure the reliability and validity of the results. Results: A total of 200 short-form videos related to RT were analyzed, revealing that the overall quality of videos on TikTok and Bilibili is unsatisfactory. Specifically, the median GQS for TikTok was 4 (IQR 3-4), while for Bilibili, it was 3 (IQR 3-4). The median mDISCERN scores for both platforms were 3 (IQR 2-4 and 3-4, respectively), and no significant differences were observed between the 2 platforms regarding the GQS (P=.12) and mDISCERN score (P=.10). On TikTok, 53% (53/100) of videos had a GQS of 4 or higher ("good" quality or better). On Bilibili, 45% (45/100) of videos had an mDISCERN score of 4 or higher, indicating "relatively reliable" quality. Videos produced by professionals, institutions, and nonprofessional institutions had significantly higher mDISCERN scores than those made by patients, with statistical significance (P<.001, P<.001, and P<.01, respectively). Furthermore, the correlations between the number of bookmarks and video duration, with mDISCERN scores, were 0.172 (P=.02) and 0.192 (P=.007), respectively. However, no video variables were found to predict the overall quality and reliability of the videos effectively. Conclusions: This study revealed that the overall quality of RT-related videos on TikTok and Bilibili is generally low. However, videos uploaded by professionals demonstrate higher information quality and reliability, providing valuable support for patients seeking guidance on health care management and treatment options for cancers. Therefore, improving the quality and reliability of video content, particularly that produced by patients, is crucial for ensuring that the public has access to accurate medical information.
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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 | 0,004 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 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 découle