Quality Assessment of Videos About Dengue Fever on Douyin: Cross-Sectional Study
Notice bibliographique
Résumé
Background: Dengue fever has evolved into a significant public health concern. In recent years, short-video platforms such as Douyin have emerged as prominent media for the dissemination of health education content. Nevertheless, there is a paucity of research investigating the quality of health education content on Douyin. Objective: This study aimed to evaluate the quality of dengue videos on Douyin. Methods: A comprehensive collection of short videos pertaining to dengue fever was retrieved from the popular social media platform, Douyin, at a designated point in time. A systematic analysis was then performed to extract the characteristics of these videos. To ensure a comprehensive evaluation, three distinct scoring tools were used: the DISCERN scoring tool, the JAMA benchmarking criteria, and the GQS method. Subsequently, an in-depth investigation was undertaken into the relationship between video features and quality. Results: A total of 156 videos were included in the analysis, 81 of which (51.9%) were posted by physicians, constituting the most active category of contributor. The selected videos pertaining to dengue fever received a total of 718,228 likes and 126,400 comments. The video sources were categorized into four distinct classifications: news agencies, organizations, physicians, and individuals. Individuals obtained the highest number of video likes, comments, and saves. However, the findings of the study demonstrated that physicians, organizations, and news agencies posted videos are of higher quality when compared with individuals. The integrity of the video content was analyzed, and the results showed a higher percentage of videos received a score of zero points for outcomes, management, and assessment, with 69 (45%), 57 (37%), and 41 (26%), respectively. The median Total DISCERN scores, JAMA, and GQS of the 156 dengue-related videos under consideration were 26 (out of a total of 80 points), 2 (out of a total of 4 points), and 3 (out of a total of 5 points), respectively. Spearman correlation analysis was conducted, revealing a positive correlation between video duration and video quality. Conversely, a negative correlation was observed between the following variables: video comments and video quality, and the number of days since posting and video quality. Conclusions: This study demonstrates that the quality of short dengue-related health information videos on Douyin is substandard. Videos uploaded by medical professionals were among the highest in terms of quality, yet their videos were not as popular. It is recommended that in future, physicians employ more accessible language incorporating visual elements to enhance the appeal and dissemination of their videos. Future research could explore how to achieve a balance between professionalism and entertainment to promote user acceptance of high-quality content. Moreover, platforms may consider employing algorithmic optimization or content recommendation mechanisms to encourage users to access and engage with more high-quality health science videos.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».