TikTok as a Platform for Patient Education and Health Information in Rare Genetic Diseases: Cross-Sectional Study
Notice bibliographique
Résumé
Background: Rare genetic diseases pose significant diagnostic and therapeutic challenges, often leading to delayed diagnoses, misinformation, and patient isolation. Social media platforms have emerged as prominent spaces for health information dissemination and community building among patients with rare diseases. Objective: This study aimed to evaluate the role of TikTok videos in patient education, community engagement, and information quality related to 5 rare genetic conditions: Ehlers-Danlos syndrome, Marfan syndrome, cystic fibrosis, Wilson disease, and Gaucher disease. Methods: A cross-sectional analysis was conducted on 184 TikTok videos identified via disease-specific hashtags. Included videos were 15 seconds to 4 minutes long and directly discussed the target diseases. Advertisements, promotional content, and product marketing were excluded. Videos were categorized by creator type: physicians, medical professionals, patients, influencers, nonprofit organizations, and others. Content quality was assessed using the Global Quality Scale (GQS) and a modified DISCERN tool (mDISCERN). Engagement metrics (views, likes, and shares) were recorded. Kruskal-Wallis and chi-square tests evaluated differences across creator categories. Results: Of the 184 TikTok videos, 88 (47.8%) were created by patients or family members; 31 (16.8%) by influencers, 24 (13.0%) by physicians, 17 (9.2%) by nonprofit organizations, 15 (8.2%) by general users, and 9 (4.9%) by others. Collectively, the videos amassed more than 123 million views. Influencer-generated content accounted for the highest cumulative view count, totaling approximately 60.9 million views. Content produced by medical professionals and physicians demonstrated higher information quality, with mean GQS scores of 3.89 (SD 0.66) and 3.62 (SD 0.71) and mDISCERN scores of 3.11 (SD 0.58) and 3.21 (SD 0.65), respectively. In contrast, videos by influencers and patients exhibited lower quality scores (influencers: GQS mean 1.48, SD 0.60; mDISCERN mean 1.42, SD 0.55; patients: GQS mean 1.57, SD 0.58; mDISCERN mean 1.38, SD 0.52). For Ehlers-Danlos syndrome (n=40 videos, 21.7%), Wilson disease (n=40 videos, 21.7%), and cystic fibrosis (n=34 videos, 18.5%), significant differences in quality scores among creator types were observed (P<.001, P<.001, and P≤.04, respectively). For Marfan syndrome (n=40 videos, 21.7%) and Gaucher disease (n=30 videos, 16.3%), no significant differences were observed (P=.43 and P=.07, respectively). Chi-square analysis indicated no association between creator type and inclusion of peer-reviewed references (χ25=10.6; P=.07). Overall, only 7 (3.8%) videos cited scientific literature. Conclusions: TikTok serves as a key platform for rare disease awareness and community engagement, although the quality and accuracy of health information vary widely. Although medical professionals produced higher-quality content, it tended to receive less visibility. Increasing the presence of health care professionals and improving visibility of evidence-based content could enhance patient education and safer health information sharing.
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 | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».