Quality and Reliability of Adolescent Sexuality Education on Chinese Video Platforms: Sentiment-Topic Analysis and Cross-Sectional Study
Notice bibliographique
Résumé
Background Adolescence is a critical period for lifelong health, which makes access to accurate and comprehensive sexuality education essential. As video platforms become a primary source of information for adolescents, the quality of their content significantly impacts their physical and mental health. Objective This study aimed to evaluate the quality, reliability, understandability, and actionability of adolescent sexuality education videos on major Chinese platforms (Bilibili, TikTok or Douyin, and Kwai), analyze associated user comment sentiment and topics, identify predictors of quality and reliability, and provide recommendations. Methods A cross-sectional analysis was conducted (April 2025) on the top 100 comprehensively ranked comprehensive sexuality education videos (N=300 total) retrieved from each platform using the keyword 青春期性教育 (“adolescent sexuality education”). Videos were assessed using the Global Quality Score, modified DISCERN, and Patient Education Materials Assessment Tool (PEMAT-U/A), with interrater reliability assessed via Cohen κ. A corpus of over 49,000 user comments underwent sentiment analysis (fine-tuned RoBERTa) and topic modeling (BERTopic, yielding 29 topics grouped into 6 themes). Statistical analyses included Kruskal-Wallis H tests, Spearman correlations, and stepwise linear regressions (SPSS [version 27.0]; P<.05). Results Video quality and reliability were moderate on Bilibili and TikTok but generally poor on Kwai. Content from verified sources (physicians, educators, and institutional media) demonstrated superior quality and stability compared to highly variable content from individual media (the predominant source type, especially on Kwai; 87/100, 87%). Paradoxically, Kwai exhibited the highest user engagement despite the lowest quality scores. Understandability (PEMAT-U) was consistently the strongest positive predictor for both quality (Global Quality Score, final model adjusted R2=0.383, β=0.485) and reliability (modified DISCERN, final model adjusted R2=0.209, β=0.319). Actionability (PEMAT-A) and video duration were also significant positive predictors. Understandability scores (PEMAT-U) were generally high (approximately 69%), while actionability scores (PEMAT-A) were moderate to low (33%-50%). Sentiment analysis revealed that comments were predominantly neutral (35,372/49,680, 71.2%), with negative comments (9141/49,680, 18.4%) significantly outweighing positive ones (5167/49,680, 10.4%). Key discussion themes identified included sources of knowledge acquisition, sexual safety and prevention, physiology, and sexual health and practices. Conclusions While online video platforms offer accessible channels for adolescent sexuality education in China, the current content is often of moderate-to-poor quality, with questionable reliability and limited actionability. Understandability is paramount, but high engagement does not necessarily correlate with high quality or reliability, potentially amplifying misinformation. To effectively empower youth, critical steps include enhancing content quality by adhering to evidence-based frameworks like the International Technical Guidance on Sexuality Education; strengthening platform accountability through improved verification and algorithms; and promoting user media literacy. These measures aim to foster a healthier and more equitable future for Chinese adolescents, helping to achieve goals related to sexually transmitted infections and pregnancy prevention and promoting more open societal attitudes toward sexuality.
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 | 0,004 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».