Content Quality of YouTube Videos About Metastatic Breast Cancer in Young Women: Systematic Assessment
Notice bibliographique
Résumé
BACKGROUND: Young women with metastatic breast cancer (MBC) are part of a digitally connected generation yet are underserved in terms of information needs. YouTube is widely used to find and identify health information. The accessibility of health-related content on social media together with the rare and marginalized experiences of young women with MBC and the digital media practices of younger generations imply a considerable likelihood that young women with MBC will seek information and community on the internet. OBJECTIVE: This study aims to assess the content quality of MBC YouTube videos and to identify themes in the experiences of young women with MBC based on YouTube videos. METHODS: A systematic assessment of MBC YouTube videos using the search term "metastatic breast cancer young" was conducted in August 2021. The search was performed in an incognito browser and with no associated YouTube or Google account. Search results were placed in order from most to least views. Title, date uploaded, length, poster identity, number of likes, and number of comments were collected. Understandability and actionability were assessed using the Patient Education Materials Assessment Tool (PEMAT); information reliability and quality were assessed with DISCERN. Themes, sponsorships, and health care professionals' and patients' narratives were reported. RESULTS: A total of 101 videos were identified. Of these, 78.2% (n=79) included sponsorships. The mean PEMAT scores were 78.8% (SD 15.3%) and 43.1% (SD 45.2%) for understandability and actionability, respectively. The mean DISCERN score was 2.44 (SD 0.7) out of 5. Identified themes included treatment (n=67, 66.3%), family relationship (n=46, 45.5%), and motherhood (n=38, 37.6%). CONCLUSIONS: YouTube videos about young women with MBC are highly understandable but demonstrate moderate rates of actionability, with low reliability and quality scores. Many have a commercial bias. While web-based materials have limitations, their potential to provide patient support is not fully developed. By acknowledging their patients' media habits, health care professionals can further develop a trusting bond with their patients, provide a space for open and honest discussions of web-based materials, and avoid any potential instances of confusion caused by misleading, inaccurate, or false web-based materials.
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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 | 0,006 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».