Assessment of educational YouTube videos on proximal humeral fracture treatment (YouTube videos on proximal humeral fractures)
Notice bibliographique
Résumé
Background: Social media platforms have become principal sources of information for patients to gather information before clinic visits. YouTube is a popular platform for educational videos, and since proximal humeral fractures (PHFs) are a common orthopedic trauma injury, this study aimed to assess the characteristics of YouTube videos on PHF and PHF treatment. Methods: The terms "Proximal Humeral Fracture" and "Proximal Humeral Fracture Treatment" were used to gather the videos included in this study. Terms were searched programmatically using YouTube's search Application Program Interface. Top 50 videos from each search term were recorded and combined for a total of 100 videos. Duplicate videos were removed, and the remaining videos were rank ordered by the frequency and order of appearance from the initial search. Any non-English videos or videos irrelevant to the topic were excluded. The first 50 rank-order videos were included. Data collected were categorized into general parameters (eg, number of views, video length), source parameters (eg publisher affiliation, number of subscribers), and video content (eg, topic discussed, media type). Data were analyzed by 4 themes of basic information, information for health-care professionals, treatment, and rehabilitation. Each theme was further categorized by relevant subthemes. Results: Publisher affiliation of the PHF videos was most commonly commercial (56%). Health-care professionals or students were the more common target audience (62%) than patients (36%). The predominant media type used in the videos was lecture-style presentation (52%), followed by demonstration (32%), and interviews (18%). Sixty-two percent of the videos discussed basic information on PHF, such as epidemiology or mechanism of injury. Treatment and rehabilitation were the most popular themes, both discussed in 80% of the videos. Among the subthemes, imaging and operative were the most popular subthemes discussed, discussed in 50% and 58% of the videos, respectively. Conclusion: As YouTube is one of the most popular platforms on the Internet, this study assessed the YouTube videos regarding PHFs and their treatment. This study found the PHF videos to cover diverse topics and to be relevant to both patients and health-care professionals. Hence, they can serve as a valuable resource for patients to supplement information they receive from their care provider. However, as YouTube is a largely unregulated platform, there is a need to advocate for content creation from credible sources such as health-care facilities or providers.
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 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 ».