Descriptive and content analysis of breast cancer vlogs on YouTube.
Notice bibliographique
Résumé
258 Background: Vlogs, or “video blogs,” are personally-created experiential videos based on wide-ranging topics, usually posted to YouTube. Many women with breast cancer (BC) document their cancer experiences in YouTube vlogs. These may have the potential to serve as peer-to-peer support and provide community. This study provides a descriptive and content analysis of vlogs by women with BC. Methods: YouTube was searched in incognito mode in 11/2023 using the search terms “breast cancer vlog.” A maximum of 10 videos/creator were included based on viewership and date created.Video characteristics collected included: title, length, number of views, likes, comments, and playlist inclusion. Videos were assessed for sponsorship, presence of explanation and discussion on BC, type of content, and themes. Creator characteristics included age, location, and engagement approaches. Descriptive and content analysis were performed to assess and analyze video content and potential areas where peer-to-peer support may be provided. Results: 90 vlogs by 13 creators were included, all originating from personal accounts. Mean video length, number of views, and number of comments were 21.4 minutes (SD 9.1), 266,780 (SD 534,465), and 1485 (SD 3422), respectively. 38.9% included hashtags. 12.2% included paid sponsorships. Most common filming location was at home (96.7%), followed by the hospital (31.1%), or in the car (21.1%). Home vlogs were most often set in the living room (44.3%), bedroom (33.0%), or kitchen (20.6%). 56.7% included visuals of treatment as well as physical findings. Creators addressed motivation for vlogging in 48.9%; the two most common reasons were wanting to build a community and helping others in a similar situation. In 46.7%, creators explicitly expressed emotion. Most common themes were treatment (85.6%), mental health (81.1%), side effects (72.2%), appearance (63.3%), and family relationships (36.7%). Subthemes included young age, finances, and the importance of online community support. Patient-directed advice was offered in 60.0%, mostly on treatment-related issues. In 56.7%,creators provided explicit treatment definitions. Chemotherapy was discussed in 70.0%; surgery in 57.8%, primarily mastectomy; radiation in 30.0%; general side effects in 71.1%. 24.4% were about a new diagnosis. When mentioned (44.4%), most common creator location was the USA. When mentioned (30.0%), most common age demographic was 20-29 years old. Conclusions: Vlogs by women with BC receive significant levels of engagement. The dedication to building community demonstrated by vlog creators, and the personal nature of their storytelling, advice, and suggestions, may make these vlogs a potential resource for peer-to-peer support.
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 | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».