Breast Cancer Vlogs on YouTube: Descriptive and Content Analyses
Notice bibliographique
Résumé
Background: Many women with breast cancer document their experiences in YouTube vlogs, which may serve as peer-to-peer and community support. Objective: This study aimed to determine (1) the forms of content about breast cancer that tend to be discussed in vlogs, (2) the reasons why women choose to vlog their breast cancer experiences, and (3) the potential for breast cancer vlogs to serve as an alternative or complement to peer-to-peer support as well as a site of digital community overall. Methods: YouTube was searched in incognito mode in November 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 breast cancer; type of content; and themes. Creator characteristics included age, location, and engagement approaches. Descriptive and content analyses were performed to analyze video content and potential areas where peer-to-peer support may be provided. Results: Ninety vlogs by 13 creators were included, all from personal accounts. The mean (SD) video length, number of views, and number of comments were 21.4 (9.1) minutes, 266,780 (534,465), and 1485 (3422), respectively. Of the 90 videos, 35 (39%) included hashtags, and 11 (12%) included paid sponsorships. The most common filming location was the home (87/90; 97%), followed by the hospital (28/90; 31%) and car (19/90; 21%). Home vlogs were most often set in the living room (43/90; 44%), bedroom (32/90; 33%), or kitchen (20/90; 21%). Thirty-four of 60 videos (57%) included treatment visuals and physical findings. Creators addressed motivation for vlogging in 44/90 videos (49%); the two most common reasons were wanting to build a community and helping others. In 42/90 videos (47%), creators explicitly expressed emotion. Most common themes were treatment (77/90; 86%), mental health (73/90; 81%), adverse effects (65/90; 72%), appearance (57/90; 63%), and family relationships (33/90; 37%). Patient-directed advice was offered in 52/90 videos (58%), mostly on treatment-related issues. In 51/90 videos (57%), creators provided explicit treatment definitions. Chemotherapy was discussed in 63/90 videos (70%); surgery in 52/90 (58%), primarily mastectomy; radiation in 27/90 (30%); and general adverse effects in 64/90 (71%). Twenty-two of 90 videos (24%) were about a new diagnosis. When mentioned (40/90; 44%), the most common creator location was the United States. When mentioned (27/90; 30%), the most common age was 20-29 years. Conclusions: The dedication to building community support by vlog creators, and the personal nature of their storytelling, may make 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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 | 0,001 |
| 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 ».