Breast Cancer Vlogs on YouTube: Descriptive and Content Analyses
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".