Descriptive and content analysis of breast cancer vlogs on YouTube.
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".