YouTube Videos Related to Skin Cancer: A Missed Opportunity for Cancer Prevention and Control
Bibliographic record
Abstract
BACKGROUND: Early detection and treatment influence the mortality risk of skin cancer. OBJECTIVE: The objective of this study was to analyze the content of the most viewed professional and consumer videos uploaded to YouTube related to skin cancer. METHODS: A total of 140 professional and consumer videos uploaded between 2007 and 2014 were identified and coded. Coding involved identifying and sorting followed by gathering descriptive information, including length of the video, number of views, and year uploaded. A dichotomous coding scheme (ie, yes or no) was used in coding specific aspects of video content, including provision of information, type of skin cancer, age group, family history, risk reduction, risk factors, fear, and home remedies for skin cancer treatment. RESULTS: The majority of videos provided information related to screening. Many consumer videos conveyed information related to the use of a black salve as a home remedy for skin cancer, despite the fact that there is no evidence that it is an effective treatment. CONCLUSIONS: Research is needed to identify characteristics of videos that are most likely to be viewed to inform the development of credible communications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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".