Information About Sunscreen on YouTube and Considerations for Sun Safety Promotion: Content Analysis
Bibliographic record
Abstract
Background Sunscreen use is a popular sun protection method; however, application of sunscreen rarely meets the standards recommended for effectiveness. Access to information about how to effectively use sunscreen may play a role in proper sunscreen application. The internet is a common health information source; however, the quality of sunscreen-related content varies. Objective The objective of this study was to examine information about sunscreen in YouTube videos by video source. Methods In November 2017, the authors identified the 20 most popular YouTube videos (sorted by view count and relevance) for each of these 5 search terms: sunscreen cancer, sunscreen health, sunscreen information, sunscreen ingredients, and sunscreen natural. The inclusion criteria were English language and view count >1000 (N=111 unique videos). We double-coded videos for standard recommendations for sunscreen use (eg, apply 20 minutes before sun exposure), use of outdated terminology, and recommendation of complementary sun safety strategies. Results The view counts ranged from 1100 to 671,142 (median 17,774, SD 109,651) and the average daily views ranged from 1 to 1448 (median 23, SD 234). End users (46/111, 41.4%) and health care providers (24/111, 21.6%) were the most common sources, and none of the most popular videos were produced by federal agencies or cancer-related nongovernmental organizations. Health care provider videos included marginally more recommendations than end user videos (mean 1.46, SD 1.96 vs mean 1.05, SD 1.20), but few (19/111, 17.1%) mentioned reapplication. The videos were generally positive toward sunscreen (82/111, 73.9%); however, some videos were negative (29/111, 26.1%), with warnings about the health risks of chemical sunscreens and their ingredients. Do-it-yourself sunscreen tutorials represented 19/111 (17.1%) of the sample. Conclusions YouTube is a potential source for disseminating sun safety messages; however, the quality of its sunscreen content varies. Most of the videos in our study failed to include important sunscreen use recommendations. Clinicians should be prepared to address the information needs of patients by discussing effective, evidence-based sunscreen application and recommending a combined sun safety approach.
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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".