No open-access location is known for this record yet.
No open-access location is known for this record yet.
PURPOSE: Online videos accessed via YouTube are a popular method to provide health education. Videos need to be critically evaluated for educational qualities as the information could influence health outcomes. The present study aimed to evaluate the understandability, actionability and reliability of videos available on YouTube regarding brain, head, and neck cancer information. METHODS: A scoping review was conducted with a specific search strategy and inclusion/exclusion criteria based on previous studies. For each video, video characteristics and user engagement activities were recorded. Videos were evaluated using the PEMAT-A/V and modified DISCERN criteria. Spearman's rank correlation, Kruskal-Wallis test and Mann-Whitney U test were used for analysis. RESULTS: Out of 200 retrieved videos, 37 were included and analysed. The median length of the video was 3 min and 33 s. The majority of videos were published by health institutional and private channels (43.2%, n = 16). Health institutional channels received the highest actionability (Md = 37.5, p = 0.049), while private channels resulted in lower views/day (Md = 0.46, p = 0.001) and likes/day (Md = 0.01, p = 0.002). Animated and narrated videos acquired the highest understandability score (Md = 92.31, p < 0.001). Videos with professional transcripts reported higher actionability (Md = 62.5, p = 0.004), reliability (Md = 3.33, p = 0.028), views/day (Md = 29.31, p = 0.026), and likes/day (Md = 0.272, p = 0.023). CONCLUSION: YouTube videos pertaining to brain and head and neck cancer have low understandability, low actionability and moderate reliability. It is beneficial to have a stronger representation of trustworthy and credible organisations for sharing essential health information via YouTube. Including animations and professional video transcripts may improve their overall quality and consumer engagement.
Loading…
Stored only when both papers are in the corpus — 15 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.