YouTube User Traffic to Paired Epilepsy Education Videos in English and Spanish: Comparative Study
Bibliographic record
Abstract
Background: Effectively managing epilepsy in children necessitates the active engagement of parents, a factor that is reliant on their understanding of this neurological disorder. Widely available, high-quality, patient-focused, bilingual videos describing topics important for managing epilepsy are limited. YouTube Analytics is a helpful resource for gaining insights into how users of differing backgrounds consume video content. Objective: This study analyzes traffic to paired educational videos of English and Spanish versions of the same content. By examining the use patterns and preferences of individuals seeking information in different languages, we gained valuable insights into how language influences the use of clinical content. Methods: Physician experts created epilepsy management videos for the REACT (Reaching Out for Epilepsy in Adolescents and Children Through Telemedicine) YouTube channel about 17 subjects, with an English and Spanish version of each. The Children's Mercy Kansas City neurology clinic incorporated these into the department's educational process. YouTube Analytics enabled analysis of traffic patterns and video characteristics between September 2, 2021, and August 31, 2023. Results: The Spanish group had higher engagement and click-through rates. The English versions of all videos had 141,605 total impressions, while impressions for the Spanish versions totaled 156,027. The Spanish videos had 11,339 total views, while the English videos had 3366. The views per month were higher for the Spanish videos (mean 472, SD 292) compared to the English set (mean 140, SD 91; P<.001). The two groups also differed in search behavior and external traffic sources, with WhatsApp driving more traffic to the Spanish videos than the English versions (94 views compared to 1). The frequency of search terms used varied by language. For example, "tonic clonic" was the most frequent term (n=372) resulting in views for English videos, while "tipos de convulsiones" (types of convulsions) was the most common expression (n=798) resulting in views for Spanish videos. We noted increased monthly views for all videos after adding tags on YouTube. Before tagging, the mean number of views per month for the English-language group was 61 (SD 28), which increased to 220 (SD 53) post tagging. A similar trend can be observed in the Spanish-language group as well. Before tagging, the mean number of monthly views was 201 (SD 71), which increased to 743 (SD 144) after tagging. Conclusions: This study showed high traffic for Spanish video content related to epilepsy in a set of paired English/Spanish videos. This highlights the importance of bilingual health content and optimizing video content based on viewer preferences and search behavior. Understanding audience engagement patterns through YouTube Analytics can further enhance the dissemination of clinical video content to users seeking content in their primary language, and tagging videos can have a substantial impact on views.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.004 | 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".