Clinical Trials in Social Media: Content Analysis of YouTube Videos in Arabic Language (Preprint)
Bibliographic record
Abstract
Background: Clinical trials are fundamental to the advancement of cancer care; nonetheless, recruitment remains inadequate.A frequently reported barrier to participation is the lack of awareness and knowledge about clinical trials.Research showed that the internet is being used now as a primary source for information on clinical trials. Objective:We aim in this study to review available videos uploaded on the YouTube, one of the most-visited websites worldwide, about clinical trials in Arabic language and evaluate the comprehensiveness of its content.Methods: YouTube videos were searched using the keywords "clinical trials" and "clinical studies" in Arabic language.Only videos targeting the public were included in the study.Videos targeting medical students/ healthcare professionals, discussing country-specific laws, longer than 30 minutes, or found irrelevant upon viewing were excluded from the analysis.Results: Seven videos about clinical trials were included in the final analysis.Only 1 video was related specifically to cancer clinical trials (14.3%).The mean length of videos was 6:43 minutes (range: 1:37-16:53 minutes) with a total number of views of 11,207 (mean 1,601.0,SD ±2,054.6).More than half of the videos (n=4, 57.1%) were created by TV/ Internet Channels and were neutral in tone.Most common presentation style and country of origin were vlog and Saudi Arabia, receptivity (n=3, 42.9% for both).For video-related content, the most frequently mentioned variables were the purpose of clinical trials to test new drug/devices in humans (n=6, 85.7%), animal testing conducted before clinical trials, clinical trials are conducted on several phases, phase I clinical trials and the aim of phase I studies is to assess safety (n=5, 71.5%; for all items).These were followed by mention of phase II and phase III in general (n=4, 57.1%; for both items). Conclusions:This study identified a clear scarcity of YouTube videos about clinical trials in the Arabic language, as well as, potential gaps in the comprehensiveness of the content presented.Viewers' engagement presented in number of views, likes and dislikes seems to be very low.Stakeholders need to pay more attention to the use of social media in prompting clinical trials and providing comprehensive and reliable sources of information to the public.
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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 | 0.003 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".