Online Information About Side Effects and Safety Concerns of Semaglutide: Mixed Methods Study of YouTube Videos
Bibliographic record
Abstract
BACKGROUND: Social media has been extensively used by the public to seek information and share views on health issues. Recently, the proper and off-label use of semaglutide drugs for weight loss has attracted huge media attention and led to temporary supply shortages. OBJECTIVE: The aim of this study was to perform a content analysis on English YouTube (Google) videos related to semaglutide. METHODS: YouTube was searched with the words semaglutide, Ozempic, Wegovy, and Rybelsus. The first 30 full-length videos (videos without a time limit) and 30 shorts (videos that are no longer than 1 minute) resulting from each search word were recorded. After discounting duplicates resulting from multiple searches, a total of 96 full-length videos and 93 shorts were analyzed. Video content was evaluated by 3 tools, that is, a custom checklist, a Global Quality Score (GQS), and Modified DISCERN. Readability and sentiment of the transcripts were also assessed. RESULTS: There was no significant difference in the mean number of views between full-length videos and shorts (mean 288,563.1, SD 513,598.3 vs mean 188,465.2, SD 780,376.2, P=.30). The former had better content quality in terms of GQS, Modified DISCERN, and the number of mentioned points from the custom checklist (all P<.001). The transcript readability of both types of videos was at a fairly easy level and mainly had a neutral tone. Full-length videos from health sources had a higher content quality in terms of GQS and Modified DISCERN (both P<.001) than their counterparts. CONCLUSIONS: The analyzed videos lacked coverage of several important aspects, including the lack of long-term data, the persistence of side effects due to the long half-life of semaglutide, and the risk of counterfeit drugs. It is crucial for the public to be aware that videos cannot replace consultations with physicians.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".