Misinformation About Medical Cannabis in YouTube Videos: Systematic Review
Bibliographic record
Abstract
Background: YouTube has become a major source of health information, with 2.5 billion monthly users. Despite efforts taken to promote reliable sources, misinformation remains prevalent, particularly regarding medical cannabis. Objective: This study aims to evaluate the quality and reliability of medical cannabis information on YouTube and to examine the relationship between video popularity and content quality. Methods: A systematic review of YouTube videos on medical cannabis was conducted. Search terms were selected based on Google Trends, and 800 videos were retrieved on July 8, 2024. After applying exclusion criteria, 516 videos were analyzed. Videos were categorized by content creators: (1) nonmedical educational channels, (2) medical education channels, and (3) independent users. Two independent reviewers (SK and SE) assessed content quality using the DISCERN grade and the Health on the Net (HON) code. Statistical analysis included one-way ANOVA and Pearson correlation coefficient. Results: Of the 516 videos analyzed, 48.5% (n=251) were from the United States, and 17.2% (n=89) from the United Kingdom. Only 12.2% (n=63) were produced by medical education channels, while 84.3% (n=435) were by independent users. The total views reached 119 million, with nonmedical educational channels having the highest median views with 274,957 (IQR 2161-546,887) and medical education channels having the lowest median views at 5721 (IQR 2263-20,792.50). The mean DISCERN and HON code scores for all videos were 34.63 (SD 9.49) and 3.93 (SD 1.20), respectively. Nonmedical educational creators had the highest DISCERN score (mean 47.78, SD 10.40) and independent users had the lowest score (mean 33.5, SD 8.50; P<.001). Similarly, nonmedical educational creators had the highest HON code score (mean 5.33, SD 1.22), while independent users had the lowest (mean 3.78, SD 1.10; P=.007). Weak positive correlations were found between video views and DISCERN scores (r=0.34, P<.001) and likes and DISCERN scores (r=0.30, P<.001). Conclusions: YouTube is a key source of information on medical cannabis, but the credibility of videos varies widely. Independent users attract the highest viewers but have reduced reliability according to the DISCERN and HON scores. Educational channels, despite increased reliability received the least engagement. The weak correlation between views and content quality emphasizes the need for content moderation to ensure that the most reliable and accurate information on health issues is widely disseminated. Future research should identify strategies to promote verified sources of information and limit misinformation.
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 | 0.010 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".