Misinformation About Medical Cannabis in YouTube Videos: Systematic Review
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,081 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,004 |
| Bibliométrie | 0,013 | 0,011 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».