Evaluating Online Cannabis Health Information for Thai Breast Cancer Survivors Using the Quality Evaluation Scoring Tool (QUEST): Mixed Method Study
Notice bibliographique
Résumé
Background: Following medical cannabis legalization in Thailand in 2019, more people are seeking medical cannabis-related information, including women living with breast cancer. The extent to which they access cannabis-related information from internet sources and social media platforms and the quality of such content are relatively unknown and need further evaluation. Objective: This study aims to analyze the factors determining cannabis-related content quality for breast cancer care from internet sources and on social media platforms and examine the characteristics of such content accessed and consumed by Thai breast cancer survivors. Methods: A mixed methods study was conducted between January 2021 and May 2022, involving a breast cancer survivor support group. The group identified medical cannabis-related content from frequently accessed internet sources and social media platforms. The contents were categorized based on content creators, platforms, content category, and upload dates. Four researchers used the Quality Evaluation Scoring Tool (QUEST) to assess content quality, with scores ranging from 0 to 28. Contents were expert-rated as either high or poor. The QUEST interobserver reliability was analyzed. Receiver-operating characteristic curve analysis with the Youden index was used to determine the QUEST score cut-off point. Statistical significance was set at P<.05. Fairclough Critical Discourse Analysis was undertaken to examine the underlying discourses around poor-quality content. Results: Sixty-two Thai-language cannabis-related items were evaluated. The content sources were categorized as follows: news channels (21/62, 34%), government sources (16/62, 26%), health care providers (12/62, 19%), and alternative medicine providers (12/62, 19%). Most of the contents (30/62, 48%) were uploaded to YouTube, whereas 31% (19/62) appeared on websites and Facebook. Forty of 62 content items (64%) were news-related and generic cannabis advertisements while 8 of 62 (13%) content items had no identifiable date. The interobserver QUEST score correlation was 0.86 (P<.001). The mean QUEST score was 12.1 (SD 7.6). Contents were considered "high" when the expert rating was >3. With a QUEST score of 15 as the threshold, the sensitivity and specificity for differentiating between high and poor content quality were 81% and 98%, respectively. Content creation was the only significant factor between high- and poor-quality content. Poor-quality contents were primarily created by alternative medicine providers and news channels. Two discourses were identified: advocacy for cannabis use normalization and cannabis romanticization as a panacea. These discourses overly normalize and romanticize the use of cannabis, focusing on indications and instructions for cannabis use, and medical cannabis promotion, while neglecting discussions on cannabis contraindications and potential side effects. Conclusions: The varying quality of medical cannabis-related information on internet sources and social media platforms accessed and shared by Thai breast cancer survivors is an issue of concern. Given that content creators are the sole predictive factors of high content quality, future studies should examine a wider range of cannabis-related sources accessible to both the public and patients to gain a more comprehensive understanding of the issue.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».