E-Vaping Patterns of Use, Information Needs and Risk Perception in the Post Pandemic Era: An International Survey and Social Media Listening Study
Notice bibliographique
Résumé
Introduction The rapid proliferation of Novel Psychoactive Substances (NPSs) in e-cigarette products presents an urgent public health concern. These include herbal compounds, synthetic cannabinoids, cathinones, and other potent analogues, often used by young adults. Their unregulated presence in vape liquids raises significant addiction and intoxication risks. This study aimed to elucidate current trends in NPS vaping and user perceptions to inform clinical and regulatory interventions. Methods This study employed a mixed-methods design that combined an international survey with social media listening. The survey was in English and Greek, distributed between April and December 2024 via online forums (e.g., Bluelight), social media, and university mailing lists. Quantitative data were analysed using SPSS. Simultaneously, a netnographic study analyzed 11,721 social media comments (Reddit, YouTube) to extract motivations and perceptions. Data scraping tools Apify and ExportComments were employed, followed by thematic categorization and sentiment analysis. Results The survey received 1,045 responses where 210 respondents (20%) were vapers {49% males; 40% aged 25–39; (66% heterosexuals, 21% bisexuals); (47% from Greece, 22% from the UK, 18% from the US and Canada)}. The majority 52% were either high school or college leavers. 86% trusted online resources for information regarding vaping and only 32% referred to healthcare professionals. 81% stated that reliable online resources would work best as the main source of information. 62% of participants were tobacco smokers, and only 21% were ex-smokers. A total of 46% started e-vaping at 25 years or above; 60% used vapes daily. 72% had frequent or occasional cravings to vape; 37% tried to stop unsuccessfully. Meanwhile 31% complained about adverse events after vaping such as coughing, weakness, dizziness, sore throat, chest pain, palpitations, anxiety, COPD. Natural novel psychoactive substances, synthetic cannabinoids and flavorings as well as nicotine were mainly referred to as preferred in vaping liquids. 55% of participants stated that vaping poses medium risk. 32% of the vapers were also users of prescribed medications such as codeine, oxycodone, and 40% also users of NPSs (mainly herbals and benzodiazepines) combined with vaping. Netnographic findings revealed that the primary motivations for vaping were smoking cessation (63%), perception that vaping was less harmful (15%), and sensory appeal (8%). Youth often cited stress relief, peer influence and social identity as vaping drivers. Posts also highlighted widespread unawareness about NPS presence in vape products, reflecting a dangerous information gap. Conclusions Vapes are increasingly exploited as vehicles for a range of illicit drugs and NPSs, particularly among youth, with high addiction and toxicity risks. Real-time monitoring, better education strategies, and targeted policies are urgently needed. Understanding psychological motivators, such as stress coping or social appeal, is crucial to inform prevention strategies and regulatory responses.
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 | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 ».