Harm perceptions across vaping product features: An on‐line cross‐sectional survey of adults who smoke and/or vape in the United Kingdom
Notice bibliographique
Résumé
BACKGROUND AND AIMS: Vaping products are diverse with a wide variety of features, and popular products change rapidly. This study examined the features and types of vaping products that people who smoke and/or vape perceive contribute to the health harms of vaping. DESIGN, SETTING AND PARTICIPANTS: This was a cross-sectional survey co-designed with adults who smoked/vaped and pre-registered. An on-line survey (November 2022) was used of a convenience sample of adults in the United Kingdom who smoked and/or vaped (n = 494). MEASUREMENTS: As primary outcomes, respondents were asked to select any of 15 vaping product features they perceived might have any effect on the health harms of vaping (for each: selected, not selected). Independent variables were smoking/vaping status (smoke and vape; vape, formerly smoked; vape, never regularly smoked; smoke, do not currently vape); relative vaping harm perceptions [less harmful than smoking (accurate), equally/more harmful than smoking or do not know/refused (other)]. Binary logistic regressions were used to compare outcomes by current vaping/smoking status and relative harm perceptions, adjusting for age and sex. FINDINGS: Most people (54.7%) selected between one and three features. The most frequently selected were nicotine concentration (62.2%) and amount of e-liquid consumed (59.1%), followed by nicotine type (e.g. salt or freebase; 33.0%), source/purchase location (25.3%), flavours (24.7%), temperature to heat e-liquid (21.1%), heat produced by device (20.9%), e-liquid brand (20.9%), amount of emissions (18.6%), device type (e.g. disposable, pod, tank; 17.2%), material of tank (17.0%), power/wattage (13.0%), device brand (8.1%), device size (4.1%) and device weight (2.4%). Higher nicotine concentrations, more e-liquid and salt (versus freebase) nicotine were perceived to confer greater harms. Disposables were perceived as slightly more harmful than reusable devices. There were few differences by current vaping/smoking status and between those with accurate (versus other) harm perceptions of vaping relative to smoking (P > 0.05 for most contrasts, adjusting for age and sex). CONCLUSIONS: Certain features and types of vaping products [higher nicotine concentrations, more e-liquid consumed and salt (versus freebase) nicotine] were perceived to confer greater health harms among a sample of UK adults who smoked and/or vaped. Findings are consistent with pervasive misperceptions that nicotine is a major cause of harm, although e-liquid volume is likely to contribute to harms.
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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 | 0,001 | 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 | 0,000 |
| 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 ».