Gender Differences in Reasons for Using Electronic Cigarettes and Product Characteristics: Findings From the 2018 ITC Four Country Smoking and Vaping Survey
Notice bibliographique
Résumé
INTRODUCTION: Little is known about why males are more likely to use electronic cigarettes (ECs) compared with females. This study examined gender differences in reasons for vaping and characteristics of EC used (device type, device capacity, e-liquid nicotine strength, and flavor). METHODS: Data were obtained from 3938 current (≥18 years) at-least-weekly EC users who participated in Wave 2 (2018) ITC Four Country Smoking and Vaping Survey in Canada, the United States, England, and Australia. RESULTS: Of the sample, 54% were male. The most commonly cited reasons for vaping in females were "less harmful to others" (85.8%) and in males were "less harmful than cigarettes" (85.5%), with females being more likely to cite "less harmful to others" (adjusted odds ratio [aOR] = 1.64, p = .001) and "help cut down on cigarettes" (aOR = 1.60, p = .001) than males. Significant gender differences were found in EC device type used (χ 2 = 35.05, p = .043). Females were less likely to report using e-liquids containing >20 mg/mL of nicotine, and tank devices with >2 mL capacity (aOR = 0.41, p < .001 and aOR = 0.65, p = .026, respectively) than males. There was no significant gender difference in use of flavored e-liquids, with fruit being the most common flavor for both males (54.5%) and females (50.2%). CONCLUSION: There were some gender differences in reasons for vaping and characteristics of the product used. Monitoring of gender differences in patterns of EC use would be useful to inform outreach activities and interventions for EC use. IMPLICATIONS: Our findings provide some evidence of gender differences in reasons for vaping and characteristics of EC used. The most common reason for vaping reported by females was "less harmful to others," which may reflect greater concern by female vapers about the adverse effects of secondhand smoke compared with male vapers. Gender differences might be considered when designing gender-sensitive smoking cessation policies. Regarding characteristics of EC products used, we found gender differences in preferences for e-liquid nicotine strength and device capacity. Further studies should examine whether the observed gender differences in EC use reasons and product characteristics are predictive of smoking cessation. Furthermore, studies monitoring gender-based marketing of ECs may be considered.
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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 | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 | 0,001 |
| 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 ».