Gender Differences in Reasons for Using Electronic Cigarettes and Product Characteristics: Findings From the 2018 ITC Four Country Smoking and Vaping Survey
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".