Patterns of e-cigarette use and interest in cessation among current users in Ontario: An online cross-sectional study
Bibliographic record
Abstract
Use of electronic cigarettes, commonly referred to as vaping, has increased substantially in recent years. Our present study aimed to explore vaping patterns and factors that may associated with intention to quit among participants in Ontario, Canada. In this original survey, a total of 757 participants over the age of 15 and currently using e-cigarettes were invited to complete an online survey via social media advertisement about vaping. We included participants who used any type of vaping device. Binary logistic regression modelling was applied to assess patterns of vaping with intention to quit. Of the 757 participants, 44.2% were under 25 years old, and 57.2% were male. Notably, 29.1% of respondents tried vaping before 18 years of age and 34.6% vaped regularly at 18 or younger. Of the sample, 81.4% reported vaping daily and 63.1% vaped more than 10 times a day. Almost half (49.6%) reported consuming more than 10 mg of nicotine per day, 23.8% were consuming more than 20mg of nicotine per day, and 6.2% were consuming more than 40mg of nicotine per day. Among respondents, 41.3% were interested in quitting. Regression analysis revealed that younger individuals, those consuming nicotine, and those with higher levels of nicotine dependence were more inclined to quit. Additionally, those who perceive vaping to be more harmful and more addictive showed a greater intention to quit. Conversely, individuals who perceive vaping to be less harmful or addictive than smoking were significantly less likely to quit. Our results also indicated that those who were vaping to quit smoking (42.7%), vaping socially (40.4%) and vaping to manage a mental health issue (37.2%) had the highest intention to quit. Those who vaped as it was healthier than smoking (32.3%), due to the flavours (33.6%), and financial reasons (34.5%) were least likely to want to quit. This study confirms that a large portion of e-cigarette users are young, daily users with high nicotine use and dependence. Given the high interest to quit vaping, there is a need for continued education, health promotion, prevention, and targeted treatment programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".