Learning from their experiences: Strategies used by youth and young adult ex-vapers
Bibliographic record
Abstract
INTRODUCTION: The prevalence of vaping among youth and young adults (YYAs; 16-18 and 19-24 years old, respectively) is moderate worldwide. Existing vaping cessation evidence lacks input from ex-vapers with a history of regular use and substantial maintenance periods. This study noted cessation strategies, relapse triggers, and recommendations for quitting identified by ex-vapers and assessed differences in outcomes across age and gender groups. METHODS: We recruited ex-vapers (N = 290; mean use = 6.5 days/week, SD = 1.05) with a minimum maintenance period of 30 days and a history of three months of consecutive use of nicotine-based devices from Nova Scotia, Canada. The ex-vapers responded to open-ended questions regarding vaping cessation strategies, triggers, and recommendations for quit strategies in an online survey. We coded responses to each topic (e.g., triggers) and grouped them into categories (e.g., social influences). We used chi-square tests and Bonferroni correction tests to determine group differences by topic and within each category. RESULTS: YYA ex-vapers identified cold turkey (28.9 %), self-restriction (27.5 %), and alternative coping mechanisms (19.0 %) as the most common cessation strategies; social influences (35.5 %,), mental state (18.3 %), and substance use (15.7 %) as the top triggers; and support systems (29.5 %), apps (17.3 %), and education (11.8 %) as the most useful recommendations for others. A higher proportion of female youth (51.3 %) identified social influences as a relapse trigger than male YAs (21.2 %) and female YAs (30.3 %). Further, male YAs (36.5 %) reported higher proportions of substance use as a relapse trigger than male youth (3.0 %) and female youth (2.6 %). Female youth (23.7 %) and YAs (22.6 %) recommended apps as a useful cessation strategy more often than male YAs (3.8 %). CONCLUSIONS: Input from ex-vapers can help to inform cessation practices, and gender and age differences shed light onto the need to tailor treatments, such as using social-centric behavioral therapy, for female youth and adopting a polysubstance substance use treatment approach for YAs.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".