A survey of quit vaping strategies and relapse triggers for maintaining youth and young adult vaping abstinence in Canada
Bibliographic record
Abstract
OBJECTIVES: To examine whether various quit strategies and relapse triggers are associated with maintenance period in a sample of people who quit vaping. METHOD: = 772) completed an online survey on maintenance period, quit strategies, and relapse triggers. Logistic regression was employed to variables associated with maintenance period. RESULTS: People with past vaping history who quit unassisted or through eliminating social influences were more likely to achieve long-term maintenance. Those who quit through thinking about health improvements, distraction techniques, or self-restriction were less likely to achieve long-term maintenance. Other substance use or sensory vaping cues as relapse triggers were less likely to be experienced for those in long-term maintenance. Using very high concentrations of nicotine prior to quitting, and being unemployed were associated with lower likelihood for long-term maintenance. CONCLUSIONS: It is important to consider quit strategies, relapse triggers, and nicotine use prior to quitting in vaping cessation programing as they are related to maintenance period.
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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".