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 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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".