Natural course of smoking cessation and predictors of quitting in young adulthood
Bibliographic record
Abstract
Abstract Background Young adulthood is characterized by major life changes that can promote or inhibit cigarette smoking. Gaps in knowledge on the natural course of, and factors associated with quitting impede progress in designing effective cessation interventions targeting this age group. We describe the natural course of cessation among students who smoked cigarettes in their last year of high school and we identify predictors of quit status at age 30. Methods Data were drawn from an ongoing investigation of 1294 grade 7 students age 12-13 recruited in 1999-2000 in 10 high schools in Montreal, Canada. Using data collected at ages 17, 20, 24 and 30, grade 11 cigarette smokers were categorized as quitters or current smokers at age 30. Results Among 245 grade 11 smokers, 48% (55% of females; 33% of males) had quit by age 30. Among 106 quitters with complete data, 27% were consistently abstinent during young adulthood, 33% were intermittently abstinent and 40% were abstinent at age 30 only. Smokers at age 30 had higher impulsivity scores in grade 11 than quitters and were more likely to report nicotine dependence symptoms and have family members who smoked. Quitters rated self-control and social influences as more important reasons to quit than smokers. Although 85% of smokers wanted to quit, only 45% had used a cessation strategy in the past year. Conclusions Adolescents likely to be smokers at age 30 are identifiable in high school. Cessation assistance must be offered early in high school to provide adolescents with opportunities to quit before smoking becomes intractable. Key messages Among grade 11 smokers, 27% were consistently abstinent in young adulthood, 33% were intermittently abstinent, and 40% were abstinent at age 30 only. Cessation programs are needed early in high school to prevent intractable smoking.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".