P2-40 Predictors of smoking cessation in adolescent smokers: a systematic review of longitudinal studies
Bibliographic record
Abstract
Tobacco use causes more than 5 million deaths worldwide annually. In Canada (2009), prevalence of smoking was 13% among those 15–19 years and 23% among those 20–24 years. Many young smokers desire to quit, but have difficulty doing so. Empirical reviews have concluded that smoking cessation programs in youth have limited efficacy. In order to provide a solid knowledge base for tobacco interventions, determinants of self-initiated cessation in youth need to be understood. We systematically searched PUBMED and EMBASE for longitudinal studies on determinants of self-initiated smoking cessation in youth. N=3807 titles and N=787 abstracts were reviewed independently by two and three reviewers, respectively. Inclusion criteria were: published between January 1984 and August 2010, youth 10–28 years, and smoking cessation of ≥6 months. Seven articles were retained for in-depth analysis. 3 of 7 studies retained defined smoking cessation as abstinence of ≥6 months and four studies as 12 months. Seven factors emerged related to quitting: few friends who smoke, no intention to smoke, higher parental education, intact nuclear family, parental disapproval of smoking, good grades, good health, high cigarette resistance self-efficacy, and older age at first use. Additional factors are significant only in some studies or only assessed once. The longitudinal literature on predictors of youth cessation is not well developed. The most consistent predictors of self-initiated cessation include few friends smoking and no intention to smoke in the future. Tobacco interventions should target youth as well their friends as soon as possible after smoking onset given the difficulty in quitting.
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 | 0.012 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.012 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".