Predictors of the Occurrence of Smoking Discontinuation in Novice Adolescent Smokers
Bibliographic record
Abstract
BACKGROUND: While long-term cessation is an outcome of interest in adult smokers, little is known about discontinuing smoking in adolescent smokers. The objective was to identify the predictors of the occurrence of smoking discontinuation in novice smokers. METHODS: Data were available for 620 adolescent smokers participating in a longitudinal study on the natural course of nicotine dependence. Data on smoking discontinuation (i.e., stopping smoking for ≥12 consecutive months) were collected in 20 cycles over five years from grade 7 to 11 (1999-2005). Data on 37 potential predictors representing a wide range of demographic, psychosocial, health, lifestyle, smoking-related, and context-related characteristics were collected once, 2 to 3 times, or 20 times. Pooled logistic regression was used to test the association between each potential predictor and smoking discontinuation, adjusting for potential confounders. RESULTS: Forty percent of 620 participants discontinued smoking during follow up. Male sex [OR (95% confidence interval), 1.8 (1.3-2.4)], age [1.3 (1.1-1.5)], cigarette package warnings [0.6 (0.5-0.9)], team sports participation [1.4 (1.1-1.9)], family stress [0.7 (0.6-1.0)], worrying about weight [0.6 (0.5-0.9)], overweight [0.7 (0.5-1.0)], illicit drugs use [0.5 (0.4-0.7)], tolerance [0.6 (0.4-1.0)], and other nicotine dependence symptoms [1.0 (0.9-1.0)] were statistically significantly associated with smoking discontinuation. CONCLUSIONS: Both individual and context-level factors were associated with smoking discontinuation. IMPACT: Programs and policy targeting novice adolescent smokers may be more effective if factors associated with long-term smoking discontinuation are taken into consideration. In particular, young smokers may need help with dependence symptoms, body weight issues, family functioning, and polysubstance use. Cigarette package warnings may be effective in helping adolescents discontinue smoking. Cancer Epidemiol Biomarkers Prev; 23(6); 1090-101. ©2014 AACR.
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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 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".