Use of Nicotine Replacement Therapy Among Canadian Youth: Data From the 2006–2007 National Youth Smoking Survey
Bibliographic record
Abstract
INTRODUCTION: In Canada, nicotine replacement therapy (NRT) is a best practice for adult smoking cessation, but it is not recommended for use among youth smokers. The purpose of this study was to determine the prevalence of NRT use among youth smokers in Canada and examine factors associated with its use. METHODS: Data from 41,886, Grade 9-12 students who participated in the 2006-2007 Youth Smoking Survey were used to determine prevalence of NRT use. Logistic regression models were conducted to examine the association between NRT use by smoking status, demographic characteristics, and exposure to tobacco control programs. RESULTS: In 2006-2007, 20.4% of current and former youth smokers in Canada had ever used NRT and 7.4% were currently using NRT. Among ever and current NRT users, 17.7% and 23.7%, respectively, had never tried to quit smoking. Odds of NRT use were highest among current smokers, older youths, boys, youths who had made multiple quit attempts, and youths with no disposable income. Participation in cessation counseling was significantly associated with increased NRT use, whereas attending antismoking classes in school was inversely associated with using NRT. CONCLUSIONS: A substantial number of Canadian youth use NRT, despite restrictions on its sale to this population. This study identifies characteristics associated with youths using NRT. Research is needed to elucidate mechanisms by which characteristics identified in this study affect NRT use. For example, it may be important to understand whether attending smoking cessation counseling induces NRT use in youths or vice versa.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| 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".