Bidi and Hookah Use Among Canadian Youth: Findings From the 2010 Canadian Youth Smoking Survey
Bibliographic record
Abstract
INTRODUCTION: Although cigarette use among Canadian youth has decreased significantly in recent years, alternative forms of tobacco use are becoming increasingly popular. Surveillance of youth tobacco use can help inform prevention programs by monitoring trends in risk behaviors. We examined the prevalence of bidi and hookah use and factors associated with their use among Canadian youth by using data from the 2010-2011 Youth Smoking Survey (YSS). METHODS: We analyzed YSS data from 28,416 students (2006-2007) and 31,396 students (2010-2011) in grades 9 through 12 to examine prevalence of bidi and hookah use. We conducted multivariate logistic regression analyses of 2010-2011 YSS data to examine factors associated with bidi and hookah use. RESULTS: From 2006 through 2010, prevalence of hookah use among Canadian youth increased by 6% (P = .02). Marijuana use emerged as a consistent predictor of bidi and hookah use. Males, youth of black, Latin, or other descent, and youth of Asian descent were more likely to use bidis (odds ratio [OR], 1.5; OR, 15.6; OR, 14.9) or hookah (OR, 1.3; OR, 2.4; OR, 1.5). Current cigarette smokers were more likely than nonsmokers to be current users of bidis (OR, 6.7) and hookahs (OR, 3.0), and occasional and frequent alcohol drinkers were also more likely than nondrinkers to be current hookah users (OR, 2.8; OR, 3.6). CONCLUSION: Although bidi use has not changed significantly among Canadian youth, the increase in hookah use warrants attention. Understanding the factors associated with use of bidis and hookahs can inform the development of tobacco use prevention programs to address emerging at-risk youth populations.
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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 | 0.007 |
| Science and technology studies | 0.003 | 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".