Water-Pipe Smoking Among North American Youths
Bibliographic record
Abstract
OBJECTIVES: The objectives of this analysis were to identify the sociodemographic characteristics of water-pipe users in a North American context and to describe concurrent psychoactive substance use. METHODS: Data on sociodemographic characteristics, water-pipe smoking, and use of other psychoactive substances were collected in 2007 through mailed self-report questionnaires completed by 871 young adults, 18 to 24 years of age, who were participating in the Nicotine Dependence in Teens Study, a longitudinal investigation of the natural history of nicotine dependence among adolescents in Montreal, Canada. Independent sociodemographic correlates of water-pipe use were identified in multivariate logistic regression analyses. RESULTS: Previous-year water-pipe use was reported by 23% of participants. Younger age, male gender, speaking English, not living with parents, and higher household income independently increased the odds of water-pipe use. Water-pipe use was markedly higher among participants who had smoked cigarettes, had used other tobacco products, had drunk alcohol, had engaged in binge drinking, had smoked marijuana, or had used other illicit drugs in the previous year. CONCLUSIONS: Water-pipe users may represent an advantaged group of young people with the leisure time, resources, and opportunity to use water-pipes. Evidence-based public health and policy interventions are required to equip the public to make informed decisions about water-pipe use.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".