ALCOHOL, MARIJUANA AND TOBACCO USE PATTERNS AMONG CANADIAN YOUTH
Bibliographic record
Abstract
Objectives Despite the health risks and public harm associated with heavy drinking, tobacco and marijuana use, the abuse of these substances remains common among youth in Canada. In this paper, for these three substances we examine (a) changes in their use over time, (b) age of onset, (c) co-morbid use, and (d) sociodemographic factors associated with their use in a nationally representative sample of Canadian youth. Methods Data were collected from students in grades 7 to 9 as part of the Canadian Youth Smoking Survey (n = 19,018 in 2002; n = 29,243 in 2004; n = 71,003 in 2006). Results Alcohol is the most prevalent substance used by youth. Co-morbid substance use was common, and it was rare to find youth who had used marijuana or tobacco without also having tried alcohol. There were high rates of underage youth trying alcohol, as well as a high prevalence of binge drinking and co-morbid use with tobacco and/or marijuana. Onset of alcohol and tobacco occurred at younger ages than marijuana. School performance and disposable income were associated with increased risk of these three behaviours. Conclusions The data suggest that alcohol, tobacco and marijuana are used by a substantial number of youth in Canada, despite age and legal regulations prohibiting their use. Considering the inter-relationship between alcohol and tobacco onset, future research should examine the potential impact that the increasing popularity of alcohol use may have on future youth smoking rates.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| 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".