ORIGINAL PAPER Alcohol, marijuana, and tobacco use patterns among youth in Canada
Bibliographic record
Abstract
Abstract The authors characterized changes in the prevalence of alcohol, tobacco, and marijuana use over time, and examined age of onset, co-morbid use and sociodemographic factors associated with ever using alco-hol, tobacco, or marijuana in a nationally representative sample of Canadian youth. Data were collected from stu-dents in grades 7–9 as part of the Canadian Youth Smoking Survey (n = 19,018 in 2002; n = 29,243 in 2004). Descriptive analyses examined age of onset, co-morbid substance use and changes over time. Logistic regression models were used to examine factors associated with ever trying alcohol, tobacco, or marijuana with the 2004 data. Alcohol was the most prevalent substance used by youth and it was also the only substance which exhibited increased rates of use between 2002 and 2004. Co-morbid substance use was common, and it was rare to find youth who had used marijuana or tobacco without also having tried alcohol. As expected, youth who had poorer school performance were more likely to drink and smoke marijuana or tobacco, as were youth with more disposable income. Such timely and relevant data are important for guiding future policy, programing, and surveillance activities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".