A cross-sectional examination of medicinal substance abuse and use of nonmedicinal substances among Canadian youth: findings from the 2012-2013 Youth Smoking Survey
Bibliographic record
Abstract
BACKGROUND: Medicinal substance abuse is prevalent in Canada; however, little is known about patterns of abuse among young people. In this study, we sought to characterize the abuse of medicinal substances, such as prescription medications and selected over-the-counter substances, as well as that of licit and illicit nonmedicinal substances, using a nationally representative sample of young people. METHODS: Cross-sectional, nationally representative data for children in grades 7-12 were obtained from Health Canada's 2012-2013 Youth Smoking Survey (n = 38 667). Multinomial regression analyses were conducted to examine subgroup differences in medicinal substance abuse and comorbid abuse of both medicinal and nonmedicinal substances. RESULTS: About 5% of youth reported abusing medicinal substances in the previous year. Dextromethorphan, a substance found in many cough and cold syrups, was the most widely abused (2.9%), followed by pain medications (2.6%), sleeping medications (1.8%), stimulants (1.7%) and sedatives (1.0%). Abuse of nonmedicinal substances aside from tobacco and alcohol was reported by 21.3% of the population, and abuse of any substances was detected in 23.0% of the surveyed population. Girls at each grade level reported higher rates of abuse of medicinal substances than boys. Regional differences were seen with regard to the types of substances abused across Canada. INTERPRETATION: A substantial minority of Canadian youth report abusing medicinal substances, including over-the-counter medications (e.g., cough syrup) and prescriptions medications (e.g., pain medication). In contrast to nonmedicinal substances, girls were more likely than boys to report abuse of medicinal substances.
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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 | 0.002 | 0.005 |
| 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) | 0.001 | 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".