Nonmedical Use of Prescription Medication among Adolescents Using Drugs in Quebec
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence and factors associated with nonmedical use of prescription medication (NMUPM) among adolescents who use drugs (ages 12 to 17 years) in Quebec. METHOD: Secondary data analyses were carried out with data from a 6-month study, namely, the 2010-2011 Quebec Health Survey of High School Students-a large-scale survey that sought to gain a better understanding of the health and well-being of young Quebecers in high school. Bivariate and multivariate logistic regression analyses were conducted to study NMUPM among adolescents who use drugs, according to sociodemographic characteristics, peer characteristics, health indicators (anxiety, depression, or attention-deficit disorder [ADD] with or without hyperactivity), self-competency, family environment, and substance use (alcohol and drug use) factors. RESULTS: Among adolescents who had used drugs in the previous 12 months, 5.4% (95% CI 4.9% to 6.0%) reported NMUPM. Based on multivariate analyses, having an ADD (adjusted odds ratio [AOR] 1.47; 95% CI 1.13 to 1.91), anxiety disorder (AOR 2.14; 95% CI 1.57 to 2.92), low self-esteem (AOR 1.62; 95% CI 1.26 to 2.08), low self-control (AOR 1.95; 95% CI 1.55 to 2.45), low parental supervision (AOR 1.43; 95% CI 1.11 to 1.83), regular alcohol use (AOR 1.72; 95% CI 1.36 to 2.16), and polysubstance use (AOR 4.09; 95% CI 3.06 to 5.48) were associated with increased odds of reporting NMUPM. CONCLUSIONS: The observed prevalence of NMUPM was lower than expected. However, the associations noted with certain mental health disorders and regular or heavy use of other psychoactive substances are troubling. Clinical implications are discussed.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".