Alcohol and Illegal Drug Use Behaviors and Prescription Opioids Use: How Do Nonmedical and Medical Users Compare, and Does Motive to Use Really Matter?
Bibliographic record
Abstract
BACKGROUND/AIMS: This study compares illegal drug and alcohol use behaviors between medical and nonmedical users of prescription opioids (PO) and nonmedical users with distinct motives to use. METHOD: An ethically approved cross-sectional study (2010) was conducted on a representative sample of private university students (n = 570), using a self-filled anonymous questionnaire. RESULTS: About 25% reported using PO only medically and 15% nonmedically. The prevalence of alcohol and illegal drug use was consistently higher among nonmedical than medical PO users. Adjusting for age and gender, lifetime medical users of PO were more likely to use marijuana only (OR = 1.8, 95% CI: 1.1, 2.8), while nonmedical users were at higher odds of using marijuana, ecstasy, cocaine/crack, and alcohol problematically. Compared to nonusers, students who took PO nonmedically for nontherapeutic reasons were more likely to use various illegal drugs, but nonmedical users who took PO to relieve pain/help in sleep were only more likely to use marijuana (OR = 2.5, 95% CI: 1.1, 5.4) and alcohol (e.g. alcohol abuse; OR = 3.8, 95% CI: = 1.4, 10.1). CONCLUSION: Youth who use PO nonmedically to self-treat have a different alcohol and illegal drug-using profile than those who take it for nontherapeutic reasons.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".