Alcohol and Illegal Drug Use Behaviors and Prescription Opioids Use: How Do Nonmedical and Medical Users Compare, and Does Motive to Use Really Matter?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it