Non-Medical Use of Prescription Analgesics: A Three-Year National Longitudinal Study
Bibliographic record
Abstract
This secondary analysis of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) data examined the non-medical use of prescription analgesics and determined its relationship to continued non-medical use and substance use disorders 3 years later. Prospective data were collected using the Alcohol Use Disorders and Associated Disabilities Interview Schedule: DSM-IV Version (AUDADIS-DSM-IV). A nationally representative sample (n = 34,653) of U.S. adults 18 years or older were interviewed at Wave 1 (2001-2002) and re-interviewed at Wave 2 (2004-2005). Multivariate logistic regression analyses indicated younger age (18 to 24 years) and non-medical use at Wave 1 was associated with higher odds of a general substance or opioid use disorder at Wave 2 (adjusted odds ratio = 3.42, 95% confidence interval = 1.45, 8.07); however, most respondents who engaged in non-medical use will cease using 3 years later although non-medical use is associated with higher prevalence of a future substance use disorder.
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 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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".