Medical and Nonmedical Use of Prescription Opioids Among High School Seniors in the United States
Bibliographic record
Abstract
OBJECTIVES To determine the prevalence of medical and nonmedical use of prescription opioids among high school seniors in the United States and to assess substance use behaviors based on medical and nonmedical use of prescription opioids. DESIGN Nationally representative samples of high school seniors (modal age 18 years) were surveyed during the spring of their senior year via self-administered questionnaires. SETTING Data were collected in public and private high schools. PARTICIPANTS The sample consisted of 7374 students from 3 independent cohorts (2007, 2008, and 2009). OUTCOME MEASURES Self-reports of medical and nonmedical use of prescription opioids and other substance use. RESULTS An estimated 17.6% of high school seniors reported lifetime medical use of prescription opioids, while 12.9% reported nonmedical use of prescription opioids. Sex differences in the medical and nonmedical use were minimal, while racial/ethnic differences were extensive. More than 37% of nonmedical users reported intranasal administration of prescription opioids. An estimated 80% of nonmedical users with an earlier history of medical use had obtained prescription opioids from a prescription they had previously. The odds of substance use behaviors were greater among individuals who reported any history of nonmedical use of prescription opioids relative to those who reported medical use only. CONCLUSIONS Nearly 1 in every 4 high school seniors in the United States has ever had some exposure to prescription opioids either medically or nonmedically. The quantity of prescription opioids and number of refills prescribed to adolescents should be carefully considered and closely monitored to reduce subsequent nonmedical use of leftover medication.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".