Prescription Drug Abuse and Diversion Among Adolescents in a Southeast Michigan School District
Bibliographic record
Abstract
OBJECTIVES: To determine the prevalence of medical use of 4 classes of prescription medications relative to nonmedical use (illicit use), to examine the relative rates among the 4 drug classes, and to assess whether gender differences exist in the trading, selling, loaning, or giving away of medications. DESIGN: A Web-based survey was administered to 7th- to 12th-grade students residing in 1 ethnically diverse school district; a 68% response rate was achieved. SETTING: During a 3-week period in May 2005, teachers brought students to their schools' computing center where students took the survey using a unique personal identification number to sign on to the survey. PARTICIPANTS: There were 1086 secondary students, including 586 girls, 498 boys, 484 black students, and 565 white students. MAIN OUTCOME MEASURES: Students were asked about their medical and nonmedical use of sleeping, sedative or anxiety, stimulant, and pain medications. Diversion of prescription medication was assessed by determining who asked the student to divert his or her prescription and who received it. RESULTS: Thirty-six percent of students reported having a recent prescription for 1 of the 4 drug classes. A higher percentage of girls reported giving away their medications than boys (27.5% vs 17.4%, respectively; chi(2)(1) = 6.7; P = .01); girls were significantly more likely than boys to divert to female friends (64.0% vs 21.2%, respectively; chi(2)(1) = 17.5; P<.001) whereas boys were more likely than girls to divert to male friends (45.5% vs 25.6%, respectively; chi(2)(1) = 4.4; P = .04). Ten percent diverted their drugs to parents. CONCLUSION: Physicians should discuss the proper use of prescription medications with their patients and their patients' families.
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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 | 0.000 | 0.001 |
| 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".