Adolescents' Motivations to Abuse Prescription Medications
Bibliographic record
Abstract
OBJECTIVES: Our goals were to (1) determine adolescents' motivations (reasons) for engaging in the nonmedical (illicit) use of 4 classes of prescription medications and (2) examine whether motivations were associated with a higher risk for substance abuse problems. RESPONDENTS: The 2005 sample (N = 1086) was derived from one ethnically diverse school district in southeastern Michigan and included 7th- through 12th-grade students. METHODS: Data were collected by using a self-administered, Web-based survey that included questions about drug use and the motivations to engage in nonmedical use of prescription medication. RESULTS: Twelve percent of the respondents had engaged in nonmedical use of opioid pain medications in the past year: 3% for sleeping, 2% as a sedative and/or for anxiety, and 2% as stimulants. The reasons for engaging in the nonmedical use of prescription medications varied by drug classification. For opioid analgesics, when the number of motives increased, so too did the likelihood of a positive Drug Abuse Screening Test score. For every additional motive endorsed, the Drug Abuse Screening Test increased by a factor of 1.8. Two groups of students were compared (at-risk versus self-treatment); those who endorsed multiple motivations for nonmedical use of opioids (at-risk group) were significantly more likely to have elevated Drug Abuse Screening Test scores when compared with those who were in the self-treatment group. Those in the at-risk group also were significantly more likely to engage in marijuana and alcohol use. CONCLUSION: The findings from this exploratory study warrant additional research because several motivations for the nonmedical use of prescription medications seem associated with a greater likelihood of substance abuse problems.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".