Medical and nonmedical stimulant use among adolescents: from sanctioned to unsanctioned use.
Bibliographic record
Abstract
BACKGROUND: The past decade has seen a generalized upward trend in the prevalence of adolescent use of substances, including stimulants. The purpose of this article was to determine the prevalence of and risk factors for the medical and nonmedical use of stimulants, and the diversion of prescribed stimulants among adolescent students, and to demonstrate links between medical use, nonmedical use and the diversion of stimulants. METHODS: A self-reported anonymous questionnaire was administered in 1998 to a random sample of students in grades 7, 9, 10 and 12 in New Brunswick, Nova Scotia, Prince Edward Island, and Newfoundland and Labrador about their medical and nonmedical use of stimulants (Benzedrine, Dexedrine, Ritalin, Cylert, diet pills, "speed," "uppers," "bennies" and "pep pills"). A total of 13,549 students completed the questionnaire, representing a 99% participation rate among the students present at school on the day of the survey. RESULTS: Of the 5.3% of students who reported medical use of stimulants in the 12 months before the survey, 14.7% reported having given some of their medication, 7.3% having sold some of their medication, 4.3% having experienced theft and 3.0% having been forced to give up some of their medication. Nonmedical stimulant use by students who did not have a prescription for stimulants was significantly related to increased numbers of students who gave or sold some of their prescribed stimulants, at both the school class and individual student levels (p < 0.001). INTERPRETATION: Although the vast majority of adolescent students taking prescribed stimulants appeared to be using their medication as sanctioned, a link was found between medical and nonmedical stimulant use and the diversion of medication from sanctioned to unsanctioned use.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".