From attention‐deficit/hyperactivity disorder to medical stimulant use to the diversion of prescribed stimulants to non‐medical stimulant use: connecting the dots
Bibliographic record
Abstract
AIMS: To describe the connections among the likelihood of attention deficit/hyperactivity disorder (ADHD), medical and non-medical methylphenidate and amphetamine use and the diversion of prescribed methylphenidate in the general adolescent population. DESIGN: Cross-sectional self-reported anonymous data from the 2002 Student Drug Use Survey in the Atlantic Provinces. SETTING: The Atlantic provinces of Canada. PARTICIPANTS: A total of 12,990 students participated. MEASUREMENTS: The outcomes were a positive ADHD screening test, medical and non-medical use of methylphenidate, medical and non-medical use of amphetamine and the giving and selling of methylphenidate medication by students with a prescription. The Ontario Child Health Study Hyperactivity Scale was used to screen for ADHD. FINDINGS: The prevalence of a positive ADHD screening test was 6% with no significant gender difference. The prevalence of medical and non-medical methylphenidate use and medical and non-medical amphetamine use was 2.0%, 6.6%, 1.2% and 8.7%, respectively. A positive ADHD screening test was independently predictive of these four patterns of use. About 26% of students with prescribed methylphenidate gave or sold some of their medication. Students in a class where at least one student had given or sold some of their prescribed pills had a 1.52-fold increased risk of non-medical methylphenidate use than their counterparts in classes where no giving or selling had taken place. CONCLUSIONS: Connections were demonstrated at the population level between ADHD, medical methylphenidate use, the diversion of prescribed methylphenidate and the non-medical use of methylphenidate. The appropriate assessment and management of ADHD are essential to minimize both the risk of diversion and of substance use associated with unrecognized or untreated ADHD.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".