The Global Diversion of Pharmaceutical Drugs Non‐medical use and diversion of psychotropic prescription drugs in North America: a review of sourcing routes and control measures
Bibliographic record
Abstract
AIMS: North America features some of the world's highest consumption levels for controlled psychoactive prescription drugs (PPDs; e.g. prescription opioids, benzodiazepines, stimulants), with non-medical use and related harms (e.g. morbidity, mortality) rising in key populations in recent years. While the determinants, characteristics and impacts of these 'use' problems are increasingly well documented, little is known about the 'supply' side of non-medical PPD use, much of which is facilitated by 'diversion' as a key sourcing route. This paper provides a select review of the phenomenon of PPD diversion in North America, also considering interventions and policy implications. METHODS: A conceptual and empirical review of select-peer- and non-peer-reviewed research literature from 1991 to 2010 focusing upon PPD diversion in North America was conducted. RESULT: The phenomenon of PPD diversion is heterogeneous. Especially among general populations, a large proportion of PPDs for non-medical use are obtained from friends or family members. Other PPD diversion routes involve 'double doctoring' or 'prescription shopping'; street drug markets; drug thefts, prescription forgeries or fraud; as well as PPD purchases from the internet. CONCLUSIONS: The distinct nature and heterogeneity make PPD diversion a complex and difficult target for interventions. Prescription monitoring programs (PMPs) appear to reduce overall PPD use, yet their impact on reducing diversion or non-medical use is not clear. Law enforcement is unlikely to reach PPD diversion effectively. Effective reduction will probably require reductions in overall PPD consumption volumes, although such will need to be accomplished without compromising standards of good medical (e.g. pain) care.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".