Characteristics of Injection Drug Users Who Participate in Drug Dealing: Implications for Drug Policy
Bibliographic record
Abstract
So-called "balanced" drug policy couples enforcement initiatives targeting drug dealers with health-focused interventions serving addicted individuals. There are few evaluations of this approach, and little is known about how these two populations may overlap. We evaluated factors associated with drug dealing among injection drug users (IDUs) in Vancouver, Canada, and examined self-reported drug-dealing roles and reasons for dealing. Among 412 IDUs seen from March through December 2005, 68 (17%) had dealt drugs during the previous six months. Variables independently associated with drug dealing included: recent incarceration (adjusted odds ratio [AOR] = 2.9; 95%CI: 1.4-6.0); frequent heroin injection (AOR = 2.5; 95%CI: 1.4-4.6); frequent cocaine injection (AOR = 2.0; 95%CI: 1.1-3.8); and recent overdose (AOR = 2.7; 95%CI: 1.0-7.3). The most common drug-dealing roles were direct selling (82%), middling (35%), and steering (19%), while the most common reasons for dealing included obtaining drugs (49%) and money (36%). Drug dealing among IDUs was predicted by several markers of higher intensity addiction, and drug-dealing IDUs tended to occupy the most dangerous positions in the drug-dealing hierarchy. These findings suggest that elements of "balanced" drug policies may undermine each other and indicate the need for alternative interventions.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".