Shifting drug markets in North America - a global crisis in the making?
Bibliographic record
Abstract
Understanding drug market dynamics and their underlying driving factors is paramount to developing effective responses to the overdose crisis in North America. This paper summarises the distinct drug market trends observed locally and internationally over the past decade to extrapolate future drug market trajectories. The emergence of fentanyl on North American street markets from 2014 onwards led to a shift of street drug use patterns. Previously perceived as contaminants, novel synthetic opioids became the drugs of choice and a trend towards higher potency was observed across various substance classes. The diversification of distribution strategies as well as the regionalisation and industrialisation of production followed basic economic principles that were heavily influenced by prosecution and policy makers. Particularly, the trend towards higher potency is likely most indicative of what to expect from future illicit drug market developments. Nitazenes and fentanyl-analogues, several times more potent than fentanyl itself, are increasingly detected in toxicological testing and have the potential of becoming the drugs of choice in the future. The dynamic of drug import and local production is less clear and influenced by a multitude of factors like precursor availability, know-how, infrastructure, and the success of local drug enforcement strategies. Drug market dynamics and the current trajectory towards ultrapotent opioids need to be recognised by legislation, enforcement, and the health care system to prepare effective responses. Without significant improvements in treatment access, the implementation of preventative approaches and early warning systems, the mortality rate will continue to increase. Furthermore, there is no mechanism in place preventing the currently North American focused overdose crisis to spread to other parts of the globe, particularly Europe. A system of oversight, research, and treatment is needed to address mortality rates of historic proportions and prevent further harm.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".