Shifting North American drug markets and challenges for the system of care
Bibliographic record
Abstract
Drug markets are dynamic systems which change based on demand, competition, legislation and revenue. Shifts that are not met with immediate and appropriate responses from the healthcare system can lead to public health crises with tragic levels of morbidity and mortality, as experienced Europe in the early 1990s and as is the case in North America currently. The major feature of the current drug market shift in North America is towards highly potent synthetic opioids such as fentanyl and fentanyl analogues. An additional spike in stimulant use further complicates this issue. Without understanding the ever-changing dynamics of drug markets and consequent patterns of drug use, the healthcare system will continue to be ineffective in its response, and morbidity and mortality will continue to increase. Economic perspectives are largely neglected in research and clinical contexts, but better treatment alternatives need to consider the large-scale macroeconomic conditions of drug markets as well as the behavioural economics of individual substance use. It is important for policy makers, health authorities, first responders and medical providers to be aware of the clinical implications of drug market changes in order to best serve people who use drugs. Only with significant clinical research, a comprehensive reorganization of the system of care across all sectors, and an evidence-driven governance, will we be successful in addressing the challenges brought on by the recent shifts in drug markets.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".