The North American opioid epidemic: current challenges and a call for treatment as prevention
Bibliographic record
Abstract
There is a need for creative, public health-oriented solutions to the increasingly intractable problems associated with the North American opioid epidemic. This epidemic is a fundamentally continental problem, as routes of migration, drug demand, and drug exchange link the USA with Mexico and Canada. The challenges faced throughout North America include entrenched prescribing practices of opioid medications, high costs and low availability of medication-assisted treatment (MAT), and policy approaches that present substantial barriers to care.We advocate for the scale up of a low-threshold treatment model for MAT that incorporates the best practices in addiction treatment. Such a model would remove barriers to care through widespread treatment availability and affordability and also a policy of decriminalization. Given that MAT reduces the frequency of drug injecting among opioid injectors, this treatment model should also be guided by an understanding of the socially communicable nature of injection drug use, such that increasing MAT availability may also prevent the spread of injecting practices to individuals at risk of transitions from non-injection to injection drug use. To that end, the "Treatment as Prevention" model employed to respond to the individual- and population-level risks for HIV/AIDS prevention could be adapted to efforts to halt the North American opioid epidemic.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".