The North American opioid epidemic: current challenges and a call for treatment as prevention
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it