Where Is the Opioid Use Epidemic in Mexico? A Cautionary Tale for Policymakers South of the US–Mexico Border
Bibliographic record
Abstract
In North America, opioid use and its harms have increased in the United States and Canada over the past 2 decades. However, Mexico has yet to document patterns suggesting a higher level of opioid use or attendant harms.Historically, Mexico has been a country with low-level use of opioids, although heroin use has been documented. Low-level opioid use is likely attributable to structural, cultural, and individual factors. However, a range of dynamic factors may be converging to increase the use of opioids: legislative changes to opioid prescribing, national health insurance coverage of opioids, pressure from the pharmaceutical industry, changing demographics and disease burden, forced migration and its trauma, and an increase in the production and trafficking of heroin. In addition, harm-reduction services are scarce.Mexico may transition from a country of low opioid use to high opioid use but has the opportunity to respond effectively through a combination of targeted public health surveillance of high-risk groups, preparation of appropriate infrastructure to support evidence-based treatment, and interventions and policies to avoid a widespread opioid use 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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.010 | 0.021 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.014 | 0.026 |
| Insufficient payload (model declined to judge) | 0.010 | 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".