The Opioid Epidemic in Numbers: A Meta-Analytic Review of Mortality, findings, and Implications for Prevention
Bibliographic record
Abstract
The opioid crisis in North America, particularly the United States and Canada, has been characterized by increasing methadone distribution, overdose deaths and diversion, although recent efforts have seen declines in some areas.Canada's prescription opioid dispensing increased until 2012, after which areas such as Ontario saw significant declines.The United States experienced a staggering 345% increase in opioid-related deaths from 2001-2016, heavily affecting people aged 25-34.This growing epidemic is further highlighted by the U.S. National Survey, which shows that 8.9% of Americans aged 12 or older engaged in illicit drug use recently.Research links opioid sales to overdose deaths, highlighting the dangers of inappropriate prescription practices.To address this, Medication-Assisted Treatment (MAT), behavioral therapies and support groups are promoted.Anti-stigma interventions such as acceptance and commitment therapy and motivational interviewing have been shown to be effective.A consistent pattern observed in cities such as Philadelphia and San Francisco indicate that young heroin addicts often switch from pharmaceutical opioids to heroin, driven by the economics of drug supply.To address opioid use disorders, primary care has recognized MAT as critical, with new innovative models such as multi-level care and stakeholder engagement.Nevertheless, barriers such as stigma and lack of expertise pose challenges, highlighting the urgent need for refined strategies and models tailored to different primary care settings.
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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 | 0.012 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".