Non‐medical use of prescription opioids and prescription opioid‐related harms: why so markedly higher in <scp>N</scp>orth <scp>A</scp>merica compared to the rest of the world?
Bibliographic record
Abstract
AIMS: This paper aims to identify possible system-level factors contributing to the marked differences in the levels of non-medical prescription opioid use (NMPOU) and prescription opioid (PO)-related harms in North America (i.e. the United States and Canada) compared to other global regions. METHODS: Scientific literature and information related to relevant areas of health systems, policy and practice were reviewed and integrated. RESULTS: We identified several but different factors contributing to the observed differences. First, North American health-care systems consume substantially more Pos-even when compared to other high-income countries-than any other global region, with dispensing levels associated strongly with levels of NMPOU and PO-related harms. Secondly, North American health-care systems, compared to other systems, appear to have lesser regulatory access restrictions for, and rely more upon, community-based dispensing mechanisms of POs, facilitating higher dissemination level and availability (e.g. through diversion) of POs implicated in NMPOU and harms. Thirdly, we note that the generally high levels of psychotrophic drug use, dynamics of medical-professional culture (including patient expectations for 'effective treatment'), as well as the more pronounced 'for-profit' orientation of key elements of health care (including pharmaceutical advertising), may have boosted the PO-related problems observed in North America. CONCLUSIONS: Differences in the organization of health systems, prescription practices, dispensing and medical cultures and patient expectations appear to contribute to the observed inter-regional differences in non-medical prescription opioid use and prescription opioid-related harms, although consistent evidence and causal analyses are limited. Further comparative examination of these and other potential drivers is needed, and also for evidence-based intervention and policy development.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".