The effectiveness of changes to drug policy, regulation and legislation for reducing harms associated with opioids and supporting their medicinal use in Australia, Canada and the UK: A systematic review
Bibliographic record
Abstract
Governments and health agencies in Australia, Canada and the United Kingdom (UK) have implemented changes to drug policy, regulation and/or legislation for reducing increasing rates of dependency, overdose and other harms associated with prescription and over-the-counter opioid drugs, and supporting their medicinal use. However, there has been no systematic evaluation of empirical evidence on the effectiveness of the drug policy changes. A systematic review of studies was conducted to assess the evidence. Studies included peer-reviewed and grey literature. The findings of studies were synthesised to identify common features and outcomes of changes to drug policy, including reductions in overdose, death and other indicators of effectiveness. There were 21 studies that met review criteria, and were of changes that generally aimed to: increase access to treatment for issues with opioid drugs; or, restrict access to opioids and other drugs. The evidence base was limited, and overall showed no major impacts in reducing harms and supporting medicinal use. However, studies of changes focused on increasing access to naloxone suggested the most promising evidence of effectiveness. More research and evaluation is required. With the risk of increased harms associated with opioid drug use in Australian, Canadian and United Kingdom settings, policymakers and other stakeholders need to prioritize measures that support: more research and evaluation; national campaigns publicising awareness of risks associated with opioid drug use and their appropriate medicinal use; and investment in health care services offering more appropriate clinical management of pain and opioid drugs.
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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 | 0.015 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".