Best Practices and Innovations for Managing Codeine Misuse and Dependence
Bibliographic record
Abstract
PURPOSE: Promoting and ensuring safe use of codeine containing medicines remains a public health issue given the rise in reporting of misuse and dependence particularly in countries where available over-the-counter (OTC). The aim of this unique study was to identify best practices in management of opioid abuse and dependence, particularly codeine, and innovations to meet challenges surrounding safe and compliant use, patient awareness-raising, reducing health harms and enhancing successful treatment of dependence. METHODS: A mixed methods approach using three data points was used that included : (1) analysis of data from existing scoping reviews to identify potential areas for innovation (2) interviews with key national stakeholders from public health, pharmaceutical, regulatory, primary care and addiction practice in three distinct regulatory regimes (Ireland, United Kingdom and South Africa); and (3) a circular email request for information on potential innovations to members of the European Medicine's Agency European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCEPP). Data from these three sources were analysed to identify best practices and opportunities for innovation. RESULTS: Best practices and potential innovations were identified under the nine headings: (1) manufacture; (2) product information and public education; (3) responsible prescribing; (4) monitoring and surveillance; (5) dispensing, screening and brief interventions in community pharmacies; (6) safety in the workplace and on the road; (7) internet supply of codeine and online support; (8) treatment of codeine dependence; and (9) learning resources and training for health professionals. CONCLUSIONS: Challenges ensuring availability of codeine containing medicines for legitimate therapeutic use, while minimising misuse, dependence and related health harms warrant consideration of new innovations. Most promising innovative potential lies across the products' retail lifecycle from manufacture to prescriber and community pharmacy practitioner.This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".