Development of pharmacy-based best practices to support safer use and management of prescription opioids based on an e-Delphi methodology
Bibliographic record
Abstract
BACKGROUND: Opioid utilization and related harm have increased in recent decades, notably in Australia, the United States, Canada, and some European countries. For people who are prescribed opioids, pharmacies offer an accessible, regular point-of-contact, providing a unique opportunity to address opioid prescription drugs risks. OBJECTIVE: This project aimed to develop consensus-based, best practice statements for improving the safer use of prescription opioids through community pharmacy settings. METHODS: The e-Delphi technique is used to obtain consensus from experts about issues where conclusive evidence is lacking, using multiple rounds of online participation. The investigator group identified an international group of potential participants with relevant expertise who were invited to the study, and asked to identify other experts for invitation. The e-Delphi process comprised three online rounds, involving (1) statement idea generation, (2) developing statement consensus, and (3) confirming and ranking statements. RESULTS: A diverse group of 42 experts (76 % female, 6 countries) participated, comprising pharmacists (n = 24, 57 %), medical doctors of differing specialties (n = 12, 29 %), and/or researchers (n = 28, 67 %), with a mean of 15 years' professional experience (SD = 8.08). Eighty-five statements were initially developed in Round 1, and 78 were supported with amendments, with suggestions to merge and remove items in Round 2, resulting in 72 final statements which were all endorsed in Round 3. Items spanned seven themes: education, monitoring outcomes and risk, deprescribing and pain management, overdose education and naloxone, opioid agonist treatment, staff education, and overarching practices. Preferred terminology was determined in Round 2 and confirmed in Round 3. CONCLUSIONS: Community pharmacies offer a unique opportunity to support the safer use of prescription opioids. These 72 best practice statements provide practical guidance on specific practices that pharmacists can undertake to support patients' safer use of prescription opioids and prevent or reduce harms from prescribed opioid use.
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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 | 0.091 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".