Leveraging the role of community pharmacists in the prevention, surveillance, and treatment of opioid use disorders
Bibliographic record
Abstract
The global rise in opioid-related harms has impacted the United States severely. Current efforts to manage the opioid crisis have prompted a re-evaluation of many of the existing roles in the healthcare system, in order to maximize their individual effects on reducing opioid-associated morbidity and preventing overdose deaths. As one of the most accessible healthcare professionals in the US, pharmacists are well-positioned to participate in such activities. Historically, US pharmacists have had a limited role in the surveillance and treatment of substance use disorders. This narrative review explores the literature describing novel programs designed to capitalize on the role of the community pharmacist in helping to reduce opioid-related harms, as well as evaluations of existing practices already in place in the US and elsewhere around the world. Specific approaches examined include strategies to facilitate pharmacist monitoring for problematic opioid use, to increase pharmacy-based harm reduction efforts (including naloxone distribution and needle exchange programs), and to involve community pharmacists in the dispensation of opioid agonist therapy (OAT). Each of these activities present a potential means to further engage pharmacists in the identification and treatment of opioid use disorders (OUDs). Through a careful examination of these approaches, we hope that new strategies can be adopted to leverage the unique role of the community pharmacist to help reduce opioid-related harms in the US.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".