Medication Treatment for Opioid use Disorder and Community Pharmacy: Expanding Care during a National Epidemic and Global Pandemic
Bibliographic record
Abstract
Medications for opioid use disorder (MOUD), such as methadone and buprenorphine, are effective strategies for treatment of opioid use disorder (OUD) and reducing overdose risk. MOUD treatment rates continue to be low across the US, and currently, some evidence suggests access to evidence-based treatment is becoming increasingly difficult for those with OUD as a result of the 2019 novel corona virus (COVID-19). A major underutilized source to address these serious challenges in the US is community pharmacy given the specialized training of pharmacists, high levels of consumer trust, and general availability for accessing these service settings. Canadian, Australian, and European pharmacists have made important contributions to the treatment and care of those with OUD over the past decades. Unfortunately, US pharmacists are not permitted to prescribe MOUD and are only currently allowed to dispense methadone for the treatment of pain, not OUD. US policymakers, regulators, and practitioners must work to facilitate this advancement of community pharmacy-based through research, education, practice, and industry. Advancing community pharmacy-based MOUD for leading clinical management of OUD and dispensation of treatment medications will afford the US a critical innovation for addressing the opioid epidemic, fallout from COVID-19, and getting individuals the care they need.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.020 | 0.024 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".