The role of Alberta pharmacists working in opioid use disorder and their potential to prescribe buprenorphine-naloxone: A qualitative study
Bibliographic record
Abstract
Objective: Opioid toxicity continues to have significant morbidity and mortality in Alberta. Opioid agonist therapy is an effective treatment for opiate use disorder (OUD), with first-line treatment with buprenorphine-naloxone (BUP-NAL) being both highly effective and safe. Barriers to care limit access to treatment, and more access points for treatment are needed. Pharmacists in Alberta have a wide scope of prescribing authority and high accessibility. This study describes the barriers to care and the roles of pharmacists engaged in OUD treatment, and explores the potential for pharmacists to prescribe BUP-NAL to improve access to care. Methods: Semistructured interviews were conducted with pharmacists from Alberta in January 2024. Key informants were identified using professional networks and the reverse snowball method, and continued until data saturation. Thematic analysis was conducted by 2 investigators using open coding. Results: Ten pharmacists were interviewed, and 4 major themes emerged: barriers to access OUD treatment, the current role of pharmacists in caring for patients with OUD, the future role of pharmacists as prescribers of BUP-NAL, and enabling pharmacists to prescribe. Patients experience many barriers to care, and a complex health system contributes to this. Pharmacists working with patients with OUD are highly knowledgeable and involved in assessing, managing, and monitoring therapy in a multidisciplinary capacity. Extending authority for pharmacists to prescribe BUP-NAL can improve access to care but must consider collaboration and social context. Conclusion: Pharmacists are skilled and positioned to improve access to care for patients with OUD needing BUP-NAL.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".