Community Pharmacy‐Based Injectable Opioid Agonist Treatment: Findings From a Canadian Pilot Program
Bibliographic record
Abstract
INTRODUCTION: Access to evidence-based treatment for opioid use disorder remains limited, particularly for individuals who have not responded to oral opioid agonist treatment (OAT). A community pharmacy-based model of injectable OAT (iOAT) was piloted in Vancouver, Canada from March 2017 to December 2018. This brief report describes the program structure, participant sociodemographics, reported outcomes, and strengths and areas for improvement of the program. METHODS: A retrospective review of cross-sectional, interviewer-led questionnaire data from participants who accessed iOAT at the pharmacy site (n = 176) and provided informed consent was conducted. Outcomes include participant-reported changes in symptomatology, function and satisfaction, analysed through descriptive statistics. Open-ended responses were analysed using content analysis to identify strengths and areas for improvement of the program. RESULTS: Fifty-one participants (29%) completed the questionnaire, and most had multiple previous overdoses and trials of oral OAT. The most commonly reported outcomes were reduction in illicit opioid use (76%), opioid cravings (45%) and illicit substance use (45%). Participants identified key strengths of the program as positive experiences with staff and efficiency of the pharmacy model including flexible dosing time and the ability to pick up other medications at the same time. Suggested improvements focused on medication options (e.g., access to diacetylmorphine, alternate routes of administration), expanded hours and flexibility, additional support services, and increased capacity and space. DISCUSSION AND CONCLUSIONS: Community pharmacy-based iOAT represents a novel strategy to expand access to evidence-based opioid use disorder treatment among individuals who inject opioids and have not responded to or do not prefer oral OAT.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".