The Community-Based Medication-First program for opioid use disorder: a hybrid implementation study protocol of a rapid access to buprenorphine program in Washington State
Bibliographic record
Abstract
BACKGROUND: Opioid use disorder (OUD) is a serious health condition that is effectively treated with buprenorphine. However, only a minority of people with OUD are able to access buprenorphine. Many access points for buprenorphine have high barriers for initiation and retention. Health care and drug treatment systems have not been able to provide services to all-let alone the majority-who need it, and many with OUD report extreme challenges starting and staying on buprenorphine in those care settings. We describe the design and protocol for a study of a rapid access buprenorphine program model in six Washington State communities at existing sites serving people who are unhoused and/or using syringe services programs. This study aimed to test the effectiveness of a Community-Based Medication-First Program model. METHODS: We are conducting a hybrid effectiveness-implementation study of a rapid access buprenorphine model of care staffed by prescribers, nurse care managers, and care navigators. The Community-Based Medication-First model of care was designed as a 6-month, induction-stabilization-transition model to be delivered between 2019 and 2022. Effectiveness outcomes will be tested by comparing the intervention group with a comparison group derived from state records of people who had OUD. Construction of the comparison group will align characteristics such as geography, demographics, historical rates of arrests, OUD medication, and health care utilization, using restriction and propensity score techniques. Outcomes will include arrests, emergency and inpatient health care utilization, and mortality rates. Descriptive statistics for buprenorphine utilization patterns during the intervention period will be documented with the prescription drug monitoring program. DISCUSSION: Results of this study will help determine the effectiveness of the intervention. Given the serious population-level and individual-level impacts of OUD, it is essential that services be readily available to all people with OUD, including those who cannot readily access care due to their circumstances, capacity, preferences, and related systems barriers.
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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 | 0.033 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".