Evaluation of a Multistage Implementation of Medication for Opioid Use Disorder Services in Primary Care
Bibliographic record
Abstract
Introduction Despite effective treatment for opioid use disorder, access to care is limited. Increased availability of evidence-based medication for opioid use disorder (MOUD) treatment within primary care is urgently needed. This study describes efforts to increase MOUD services within a large urban primary care practice.Methods After an internal assessment of barriers to MOUD services, a two-phase approach was used to educate providers and to implement MOUD services within a primary care practice over 2.5 years. Physicians became X-waiver certified in the education phase and completed four internal training sessions. Physicians completed pre-post surveys to assess their intention to prescribe MOUD. In the implementation phase, an interdisciplinary team designed accessible MOUD clinical hours. The RE-AIM model guided the evaluation of the MOUD training and services. The clinic evaluation included a medical records review, a provider focus group (n = 6), and patient interviews (n = 6).Results Pre-post surveys indicated that providers did not increase their intentions to prescribe MOUD. Once MOUD clinical hours were operational, the number of providers treating patients with MOUD increased substantially. Patients who received these services found them low-barrier, non-stigmatizing, and effective. The clinical team was satisfied with service delivery but offered suggestions for improvement for the whole primary care team.Conclusions Increasing access to MOUD services within primary care may require iterative efforts to overcome practice-specific barriers, and gains may still be moderate. Training in MOUD services should focus on the whole primary care team as it requires interdisciplinary coordination to deliver high-quality services.
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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 | 0.023 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".