Virtual Primary Care for People with Opioid Use Disorder: A Scoping Review of Current Strategies, Benefits, and Challenges
Bibliographic record
Abstract
ABSTRACT Background There is a pressing need to understand the implications of the rapid adoption of virtual primary care for people with opioid use disorder. Potential impacts, including disruptions to opiate agonist therapies, and the prospect of improved service accessibility remain underexplored. This scoping review synthesizes current literature on virtual primary care for people with opioid use disorder, with a specific focus on benefits, challenges, and strategies. Methods We followed the Joanna Briggs Institute methodological approach for scoping reviews and reported our findings consistent with the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews. We conducted searches on MEDLINE, Web of Science, CINAHL Complete, and Embase using our developed search strategy with no date restrictions. We incorporated all study types that included the three concepts (i.e., virtual care; primary care; people with opioid use disorder). We excluded research on minors, asynchronous virtual modalities, and care not provided in a primary care setting. We used Covidence to screen and extract data, pulling information on study characteristics, health system features, patient outcomes, and challenges and benefits of virtual primary care. We conducted inductive content analysis and calculated descriptive statistics. We appraised the quality of studies using the Quality Assessment with Diverse Studies tool and categorized findings using the Consolidated Framework for Implementation Science. Results Our search identified 1474 studies. We removed 536 duplicates, leaving 936 studies for title and abstract screening. After a double review process, we retained 28 studies for extraction. Most studies described virtual primary care delivered via phone (n=18, 64.3%) rather than video. While increased healthcare accessibility was a significant benefit (n=13, 46.4%) to the adoption of virtual visits, issues around access to technology and digital literacy stood out as the main challenge (n=12, 42.9%). Conclusions The available studies highlight the potential for enhancing accessibility and continuous access care for people with opioid use disorder using virtual modalities. Future research and policies must focus on bridging gaps to ensure virtual primary care does not exacerbate or entrench health inequities.
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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 | 0.040 | 0.138 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.026 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".