The impact of the COVID-19 pandemic on medications for opioid use disorder services in the U.S. and Canada: a scoping review
Bibliographic record
Abstract
Since the arrival of the COVID-19 pandemic, preliminary evidence suggests that rates of opioid use and overdose in North America have only been exacerbated. During this time, healthcare services providing medications for opioid use disorder (MOUD) have faced heightened challenges, rapidly adjusting services in order to continue to provide access to treatment. To better understand the impact of the pandemic on MOUD services in the U.S. and Canada, this scoping review summarizes and synthesizes the existing literature on this topic. Articles were deemed eligible to be included in this review if they met the following three criteria: focused on MOUD services; situated within the COVID-19 pandemic; and situated within the U.S. or Canada. Common themes among the articles that met inclusion included the impacts of MOUD policy changes; the transition to telehealth; challenges to providing MOUD; innovative changes to services; and recommendations for policy and service changes. Many articles supported MOUD regulatory changes, with some finding these changes had increased access to MOUD for underserved populations. There is currently a pressing need to evaluate the impacts on MOUD services in greater depth, as recent changes could have lasting implications on future MOUD regulatory policies and treatment standards.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".