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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.022 | 0.039 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".