New Challenges and Opportunities for Opioid Agonist Treatment Access and Retention: A Scoping Review
Bibliographic record
Abstract
Changes to the landscape of opioid use have occurred throughout the past two decades. Opioid agonist treatment (OAT) plays an important role in reducing opioid-related harms. Yet, people with opioid use disorder (PWOUD) face issues with access and retention. This scoping review sought to provide an up-to-date synthesis on barriers and facilitators to OAT. Ninety-three studies were included. Major barriers included cost, waiting lists, and negative public attitudes toward treatment. Prominent facilitators included OAT education and flexible treatment conditions such as telehealth-delivered OAT. In prison settings, limited support with treatment referral was a barrier. Fear of judgement was a challenge for pregnant people. There was a lack of information on barriers and facilitators specific to PWOUD dealing with chronic pain or mental health comorbidities. Results highlight new challenges in reaching an increasingly heterogeneous PWOUD population. Findings underline the potential for recent advancements in treatment modalities to promote access and retention.
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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".