Factors influencing opioid agonist therapy retention among individuals with opioid use disorder in primary care
Bibliographic record
Abstract
The opioid crisis remains a significant public health concern, with fentanyl contributing to 79% of accidental opioid-related deaths in Canada between January and June 2024, nearly a 40% increase since the national surveillance in 2016. Opioid agonist therapy (OAT), using medications such as methadone and buprenorphine, is an evidence-based treatment for opioid use disorder (OUD) that aims to reduce harm and mortality. While OAT is increasingly delivered in primary care to improve accessibility, patient retention in these programs remains a significant challenge. Retention is a commonly used outcome in OUD treatment studies; however, there is no universal definition. This integrative review explores the factors influencing OAT retention among individuals with opioid use disorder in primary care settings. A systematic search of peerreviewed literature published from 2016 to 2024 identified nine U.S.-based studies, including three qualitative and six cohort studies. Findings indicate that comorbid mental health and substance use disorders, along with limited access to psychosocial supports, negatively affect retention. Conversely, low-barrier, trauma-informed, and multidisciplinary care models are associated with improved outcomes. These findings highlight the need for OAT programs to adapt to the complex needs of patients with OUD by providing individualized, flexible, and accessible treatment options. Integrating mental health and addiction care within primary care settings may improve patient engagement and decrease opioid-related harm and mortality.,
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".