Resources
6 Results (showing 1 - 6)
Results sorted by updated date (newest first)
Results sorted by updated date (newest first)
Posted 5/17/2021 (updated 4/10/2024)
Medication-assisted treatment (MAT) is the use of medications, combined with counseling, to treat substance use disorders. Research has proven the effectiveness of MAT and addiction treatment experts endorse it, but a variety of barriers have prevented the widespread use of MAT. These include a lack of financing for medication, insufficient organizational infrastructure to deliver medication, state and county funding and regulatory obstacles, physician training and certification, staff and client resistance, and community attitudes.
Posted 1/16/2020 (updated 3/28/2024)
Co-location refers to services that are located in the same physical space (e.g. office, building, campus), though not necessarily fully integrated with one another.
Posted 11/27/2019 (updated 3/28/2024)
The purpose of this Technical Brief is to describe promising and innovative MAT models of care in primary care settings, describe barriers to MAT implementation, summarize the evidence available on MAT models of care in primary care settings, identify gaps in the evidence base, and guide future research.
Posted 6/26/2020 (updated 3/28/2024)
The MAT for OUD Playbook aims to address the growing need for guidance as more primary care practices and health systems begin to implement MAT. The Playbook’s framework is designed to be useful for practices implementing any array of MAT services.
Posted 7/28/2020 (updated 3/28/2024)
Brandeis University’s Institute for Behavioral Health Opioid Policy Research Collaborative launched the Brandeis Opioid Resource Connector (BORC) website, a comprehensive online resource for communities and local leaders addressing the opioid crisis.
Posted 11/11/2022 (updated 3/27/2024)
OMNI Institute, in partnership with the JBS RCORP-TA team, created the 2022-2023 RCORP-TA Data Learning Collaborative (LC) for grantees to come together and share knowledge, talk through challenges, and build relationships with one another. This LC will build upon the foundation established in the prior 2022 RCORP-TA Data Learning Collaborative.