ArticlePublic health reports (Washington, D.C. : 1974)
Lessening the Impact of Opioid Misuse at a Federally Qualified Health Center in New York: Outcomes of an Integrated Workforce Training Program.
Article in Public health reports (Washington, D.C. : 1974). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 3 citations in OpenAlex.
- Three trajectories of implementation of medications for opioid use disorder in primary care.Addiction science & clinical practice · 2025Article
- Organizational Characteristics of Medical-Legal Partnerships in HIV Care: Exploring Challenges and Opportunities through a Mixed Methods Study.The Journal of law, medicine & ethics : a journal of the American Society of Law, Medicine & Ethics · 2025Article
- Supporting Nursing and Public Health During the COVID-19 Pandemic and Other Public Health Emergencies.Public health reports (Washington, D.C. : 1974)Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The COVID-19 pandemic has placed an unprecedented burden on patients, health care providers, and communities and has been particularly challenging for medically underserved populations impacted by the social determinants of health, as well as people with co-occurring mental health and substance use risks. This case study examines outcomes and lessons learned from a multisite low-threshold medication-assisted treatment (MAT) program at a federally qualified health center in partnership with a large suburban public university in New York to integrate and train Health Resources & Services Administration Behavioral Health Workforce Education and Training-funded graduate student trainees in social work and nursing in screening, brief intervention, and referral to treatment and patient care coordination, including social determinants of health and medical and behavioral comorbidities. The MAT program for the treatment of opioid use disorder has a low threshold for entry that is accessible and affordable, reduces barriers to care, and uses a harm reduction approach. Outcome data showed an average 70% retention rate in the MAT program and reductions in substance use. And, while more than 73% of patients reported being somewhat or definitely impacted by the pandemic, most patients endorsed the effectiveness of telemedicine and telebehavioral health, such that 86% indicated the pandemic did not affect the quality of their health care. The main implementation lessons learned were the importance of increasing the capacity of primary care and health care centers to deliver integrated care, using cross-disciplinary practicum experiences to enhance trainee competencies, and addressing the social determinants of health among populations with social vulnerabilities and chronic medical conditions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.