Individual vs. Group Delivery of Acupuncture Therapy for Chronic Musculoskeletal Pain in Urban Primary Care-a Randomized Trial

BACKGROUND: Acupuncture has been shown to be effective for the treatment of chronic musculoskeletal back, neck, and osteoarthritis pain. However, access to acupuncture treatment has been limited in medically underserved and low-income populations. OBJECTIVE: Acupuncture therapy delivered in groups could reduce cost and expand access. We compared the effectiveness of group versus individual acupuncture for pain and function among ethnically diverse, low-income primary care patients with chronic musculoskeletal pain. DESIGN: This was a randomized comparative effectiveness non-inferiority trial in 6 Bronx primary care community health centers. Participants with chronic ( \u3e 3 months) back, neck, or osteoarthritis pain were randomly assigned to individual or group acupuncture therapy for 12 weeks. PARTICIPANTS: Seven hundred seventy-nine participants were randomized. Mean age was 54.8 years. 35.3% of participants identified as black and 56.9% identified as Latino. Seventy-six percent were Medicaid insured, 60% reported poor/fair health, and 37% were unable to work due to disability. INTERVENTIONS: Participants received weekly acupuncture treatment in either group or individual setting for 12 weeks. MAIN MEASURES: Primary outcome was pain interference on the Brief Pain Inventory at 12 weeks; secondary outcomes were pain severity (BPI), physical and mental well-being (PROMIS-10), and opiate use. Outcome measures were collected at baseline, 12 and 24 weeks. KEY RESULTS: 37.5% of individual arm and 30.3% in group had \u3e 30% improvement in pain interference (d = 7.2%, 95% CI - 0.6%, 15.1%). Non-inferiority of group acupuncture was not demonstrated for the primary outcome assuming a margin of 10%. In the responder analysis of physical well-being, 63.1% of individual participants and 59.5% of group had clinically important improvement at 12 weeks (d = 3.6%, 95% CI - 4.2%, 11.4%). CONCLUSIONS: Both individual and group acupuncture therapy delivered in primary care settings reduced chronic pain and improved physical function at 12 weeks; non-inferiority of group was not shown. TRIAL REGISTRATION: Clinicaltrials.gov # NCT02456727

The Technical Difficulties Associated With Interprofessional Healthcare Through Telehealth Medicine

Presentation illustrating the following: With the implantation of Telehealth medicine over the past year, many have shown advantages of this method, however, most don\u27t see behind the scenes. Working with an interprofessional team, collaboration is needed to move smoothly through these encounters. Technical difficulties, talking over each other and feeling the need to talk through needed silences happens often. Since it\u27s a new experience for everyone, provider included, working through an encounter can take patience from all involved.https://dune.une.edu/cecespring2021/1001/thumbnail.jp

Immanent authority and the performance of community in late nineteenth century Montmartre

This article develops an account of the aesthetic structure of ‘immanent’, non-foundational forms of authority. It argues for the need to develop a positive account of decentralized authority as an important constitutive form of social bond. Through a genealogical reading of the cultural experiments of the artistic community of late nineteenth century Montmartre, it builds an analysis of the affective and perceptual structures of immanent authority. Authority, it argues, operates across three axes of experience: amplitude, gravity and distance. Although the artistic experiments and cultural politics of fin-de-siècle Montmartre were politically naive, they offer an illuminating lens through which to view the emerging experiential structures of authority in the twentieth century

Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning

The rapidly-developing intersection of machine learning (ML) with high-energy physics (HEP) presents both opportunities and challenges to our community. Far beyond applications of standard ML tools to HEP problems, genuinely new and potentially revolutionary approaches are being developed by a generation of talent literate in both fields. There is an urgent need to support the needs of the interdisciplinary community driving these developments, including funding dedicated research at the intersection of the two fields, investing in high-performance computing at universities and tailoring allocation policies to support this work, developing of community tools and standards, and providing education and career paths for young researchers attracted by the intellectual vitality of machine learning for high energy physics

Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning

The rapidly-developing intersection of machine learning (ML) with high-energy physics (HEP) presents both opportunities and challenges to our community. Far beyond applications of standard ML tools to HEP problems, genuinely new and potentially revolutionary approaches are being developed by a generation of talent literate in both fields. There is an urgent need to support the needs of the interdisciplinary community driving these developments, including funding dedicated research at the intersection of the two fields, investing in high-performance computing at universities and tailoring allocation policies to support this work, developing of community tools and standards, and providing education and career paths for young researchers attracted by the intellectual vitality of machine learning for high energy physics

Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning

The rapidly-developing intersection of machine learning (ML) with high-energy physics (HEP) presents both opportunities and challenges to our community. Far beyond applications of standard ML tools to HEP problems, genuinely new and potentially revolutionary approaches are being developed by a generation of talent literate in both fields. There is an urgent need to support the needs of the interdisciplinary community driving these developments, including funding dedicated research at the intersection of the two fields, investing in high-performance computing at universities and tailoring allocation policies to support this work, developing of community tools and standards, and providing education and career paths for young researchers attracted by the intellectual vitality of machine learning for high energy physics