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Using attendance data for social network analysis of a community-engaged research partnership.
J Clin Transl Sci. 2020 Dec 21; 5(1):e75.JC

Abstract

BACKGROUND

The Rockefeller University Center for Clinical and Translational Science (RU-CCTS) and Clinical Directors Network (CDN), a Practice-Based Research Network (PBRN), fostered a community-academic research partnership involving Community Health Center (CHCs) clinicians, laboratory scientists, clinical researchers, community, and patient partners. From 2011 to 2018, the partnership designed and completed Community-Associated Methicillin-Resistant Staphylococcus Aureus Project (CAMP1), an observational study funded by the National Center for Advancing Translational Sciences (NCATS), and CAMP2, a Comparative Effectiveness Research Study funded by the Patient-Centered Outcomes Research Institute (PCORI). We conducted a social network analysis (SNA) to characterize this Community-Engaged Research (CEnR) partnership.

METHODS

Projects incorporated principles of Community-Based Participatory Research (CAMP1/2) and PCORI engagement rubrics (CAMP2). Meetings were designed to be highly interactive, facilitate co-learning, share governance, and incentivize ongoing engagement. Meeting attendance formed the raw dataset enriched by stakeholder roles and affiliations. We used SNA software (Gephi) to form networks for four project periods, characterize network attributes (density, degree, centrality, vulnerability), and create sociograms. Polynomial regression models were used to study stakeholder interactions.

RESULTS

Forty-seven progress meetings engaged 141 stakeholders, fulfilling 7 roles, and affiliated with 28 organizations (6 types). Network size, density, and interactions across organizations increased over time. Interactions between Community Members or Recruiters/Community Health Workers and almost every other role increased significantly across CAMP2 (P < 0.005); Community Members' centrality to the network increased over time.

CONCLUSIONS

In a partnership with a highly interactive meeting model, SNA using operational attendance data afforded a view of stakeholder interactions that realized the engagement goals of the partnership.

Links

PMC Free PDF

Authors+Show Affiliations

Vasquez KS
Community and Collaboration Core, The Rockefeller University, Center for Clinical and Translational Science, New York, NY, USA.
Chatterjee S
Department of Mathematics, City University of New York, City College & Graduate Center, New York, NY, USA.
Khalida C
Center for Excellence for Practice-Based Research and Learning, Clinical Directors Network (CDN), New York, NY, USA.
Moftah D
Center for Excellence for Practice-Based Research and Learning, Clinical Directors Network (CDN), New York, NY, USA.
D'Orazio B
Center for Excellence for Practice-Based Research and Learning, Clinical Directors Network (CDN), New York, NY, USA.
Leinberger-Jabari A
Public Health Research Center, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.
Tobin JN
Community and Collaboration Core, The Rockefeller University, Center for Clinical and Translational Science, New York, NY, USA. Center for Excellence for Practice-Based Research and Learning, Clinical Directors Network (CDN), New York, NY, USA.
Kost RG
Community and Collaboration Core, The Rockefeller University, Center for Clinical and Translational Science, New York, NY, USA.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

33948293

Citation

Vasquez, Kimberly S., et al. "Using Attendance Data for Social Network Analysis of a Community-engaged Research Partnership." Journal of Clinical and Translational Science, vol. 5, no. 1, 2020, pp. e75.
Vasquez KS, Chatterjee S, Khalida C, et al. Using attendance data for social network analysis of a community-engaged research partnership. J Clin Transl Sci. 2020;5(1):e75.
Vasquez, K. S., Chatterjee, S., Khalida, C., Moftah, D., D'Orazio, B., Leinberger-Jabari, A., Tobin, J. N., & Kost, R. G. (2020). Using attendance data for social network analysis of a community-engaged research partnership. Journal of Clinical and Translational Science, 5(1), e75. https://doi.org/10.1017/cts.2020.571
Vasquez KS, et al. Using Attendance Data for Social Network Analysis of a Community-engaged Research Partnership. J Clin Transl Sci. 2020 Dec 21;5(1):e75. PubMed PMID: 33948293.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Using attendance data for social network analysis of a community-engaged research partnership. AU - Vasquez,Kimberly S, AU - Chatterjee,Shirshendu, AU - Khalida,Chamanara, AU - Moftah,Dena, AU - D'Orazio,Brianna, AU - Leinberger-Jabari,Andrea, AU - Tobin,Jonathan N, AU - Kost,Rhonda G, Y1 - 2020/12/21/ PY - 2021/5/5/entrez PY - 2021/5/6/pubmed PY - 2021/5/6/medline KW - Interdisciplinary KW - collaboration outcomes KW - community engagement KW - community-based participatory research KW - partnership development KW - social network analysis SP - e75 EP - e75 JF - Journal of clinical and translational science JO - J Clin Transl Sci VL - 5 IS - 1 N2 - BACKGROUND: The Rockefeller University Center for Clinical and Translational Science (RU-CCTS) and Clinical Directors Network (CDN), a Practice-Based Research Network (PBRN), fostered a community-academic research partnership involving Community Health Center (CHCs) clinicians, laboratory scientists, clinical researchers, community, and patient partners. From 2011 to 2018, the partnership designed and completed Community-Associated Methicillin-Resistant Staphylococcus Aureus Project (CAMP1), an observational study funded by the National Center for Advancing Translational Sciences (NCATS), and CAMP2, a Comparative Effectiveness Research Study funded by the Patient-Centered Outcomes Research Institute (PCORI). We conducted a social network analysis (SNA) to characterize this Community-Engaged Research (CEnR) partnership. METHODS: Projects incorporated principles of Community-Based Participatory Research (CAMP1/2) and PCORI engagement rubrics (CAMP2). Meetings were designed to be highly interactive, facilitate co-learning, share governance, and incentivize ongoing engagement. Meeting attendance formed the raw dataset enriched by stakeholder roles and affiliations. We used SNA software (Gephi) to form networks for four project periods, characterize network attributes (density, degree, centrality, vulnerability), and create sociograms. Polynomial regression models were used to study stakeholder interactions. RESULTS: Forty-seven progress meetings engaged 141 stakeholders, fulfilling 7 roles, and affiliated with 28 organizations (6 types). Network size, density, and interactions across organizations increased over time. Interactions between Community Members or Recruiters/Community Health Workers and almost every other role increased significantly across CAMP2 (P < 0.005); Community Members' centrality to the network increased over time. CONCLUSIONS: In a partnership with a highly interactive meeting model, SNA using operational attendance data afforded a view of stakeholder interactions that realized the engagement goals of the partnership. SN - 2059-8661 UR - https://www.unboundmedicine.com/medline/citation/33948293/Using_attendance_data_for_social_network_analysis_of_a_community_engaged_research_partnership_ DB - PRIME DP - Unbound Medicine ER -