Exploring team dynamics during the development of a multi-institutional cross-disciplinary translational team: Implications for potential best practices.

Evaluation communication leadership long COVID multi-institutional team science translational science

Journal

Journal of clinical and translational science
ISSN: 2059-8661
Titre abrégé: J Clin Transl Sci
Pays: England
ID NLM: 101689953

Informations de publication

Date de publication:
2023
Historique:
received: 04 05 2023
revised: 06 09 2023
accepted: 21 09 2023
medline: 29 11 2023
pubmed: 29 11 2023
entrez: 29 11 2023
Statut: epublish

Résumé

A recent literature review revealed no studies that explored teams that used an explicit theoretical framework for multiteam systems in academic settings, such as the increasingly important multi-institutional cross-disciplinary translational team (MCTT) form. We conducted an exploratory 30-interview grounded theory study over two rounds to analyze participants' experiences from three universities who assembled an MCTT in order to pursue a complex grant proposal related to research on post-acute sequelae of COVID-19, also called "long COVID." This article considers activities beginning with preliminary discussions among principal investigators through grant writing and submission, and completion of reviews by the National Center for Advancing Translational Sciences, which resulted in the proposal not being scored. There were two stages to this interview study with MCTT members: pre-submission, and post-decision. Round one focused on the process of developing structures to collaborate on proposal writing and assembly, whereas round two focused on evaluation of the complete process. A total of 15 participants agreed to be interviewed in each round. The first round of interviews was conducted prior to submission and explored issues during proposal writing, including (1) importance of the topic; (2) meaning and perception of "team" within the MCTT context; and (3) leadership at different levels of the team. The second round explored best practices-related issues including (1) leadership and design; (2) specific proposal assembly tasks; (3) communication; and (4) critical events. We conclude with suggestions for developing best practices for assembling MCTTs involving multi-institutional teams.

Identifiants

pubmed: 38028346
doi: 10.1017/cts.2023.640
pii: S2059866123006404
pmc: PMC10643934
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e220

Informations de copyright

© The Author(s) 2023.

Déclaration de conflit d'intérêts

The authors have no conflicts of interest to declare.

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Auteurs

Joseph A Kotarba (JA)

Institute for Translational Sciences, University of Texas Medical Branch, Galveston, TX, USA.
Department of Sociology, Texas State University, San Marcos, TX, USA.

Stephen Molldrem (S)

Institute for Translational Sciences, University of Texas Medical Branch, Galveston, TX, USA.
Institute for Bioethics and Health Humanities, University of Texas Medical Branch, Galveston, TX, USA.

Elise Smith (E)

Institute for Translational Sciences, University of Texas Medical Branch, Galveston, TX, USA.
Institute for Bioethics and Health Humanities, University of Texas Medical Branch, Galveston, TX, USA.

Heidi Spratt (H)

Institute for Translational Sciences, University of Texas Medical Branch, Galveston, TX, USA.
Department of Biostatistics and Data Science, University of Texas Medical Branch, Galveston, TX, USA.

Suresh K Bhavnani (SK)

Institute for Translational Sciences, University of Texas Medical Branch, Galveston, TX, USA.
Department of Biostatistics and Data Science, University of Texas Medical Branch, Galveston, TX, USA.

Jeffrey S Farroni (JS)

Institute for Translational Sciences, University of Texas Medical Branch, Galveston, TX, USA.
Institute for Bioethics and Health Humanities, University of Texas Medical Branch, Galveston, TX, USA.

Kevin Wooten (K)

Institute for Translational Sciences, University of Texas Medical Branch, Galveston, TX, USA.
University of Houston Clear Lake, Houston, TX, USA.

Classifications MeSH