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Network Analysis for Formative Evaluation of Collaborative, Team Science Research Partnerships
Cancer health disparities persist across the cancer care continuum despite decades of effort to eliminate them. Among the strategies currently used to address these disparities are multi-institution research initiatives that engage multiple stakeholders and change efforts. Endemic to the theory of c...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10637077/ https://www.ncbi.nlm.nih.gov/pubmed/37594293 http://dx.doi.org/10.1177/01632787231195642 |
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author | Liu, Grace M. Meadows, Meredith L. Wiley, Katherine T. Jurinsky, Jordan Anglemyer, Andrew A. Wang, Lucy L. Schneider, Joseph T. Suiter, Sarah V. |
author_facet | Liu, Grace M. Meadows, Meredith L. Wiley, Katherine T. Jurinsky, Jordan Anglemyer, Andrew A. Wang, Lucy L. Schneider, Joseph T. Suiter, Sarah V. |
author_sort | Liu, Grace M. |
collection | PubMed |
description | Cancer health disparities persist across the cancer care continuum despite decades of effort to eliminate them. Among the strategies currently used to address these disparities are multi-institution research initiatives that engage multiple stakeholders and change efforts. Endemic to the theory of change of such programs is the idea that collaboration—across institutions, research disciplines, and academic ranks—is necessary to improve outcomes. Despite this emphasis on collaboration, however, it is not often a focus of evaluation for these programs and others like them. In this paper we describe a method for evaluating collaboration within the Meharry-Vanderbilt-Tennessee State University Cancer Partnership using network analysis. Specifically, we used network analysis of co-authorship on academic publications to visualize the growth and patterns of scientific collaboration across partnership institutions, research disciplines, and academic ranks over time. We presented the results of the network analysis to internal and external advisory groups, creating the opportunity to discuss partnership collaboration, celebrate successes, and identify opportunities for improvement. We propose that basic network analysis of existing data along with network visualizations can foster conversation and feedback and are simple and effective ways to evaluate collaboration initiatives. |
format | Online Article Text |
id | pubmed-10637077 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-106370772023-11-14 Network Analysis for Formative Evaluation of Collaborative, Team Science Research Partnerships Liu, Grace M. Meadows, Meredith L. Wiley, Katherine T. Jurinsky, Jordan Anglemyer, Andrew A. Wang, Lucy L. Schneider, Joseph T. Suiter, Sarah V. Eval Health Prof Translational Research Cancer health disparities persist across the cancer care continuum despite decades of effort to eliminate them. Among the strategies currently used to address these disparities are multi-institution research initiatives that engage multiple stakeholders and change efforts. Endemic to the theory of change of such programs is the idea that collaboration—across institutions, research disciplines, and academic ranks—is necessary to improve outcomes. Despite this emphasis on collaboration, however, it is not often a focus of evaluation for these programs and others like them. In this paper we describe a method for evaluating collaboration within the Meharry-Vanderbilt-Tennessee State University Cancer Partnership using network analysis. Specifically, we used network analysis of co-authorship on academic publications to visualize the growth and patterns of scientific collaboration across partnership institutions, research disciplines, and academic ranks over time. We presented the results of the network analysis to internal and external advisory groups, creating the opportunity to discuss partnership collaboration, celebrate successes, and identify opportunities for improvement. We propose that basic network analysis of existing data along with network visualizations can foster conversation and feedback and are simple and effective ways to evaluate collaboration initiatives. SAGE Publications 2023-08-18 2023-12 /pmc/articles/PMC10637077/ /pubmed/37594293 http://dx.doi.org/10.1177/01632787231195642 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Translational Research Liu, Grace M. Meadows, Meredith L. Wiley, Katherine T. Jurinsky, Jordan Anglemyer, Andrew A. Wang, Lucy L. Schneider, Joseph T. Suiter, Sarah V. Network Analysis for Formative Evaluation of Collaborative, Team Science Research Partnerships |
title | Network Analysis for Formative Evaluation of Collaborative, Team Science Research Partnerships |
title_full | Network Analysis for Formative Evaluation of Collaborative, Team Science Research Partnerships |
title_fullStr | Network Analysis for Formative Evaluation of Collaborative, Team Science Research Partnerships |
title_full_unstemmed | Network Analysis for Formative Evaluation of Collaborative, Team Science Research Partnerships |
title_short | Network Analysis for Formative Evaluation of Collaborative, Team Science Research Partnerships |
title_sort | network analysis for formative evaluation of collaborative, team science research partnerships |
topic | Translational Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10637077/ https://www.ncbi.nlm.nih.gov/pubmed/37594293 http://dx.doi.org/10.1177/01632787231195642 |
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