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Partnership capacity for community health improvement plan implementation: findings from a social network analysis

BACKGROUND: Many health departments collaborate with community organizations on community health improvement processes. While a number of resources exist to plan and implement a community health improvement plan (CHIP), little empirical evidence exists on how to leverage and expand partnerships when...

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Autores principales: McCullough, J. Mac, Eisen-Cohen, Eileen, Salas, S. Bianca
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4944444/
https://www.ncbi.nlm.nih.gov/pubmed/27411474
http://dx.doi.org/10.1186/s12889-016-3194-7
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author McCullough, J. Mac
Eisen-Cohen, Eileen
Salas, S. Bianca
author_facet McCullough, J. Mac
Eisen-Cohen, Eileen
Salas, S. Bianca
author_sort McCullough, J. Mac
collection PubMed
description BACKGROUND: Many health departments collaborate with community organizations on community health improvement processes. While a number of resources exist to plan and implement a community health improvement plan (CHIP), little empirical evidence exists on how to leverage and expand partnerships when implementing a CHIP. The purpose of this study was to identify characteristics of the network involved in implementing the CHIP in one large community. The aims of this analysis are to: 1) identify essential network partners (and thereby highlight potential network gaps), 2) gauge current levels of partner involvement, 3) understand and effectively leverage network resources, and 4) enable a data-driven approach for future collaborative network improvements. METHODS: We collected primary data via survey from n = 41 organizations involved in the Health Improvement Partnership of Maricopa County (HIPMC), in Arizona. Using the previously validated Program to Analyze, Record, and Track Networks to Enhance Relationships (PARTNER) tool, organizations provided information on existing ties with other coalition members, including frequency and depth of partnership and eight categories of perceived value/trust of each current partner organization. RESULTS: The coalition’s overall network had a density score of 30 %, degree centralization score of 73 %, and trust score of 81 %. Network maps are presented to identify existing relationships between HIPMC members according to partnership frequency and intensity, duration of involvement in the coalition, and self-reported contributions to the coalition. Overall, number of ties and other partnership measures were positively correlated with an organization’s perceived value and trustworthiness as rated by other coalition members. CONCLUSIONS: Our study presents a novel use of social network analysis methods to evaluate the coalition of organizations involved in implementing a CHIP in an urban community. The large coalition had relatively low network density but high degree centralization—meaning key organizations link organizations otherwise not tightly partnering. Coalition members rated each other highly on trust, a positive sign for future partnership development efforts. Examination of network maps reveal key organizations that can be targeted for future partnership facilitation and expansion. Future network data collection will enable exploration of longitudinal trends and exploration of network characteristics versus health behavior, status, and outcome changes.
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spelling pubmed-49444442016-07-15 Partnership capacity for community health improvement plan implementation: findings from a social network analysis McCullough, J. Mac Eisen-Cohen, Eileen Salas, S. Bianca BMC Public Health Research Article BACKGROUND: Many health departments collaborate with community organizations on community health improvement processes. While a number of resources exist to plan and implement a community health improvement plan (CHIP), little empirical evidence exists on how to leverage and expand partnerships when implementing a CHIP. The purpose of this study was to identify characteristics of the network involved in implementing the CHIP in one large community. The aims of this analysis are to: 1) identify essential network partners (and thereby highlight potential network gaps), 2) gauge current levels of partner involvement, 3) understand and effectively leverage network resources, and 4) enable a data-driven approach for future collaborative network improvements. METHODS: We collected primary data via survey from n = 41 organizations involved in the Health Improvement Partnership of Maricopa County (HIPMC), in Arizona. Using the previously validated Program to Analyze, Record, and Track Networks to Enhance Relationships (PARTNER) tool, organizations provided information on existing ties with other coalition members, including frequency and depth of partnership and eight categories of perceived value/trust of each current partner organization. RESULTS: The coalition’s overall network had a density score of 30 %, degree centralization score of 73 %, and trust score of 81 %. Network maps are presented to identify existing relationships between HIPMC members according to partnership frequency and intensity, duration of involvement in the coalition, and self-reported contributions to the coalition. Overall, number of ties and other partnership measures were positively correlated with an organization’s perceived value and trustworthiness as rated by other coalition members. CONCLUSIONS: Our study presents a novel use of social network analysis methods to evaluate the coalition of organizations involved in implementing a CHIP in an urban community. The large coalition had relatively low network density but high degree centralization—meaning key organizations link organizations otherwise not tightly partnering. Coalition members rated each other highly on trust, a positive sign for future partnership development efforts. Examination of network maps reveal key organizations that can be targeted for future partnership facilitation and expansion. Future network data collection will enable exploration of longitudinal trends and exploration of network characteristics versus health behavior, status, and outcome changes. BioMed Central 2016-07-13 /pmc/articles/PMC4944444/ /pubmed/27411474 http://dx.doi.org/10.1186/s12889-016-3194-7 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
McCullough, J. Mac
Eisen-Cohen, Eileen
Salas, S. Bianca
Partnership capacity for community health improvement plan implementation: findings from a social network analysis
title Partnership capacity for community health improvement plan implementation: findings from a social network analysis
title_full Partnership capacity for community health improvement plan implementation: findings from a social network analysis
title_fullStr Partnership capacity for community health improvement plan implementation: findings from a social network analysis
title_full_unstemmed Partnership capacity for community health improvement plan implementation: findings from a social network analysis
title_short Partnership capacity for community health improvement plan implementation: findings from a social network analysis
title_sort partnership capacity for community health improvement plan implementation: findings from a social network analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4944444/
https://www.ncbi.nlm.nih.gov/pubmed/27411474
http://dx.doi.org/10.1186/s12889-016-3194-7
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