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

Abstract

BackgroundMany 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.MethodsWe 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.ResultsThe 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.ConclusionsOur 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.

Joining the dots: the role of brokers in nutrition policy in Australia

“…It has been used to examine a range of topics areas in health including community health coalitions [19], physician collaborations [20], relationships between tobacco control partners [21] and identifying the most powerful actors in public health policymaking [22]. A network consists of ‘ties’ which are patterns of association that link actors together; they can include informal linkages, based on communication and trust, as well as links based on the traditional institutionalized structures of co-ordination [23].…”

Conceptualising, Operationalising, and Measuring Trust in Participatory Health Research Networks: a Scoping Review

“…The majority of references explored trust in social networks (n = 17), (30-35, 38-42, 49-54) while seven references explored trust in CBPR, (43)(44)(45)(46)(47)(48)55) and two explored both trust in social networks and CBPR. (36,37) Further individual study details and characteristics can be found in Additional File 3.…”

“…Sustaining high levels of trust should become a key priority for coalition leaders moving forward." (37) Legend:ST: Sub-Theme, R(#): Outcome pertaining to Trust Similar to conceptualisation and operationalisation, R2 'relational' features of trust continued to be the most common parent theme for outcomes related to trust across studies. Comparatively, the second most prevalent parent theme was, R4 'features of social network analysis', indicating the reporting of more features of social network analysis connected with trust in some way (e.g., associated or indicating trust).…”

Social Network Gap Analysis Evaluation

“…Council effectiveness is often measured by member satisfaction, commitment to the coalition, and the quality of planning efforts 25 . While studies have also assessed trust, 26 inclusiveness, 27 and effectiveness 28 among members, there are to date no published studies using social network analysis for conducting a partnership gap analysis and associated partnership database. To highlight the utility of SNGA, this research presents a case study of the Southeastern Health Equity Council (SHEC).…”