1University of Colorado Denver, CO, USA.
Health Educ Behav. 2013 Oct;40(1 Suppl):13S-23S. doi: 10.1177/1090198113492759.
Interorganizational collaboration is an essential function of public health agencies. These partnerships form social networks that involve diverse types of partners and varying levels of interaction. Such collaborations are widely accepted and encouraged, yet very little comparative research exists on how public health partnerships develop and evolve, specifically in terms of how subsequent network structures are linked to outcomes. A systems science approach, that is, one that considers the interdependencies and nested features of networks, provides the appropriate methods to examine the complex nature of these networks. Applying Mays and Scutchfields's categorization of "structural signatures" (breadth, density, and centralization), this research examines how network structure influences the outcomes of public health collaboratives. Secondary data from the Program to Analyze, Record, and Track Networks to Enhance Relationships (www.partnertool.net) data set are analyzed. This data set consists of dyadic (N = 12,355), organizational (N = 2,486), and whole network (N = 99) data from public health collaborations around the United States. Network data are used to calculate structural signatures and weighted least squares regression is used to examine how network structures can predict selected intermediary outcomes (resource contributions, overall value and trust rankings, and outcomes) in public health collaboratives. Our findings suggest that network structure may have an influence on collaborative-related outcomes. The structural signature that had the most significant relationship to outcomes was density, with higher density indicating more positive outcomes. Also significant was the finding that more breadth creates new challenges such as difficulty in reaching consensus and creating ties with other members. However, assumptions that these structural components lead to improved outcomes for public health collaboratives may be slightly premature. Implications of these findings for research and practice are discussed.
组织间合作是公共卫生机构的基本职能。这些伙伴关系形成了涉及不同类型伙伴和不同程度互动的社交网络。这种合作得到了广泛的认可和鼓励，但关于公共卫生伙伴关系如何发展和演变的比较研究很少，特别是关于后续网络结构如何与结果相关联的研究。系统科学方法，即考虑网络的相互依存关系和嵌套特征的方法，为研究这些网络的复杂性质提供了适当的方法。本研究应用 Mays 和 Scutchfield 的“结构特征”（广度、密度和集中化）分类，考察了网络结构如何影响公共卫生合作的结果。使用来自美国各地公共卫生合作的二元（N=12355）、组织（N=2486）和整体网络（N=99）数据的二次数据，对 Program to Analyze, Record, and Track Networks to Enhance Relationships（www.partnertool.net）数据集进行了分析。网络数据用于计算结构特征，加权最小二乘法回归用于检验网络结构如何预测公共卫生合作中的选定中介结果（资源贡献、整体价值和信任排名以及结果）。我们的研究结果表明，网络结构可能对合作相关结果产生影响。与结果最相关的结构特征是密度，密度越高表示结果越积极。另一个重要发现是，更广泛的范围会带来新的挑战，例如难以达成共识和与其他成员建立联系。然而，这些结构成分会导致公共卫生合作的结果得到改善的假设可能有些过早。本文讨论了这些发现对研究和实践的意义。
