Friedman School of Nutrition Science and Policy, ChildObesity180, Tufts University, 150 Harrison Ave, Boston, MA, 02111, USA.
Department of Sociology, Computational Social Science Institute, University of Massachusetts Amherst, Amherst, MA, 01003, USA.
BMC Public Health. 2022 Sep 30;22(1):1838. doi: 10.1186/s12889-022-14208-3.
While most coalition research focuses on studying the effects of peer relationship structure, this study examines the coevolution of coalition structure and behavior across three communities in the U.S. with the goal of identifying coalition dynamics that impact a childhood obesity prevention intervention. METHODS: Over two years (2018-2020), three communities within the U.S. participated in a childhood obesity prevention intervention at different times. This intervention was guided by the Stakeholder-Driven Community Diffusion theory, which describes an empirically testable mechanism for promoting community change. Measures are part of the Stakeholder-driven Community Diffusion (SDCD) survey with demonstrated reliability, which include knowledge of and engagement with childhood obesity prevention and social networks. Data from three coalition-committees and their respective networks were used to build three different stochastic actor-oriented models. These models were used to examine the coevolution of coalition structure with coalition behavior (defined a priori as knowledge of and engagement with obesity prevention) among coalition-committee members and their nominated alters (Network A) and coalition-committee members only (Network B). RESULTS: Overall, coalitions decrease in size and their structure becomes less dense over time. Both Network A and B show a consistent preference to form and sustain ties with those who have more ties. In Network B, there was a trend for those who have higher knowledge scores to increase their number of ties over time. The same trend appeared in Network A but varied based on their peers' knowledge in and engagement with childhood obesity prevention. Across models, engagement with childhood obesity prevention research was not a significant driver of changes in either coalition network structure or knowledge.
The trends in coalition Network A and B's coevolution models may point to context-specific features (e.g., ties among stakeholders) that can be leveraged for better intervention implementation. To that end, examining tie density, average path length, network diameter, and the dynamics of each behavior outcome (i.e., knowledge in and engagement with childhood obesity prevention) may help tailor whole-of-community interventions. Future research should attend to additional behavioral variables (e.g., group efficacy) that can capture other aspects of coalition development and that influence implementation, and to testing the efficacy of network interventions after trends have been identified.
虽然大多数联盟研究都集中在研究同伴关系结构的影响上,但本研究考察了美国三个社区的联盟结构和行为的共同演变,目的是确定影响儿童肥胖预防干预的联盟动态。
在两年(2018-2020 年)内,美国的三个社区在不同时间参与了一项儿童肥胖预防干预。该干预措施以利益相关者驱动的社区扩散理论为指导,该理论描述了一种可通过实证检验的促进社区变革的机制。措施是利益相关者驱动的社区扩散(SDCD)调查的一部分,具有可靠的证明,包括对儿童肥胖预防的了解和参与以及社交网络。来自三个联盟委员会及其各自网络的数据被用于构建三个不同的随机演员导向模型。这些模型用于检查联盟委员会成员及其提名的盟友(网络 A)和联盟委员会成员自身(网络 B)之间的联盟结构与联盟行为(先验定义为对肥胖预防的了解和参与)的共同演变。
总体而言,随着时间的推移,联盟的规模会缩小,其结构会变得不那么密集。网络 A 和 B 都表现出一种一致的倾向,即与具有更多联系的人建立和维持联系。在网络 B 中,那些知识得分较高的人随着时间的推移增加联系数量的趋势较为明显。在网络 A 中也出现了同样的趋势,但取决于他们的同龄人对儿童肥胖预防的了解和参与情况。在所有模型中,参与儿童肥胖预防研究并不是联盟网络结构或知识变化的重要驱动因素。
联盟网络 A 和 B 的共同演变模型中的趋势可能指向特定于上下文的特征(例如,利益相关者之间的联系),这些特征可以被利用来更好地实施干预措施。为此,检查联系密度、平均路径长度、网络直径以及每个行为结果(即对儿童肥胖预防的了解和参与)的动态可能有助于调整整个社区的干预措施。未来的研究应该关注其他行为变量(例如,群体效能),这些变量可以捕捉到联盟发展的其他方面,并影响实施,以及在确定趋势后测试网络干预的效果。