Emergent Social Environments As Predictors of Recovery Resident Outcomes

 描述(由申请人提供)：拟议的研究测试了一个基于动态系统的理论，该理论解释了康复之家居民与恢复相关的态度、行为和社会关系是如何共同演变的，以及这些新出现的个人特征和房屋级别的社会结构随后如何与个人的恢复终点相联系。该理论采用了群体和社会网络动力学中的概念，将它们置于更广泛的社区心理健康框架中。它是通过衡量信任、友谊和建议/指导的关系作为动态的多元化社交网络(Snijders等人，2012)--存在于每个家庭中的多个同时相互依赖的关系--来运作和测试的。这些关系被认为是随着时间的推移而共同发展的，影响和影响着与康复相关的态度和行为，以及屋外的个人网络。通过汇集房屋之间的动态关系，我们将应用面向随机行为者的建模框架(Snijders等人，2010)来估计一组随机的、连续时间的差分方程式。这 然后将对模型进行理论分析，以提出可能的战略，以改善这一群体的结果(例如，保持居住权)。我们拟议的研究将确定社会环境影响健康结果的机制，从而通过提高美国住宅康复之家系统的有效性以及重组和改善其他基于社区的恢复环境来帮助降低不必要的医疗成本。这些类型的改善可能会带来更好的客户护理和治疗结果。我们建议的研究将提供关于内部结构和动态的重要洞察力，作为个人保持积极恢复轨迹的可能性的预测因素；它将提供关于外部恢复行为(例如AA)、外部以自我为中心的网络(范围、组成、动态)和内部社会网络之间相互作用的信息，并可能识别个人达到显著复发可能性的“失败点”。此外，这项工作应该为康复之家的网络动态研究建立一个初步的框架，这将促进对更广泛的康复之家领域的理论发展和实证调查。

 DESCRIPTION (provided by applicant): The proposed research tests a dynamic systems-based theory, which explains how recovery house residents' recovery-related attitudes, behaviors, and social relationships co-evolve, and how these emergent individual characteristics and house-level social structures subsequently link to individuals' recovery endpoints. The theory adapts concepts from group and social network dynamics, placing them within a broader community mental health framework. It is operationalized and tested by measuring relationships of trust, friendship, and advice/mentoring as dynamic multiplex social networks (Snijders et al., 2012)-multiple, simultaneous interdependent relationships--that exist within each house. These relationships are assumed to co-evolve over time, affecting and affected by recovery-related attitudes and behaviors, and personal networks outside the house. By pooling dynamic relationships across houses, we will apply the Stochastic Actor-Oriented Modeling framework (Snijders et al., 2010) to estimate a set of stochastic, continuous-time difference equations. This model will then be subjected to theoretical analyses designed to suggest possible strategies for improving outcomes (e.g. maintaining residence) for this population. Our proposed study will identify mechanisms through which social environments affect health outcomes, and thereby contribute to reducing unnecessary health care costs by improving the effectiveness of the residential recovery home system in the US and also restructuring and improving other community-based recovery settings. These types of improvements could lead to better client care and treatment outcomes. Our proposed research would provide significant insight on within house structure and dynamics as predictors of an individual's likelihood of maintaining a positive recovery trajectory; it would provide information on the interactions of external recovery behaviors (e.g. AA), external ego-centered networks (scope, composition, dynamics), and within-setting social networks, and it might identify points of "failure" where the individual reaches a significant likelihood of relapse. In addition, this work should result in an initial framework for the study of network dynamics in recovery homes which should facilitate both the theoretical development and empirical investigation of the broader domain of recovery homes.