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，该理论解释了恢复房屋居民的恢复相关态度，行为和社会关系如何共同演变，以及这些新兴的个人特征和房屋级别的社会结构如何随后与个人的恢复端点联系起来。该理论采用了群体和社会网络动态的概念，将其置于更广泛的社区心理健康框架内。它通过测量信任、友谊和建议/指导的关系作为动态多元社交网络来操作和测试（Snijders等人，2012年）-多重，同时相互依存的关系-存在于每个房子。这些关系被认为是随着时间的推移共同发展，影响和影响恢复相关的态度和行为，以及家庭以外的个人网络。通过汇集跨房屋的动态关系，我们将应用随机面向角色的建模框架（Snijders等人，2010）来估计一组随机的连续时间差分方程。这 然后，将对模型进行理论分析，旨在提出改善这一人口结果（例如，保持居住）的可能战略。我们提出的研究将确定社会环境影响健康结果的机制，从而有助于通过提高美国住宅恢复之家系统的有效性，以及重组和改善其他以社区为基础的恢复环境，减少不必要的医疗保健费用。这些类型的改进可能会导致更好的客户护理和治疗结果。我们提出的研究将提供重要的洞察内部的房子结构和动态的预测个人的可能性保持积极的恢复轨迹;它将提供外部恢复行为相互作用的信息，（例如AA），外部自我中心网络（范围、组成、动态）和环境内的社交网络，并且它可能识别出个体达到复发的显著可能性的“失败”点。此外，这项工作应导致在恢复家园的网络动态研究的初步框架，这应有利于理论发展和更广泛的恢复家园领域的实证调查。

 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.