213,057 research outputs found
Social Network Based Substance Abuse Prevention via Network Modification (A Preliminary Study)
Substance use and abuse is a significant public health problem in the United
States. Group-based intervention programs offer a promising means of preventing
and reducing substance abuse. While effective, unfortunately, inappropriate
intervention groups can result in an increase in deviant behaviors among
participants, a process known as deviancy training. This paper investigates the
problem of optimizing the social influence related to the deviant behavior via
careful construction of the intervention groups. We propose a Mixed Integer
Optimization formulation that decides on the intervention groups, captures the
impact of the groups on the structure of the social network, and models the
impact of these changes on behavior propagation. In addition, we propose a
scalable hybrid meta-heuristic algorithm that combines Mixed Integer
Programming and Large Neighborhood Search to find near-optimal network
partitions. Our algorithm is packaged in the form of GUIDE, an AI-based
decision aid that recommends intervention groups. Being the first quantitative
decision aid of this kind, GUIDE is able to assist practitioners, in particular
social workers, in three key areas: (a) GUIDE proposes near-optimal solutions
that are shown, via extensive simulations, to significantly improve over the
traditional qualitative practices for forming intervention groups; (b) GUIDE is
able to identify circumstances when an intervention will lead to deviancy
training, thus saving time, money, and effort; (c) GUIDE can evaluate current
strategies of group formation and discard strategies that will lead to deviancy
training. In developing GUIDE, we are primarily interested in substance use
interventions among homeless youth as a high risk and vulnerable population.
GUIDE is developed in collaboration with Urban Peak, a homeless-youth serving
organization in Denver, CO, and is under preparation for deployment
Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science
Abstract Background Many interventions found to be effective in health services research studies fail to translate into meaningful patient care outcomes across multiple contexts. Health services researchers recognize the need to evaluate not only summative outcomes but also formative outcomes to assess the extent to which implementation is effective in a specific setting, prolongs sustainability, and promotes dissemination into other settings. Many implementation theories have been published to help promote effective implementation. However, they overlap considerably in the constructs included in individual theories, and a comparison of theories reveals that each is missing important constructs included in other theories. In addition, terminology and definitions are not consistent across theories. We describe the Consolidated Framework For Implementation Research (CFIR) that offers an overarching typology to promote implementation theory development and verification about what works where and why across multiple contexts. Methods We used a snowball sampling approach to identify published theories that were evaluated to identify constructs based on strength of conceptual or empirical support for influence on implementation, consistency in definitions, alignment with our own findings, and potential for measurement. We combined constructs across published theories that had different labels but were redundant or overlapping in definition, and we parsed apart constructs that conflated underlying concepts. Results The CFIR is composed of five major domains: intervention characteristics, outer setting, inner setting, characteristics of the individuals involved, and the process of implementation. Eight constructs were identified related to the intervention (e.g., evidence strength and quality), four constructs were identified related to outer setting (e.g., patient needs and resources), 12 constructs were identified related to inner setting (e.g., culture, leadership engagement), five constructs were identified related to individual characteristics, and eight constructs were identified related to process (e.g., plan, evaluate, and reflect). We present explicit definitions for each construct. Conclusion The CFIR provides a pragmatic structure for approaching complex, interacting, multi-level, and transient states of constructs in the real world by embracing, consolidating, and unifying key constructs from published implementation theories. It can be used to guide formative evaluations and build the implementation knowledge base across multiple studies and settings.http://deepblue.lib.umich.edu/bitstream/2027.42/78272/1/1748-5908-4-50.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78272/2/1748-5908-4-50-S1.PDFhttp://deepblue.lib.umich.edu/bitstream/2027.42/78272/3/1748-5908-4-50-S3.PDFhttp://deepblue.lib.umich.edu/bitstream/2027.42/78272/4/1748-5908-4-50-S4.PDFhttp://deepblue.lib.umich.edu/bitstream/2027.42/78272/5/1748-5908-4-50.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/78272/6/1748-5908-4-50-S2.PDFPeer Reviewe
Adoption as a Social Marker: Innovation Diffusion with Outgroup Aversion
Social identities are among the key factors driving behavior in complex
societies. Signals of social identity are known to influence individual
behaviors in the adoption of innovations. Yet the population-level consequences
of identity signaling on the diffusion of innovations are largely unknown. Here
we use both analytical and agent-based modeling to consider the spread of a
beneficial innovation in a structured population in which there exist two
groups who are averse to being mistaken for each other. We investigate the
dynamics of adoption and consider the role of structural factors such as
demographic skew and communication scale on population-level outcomes. We find
that outgroup aversion can lead to adoption being delayed or suppressed in one
group, and that population-wide underadoption is common. Comparing the two
models, we find that differential adoption can arise due to structural
constraints on information flow even in the absence of intrinsic between-group
differences in adoption rates. Further, we find that patterns of polarization
in adoption at both local and global scales depend on the details of
demographic organization and the scale of communication. This research has
particular relevance to widely beneficial but identity-relevant products and
behaviors, such as green technologies, where overall levels of adoption
determine the positive benefits that accrue to society at large.Comment: 26 pages, 10 figure
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