Collaborative Community Prevention: An Ecological Approach to Mental Health Support for Children in Rural America

There exists a dearth of research literature devoted to informing mental health practice in rural areas. However, what little research that does exist surrounding children’s mental wellness in rural places describes mental health programs as being smaller, under-served versions of their urban counterparts (National Association for Rural Mental Health, 2001). Mental health collaboration in rural areas is a clear need and an ongoing challenge. This study aims to address these concerns by reviewing relevant theories, analyzing one rural community’s mental health needs, and identifying next steps in mental health service delivery for this community. Additional research surrounding the mental health of children in schools indicates that children benefit most from mental health services when the context of both the individual child and the child’s environment is taken into account (Bronfenbrenner, 1979). Further, when taking the individual and systemic levels into account, research indicates that ideal delivery systems incorporate a preventative, public health model approach that uses a Multi-Tiered System of Support (MTSS). (Friedman, 2003). One way to effect change such as this is to create a school-family-community partnership. Such partnership allows previously separate organizations to create a common mission, streamline services, reduce redundancies, and enhance communication between professionals. This study utilized a qualitative case study design of a rural county in the Midwestern United States, addressing the following research questions: How does one identify and enhance collaborations in rural mental health? What are barriers to creating an integrated system of support for children, adolescents, and families? What do community members see as the biggest concern for youth and the system currently serving them? What supportive services and resources already exist and can be built upon? In reviewing the literature, what is available or recommended to support the community in addressing its concerns

An Abstract Formal Basis for Digital Crowds

Crowdsourcing, together with its related approaches, has become very popular in recent years. All crowdsourcing processes involve the participation of a digital crowd, a large number of people that access a single Internet platform or shared service. In this paper we explore the possibility of applying formal methods, typically used for the verification of software and hardware systems, in analysing the behaviour of a digital crowd. More precisely, we provide a formal description language for specifying digital crowds. We represent digital crowds in which the agents do not directly communicate with each other. We further show how this specification can provide the basis for sophisticated formal methods, in particular formal verification.Comment: 32 pages, 4 figure

Final Report from the Models for Change Evaluation

Note: This evaluation is accompanied by an evaluation of the National Campaign for this initiative as well as introduction to the evaluation effort by MacArthur's President, Julia Stasch, and a response to the evaluation from the program team. Access these related materials here (https://www.macfound.org/press/grantee-publications/evaluation-models-change-initiative).Models for Change is an initiative of The John D. and Catherine T. MacArthur Foundationto accelerate juvenile justice reforms and promote fairer, more effective, and more developmentally appropriate juvenile justice systems throughout the United States. Between 2004 and 2014, the Foundation invested more than $121 million in the initiative, intending to create sustainable and replicable models of systems reform.In June 2013, the Foundation partnered with Mathematica Policy Research and the University of Maryland to design and conduct a retrospective evaluation of Models for Change. The evaluation focused on the core state strategy, the action network strategy, and the national context in which Models for Change played out. This report is a digest and synthesis of several technical reports prepared as part of the evaluation

Categorical Ontology of Complex Systems, Meta-Systems and Theory of Levels: The Emergence of Life, Human Consciousness and Society

Single cell interactomics in simpler organisms, as well as somatic cell interactomics in multicellular organisms, involve biomolecular interactions in complex signalling pathways that were recently represented in modular terms by quantum automata with ‘reversible behavior’ representing normal cell cycling and division. Other implications of such quantum automata, modular modeling of signaling pathways and cell differentiation during development are in the fields of neural plasticity and brain development leading to quantum-weave dynamic patterns and specific molecular processes underlying extensive memory, learning, anticipation mechanisms and the emergence of human consciousness during the early brain development in children. Cell interactomics is here represented for the first time as a mixture of ‘classical’ states that determine molecular dynamics subject to Boltzmann statistics and ‘steady-state’, metabolic (multi-stable) manifolds, together with ‘configuration’ spaces of metastable quantum states emerging from complex quantum dynamics of interacting networks of biomolecules, such as proteins and nucleic acids that are now collectively defined as quantum interactomics. On the other hand, the time dependent evolution over several generations of cancer cells --that are generally known to undergo frequent and extensive genetic mutations and, indeed, suffer genomic transformations at the chromosome level (such as extensive chromosomal aberrations found in many colon cancers)-- cannot be correctly represented in the ‘standard’ terms of quantum automaton modules, as the normal somatic cells can. This significant difference at the cancer cell genomic level is therefore reflected in major changes in cancer cell interactomics often from one cancer cell ‘cycle’ to the next, and thus it requires substantial changes in the modeling strategies, mathematical tools and experimental designs aimed at understanding cancer mechanisms. Novel solutions to this important problem in carcinogenesis are proposed and experimental validation procedures are suggested. From a medical research and clinical standpoint, this approach has important consequences for addressing and preventing the development of cancer resistance to medical therapy in ongoing clinical trials involving stage III cancer patients, as well as improving the designs of future clinical trials for cancer treatments.\ud \ud \ud KEYWORDS: Emergence of Life and Human Consciousness;\ud Proteomics; Artificial Intelligence; Complex Systems Dynamics; Quantum Automata models and Quantum Interactomics; quantum-weave dynamic patterns underlying human consciousness; specific molecular processes underlying extensive memory, learning, anticipation mechanisms and human consciousness; emergence of human consciousness during the early brain development in children; Cancer cell ‘cycling’; interacting networks of proteins and nucleic acids; genetic mutations and chromosomal aberrations in cancers, such as colon cancer; development of cancer resistance to therapy; ongoing clinical trials involving stage III cancer patients’ possible improvements of the designs for future clinical trials and cancer treatments. \ud \u

Employing Environmental Data and Machine Learning to Improve Mobile Health Receptivity

Behavioral intervention strategies can be enhanced by recognizing human activities using eHealth technologies. As we find after a thorough literature review, activity spotting and added insights may be used to detect daily routines inferring receptivity for mobile notifications similar to just-in-time support. Towards this end, this work develops a model, using machine learning, to analyze the motivation of digital mental health users that answer self-assessment questions in their everyday lives through an intelligent mobile application. A uniform and extensible sequence prediction model combining environmental data with everyday activities has been created and validated for proof of concept through an experiment. We find that the reported receptivity is not sequentially predictable on its own, the mean error and standard deviation are only slightly below by-chance comparison. Nevertheless, predicting the upcoming activity shows to cover about 39% of the day (up to 58% in the best case) and can be linked to user individual intervention preferences to indirectly find an opportune moment of receptivity. Therefore, we introduce an application comprising the influences of sensor data on activities and intervention thresholds, as well as allowing for preferred events on a weekly basis. As a result of combining those multiple approaches, promising avenues for innovative behavioral assessments are possible. Identifying and segmenting the appropriate set of activities is key. Consequently, deliberate and thoughtful design lays the foundation for further development within research projects by extending the activity weighting process or introducing a model reinforcement.BMBF, 13GW0157A, Verbundprojekt: Self-administered Psycho-TherApy-SystemS (SELFPASS) - Teilvorhaben: Data Analytics and Prescription for SELFPASSTU Berlin, Open-Access-Mittel - 201