The Impact of Residential Treatment on Emotionally Disturbed Boys

Within the past four decades, social work has witnessed the development of increasingly specialized servicecs to children, among these a sort of “total impact therapy” generally defined as residential treatment. In conjunction with the basic social work values of the bio-psycho-social nature of human maladjustment, residential centres have attempted to help the child effect a happier adjustment to his life situation by meeting some ungratified basic need. Institutions for dependent children complimented those for custodial care of even isolation; contemporary residential treatment centres are designed to meet a broader range of needs of the child than those of forty years ago through a variety of approaches, often referred to as milieu therapy. Consideration of the common needs of children is basic to questions concerning the place of institutional treatment and the particular type of child for which this social work service is the most appropriate one. The residential treatment centre addresses the whole gamut of a child’s needs from physical care to rehabilitation. Exposure to, and participation in, a group life experience simulating as closely as possible the family or community life experience is the element differentiating residential care from other treatment modes. By involvement in the realities of his daily situation and the working through or resolution of these, the child is helped to cope with his own growth and development—physical, emotional, and social. Problems and questions examined in this paper revolve around the residential treatment centre defined vaguely by the Child Welfare League of America as “A building....maintained and operated by a chartered agency, organization or institution, whose main purpose is to provide shelter and care to a group of unrelated children and youths up to eighteen years of age.” More specifically, the concern for research, the proposal and plans for implementation are focused on Mount St. Joseph, an autonomous, non-profit institution providing care for boys with moderate to severe emotional disturbances

An Evidence-Based Approach to Community Planning and Design for Children in Care

This report outlines an evidence-based approach to planning and designing a community for children in care as well as local at-risk populations using the Golconda Civilian Conservation Center (GCCC) and the neighboring San Damiano Site as a test case. We conducted interviews, design games, and community meetings to generate a set of programmatic elements and outcomes desired by stakeholders. This public input was cross-referenced with literature and best practices before it was translated into a preliminary master plan for the Community. Our research suggests that the Teacher-Family Model (TFM) where "Teaching Parents" live in group homes with six to eight children at a time is appropriate for children younger than eight at the San Damiano site. On the other hand, for the GCCC site, we recommend serving children between eight and fourteen using institutional care with evidence-based-treatments (EBT), which refer to structured interventions based on empirically-proven theories around factors disrupting adaptive functioning. Children can also elect into one of the two care programs based on personal preferences should their parental relationships influence their level of comfort with one or the other model. Finally, we normalize special education and rehabilitation by integrating them with education and recreation. This synthetic approach to programming is supported by evidence-based community planning and designing for children in care in the proposed Unity Model composed of a series of triads: 1) The play triad of education, rehabilitation, and recreation; 2) the performance triad of sleep,diet, and fitness; 3) the home-fit triad of child, caretakers, and environment; 4) the talent acquisition triad of qualification, cost, and personality; and 5) the community engagement triad of transparency, opportunity, and economy. Through leveraging existing programs and resources for rehabilitation, recreation, and education, the future phases of this project have the potential to provide job training and employment opportunities to local vulnerable populations, including female veterans and women suffering from homelessness due to post-traumatic stress disorders (PTSD) or domestic violence

Green criminology and the reconceptualization of school violence: Comparing green school violence and traditional forms of school violence for school children

School crime and violence continue to be important topics of criminological inquiry. Forms of violence that have received much attention from criminologists include school gun violence, assaults, and bullying. What appears missing from criminological studies are analyses of different forms of violent victimization imposed on school children related to environmental injustice, pollution, and exposure to toxins. In this article, we argue for the interpretation of these harms as violent victimizations. To facilitate this, we draw upon definitions of violent victimization developed in green criminology, conceptualizing exposure to environmental toxins as violent assault, and introduce the term green school violence (GSV). Next, we draw upon the medical, environmental, and public health literature to offer a series of examples of GSV in the United States, discuss numerous environmental hazards present in American schools, and describe their scope and severity. A conservative estimate of the frequency of GSV suggests that far more school children are victimized by GSV than forms of interpersonal acts of violence

Archival offender records analysis: examining patient abuses in Tennessee

This quantitative causal-comparative study was designed to examine potential relationships between independent variables (job level, dependency of patient, work environments, sex, and race) related to health care practitioner offenders and the dependent variable (types of abuse) in Tennessee from 2006 to 2015. A total of 227 practitioners who were either licensed, certified, or trained in their perspective professional practice or job level, convicted of abuse, physical/emotional abuse and financial abuse, were examined from criminal and civil dispositions. The Pearson’s Chi-square was used to evaluate the five research questions and test the null hypotheses for potential relationships. Additional testing with the Holm’s Sequential Bonferroni Method was used to control for Type I error for pairwise comparisons between variables. The chi-square results indicated strong relationships between job level, dependency of patient, and work environments with small but weak relationships for sex and race of the offenders and types of abuse. The results of this study indicated that financial abuse was prominent for all independent variables measured while physical/emotional abuse was secondary. Offenders with technical or advanced job levels committed 87.3% of financial abuse. Patients dependent on skilled care nursing were 60.7% more likely to experience physical/emotional abuse. Practitioners in private duty care committed 83.1% of financial abuse. Female offenders committed 37.1% of physical/emotional abuse compared to males who committed 75.7% of financial abuse. The findings for financial abuse was 74.0% of Caucasians offenders and 63.6% of minority offenders. The descriptive analysis examined variables relative to all offenders convicted of patient abuse, their position of professional authority and the work environments, as well as the dependency of the victims on care services

Holistic Measures for Evaluating Prediction Models in Smart Grids

The performance of prediction models is often based on "abstract metrics" that estimate the model's ability to limit residual errors between the observed and predicted values. However, meaningful evaluation and selection of prediction models for end-user domains requires holistic and application-sensitive performance measures. Inspired by energy consumption prediction models used in the emerging "big data" domain of Smart Power Grids, we propose a suite of performance measures to rationally compare models along the dimensions of scale independence, reliability, volatility and cost. We include both application independent and dependent measures, the latter parameterized to allow customization by domain experts to fit their scenario. While our measures are generalizable to other domains, we offer an empirical analysis using real energy use data for three Smart Grid applications: planning, customer education and demand response, which are relevant for energy sustainability. Our results underscore the value of the proposed measures to offer a deeper insight into models' behavior and their impact on real applications, which benefit both data mining researchers and practitioners.Comment: 14 Pages, 8 figures, Accepted and to appear in IEEE Transactions on Knowledge and Data Engineering, 2014. Authors' final version. Copyright transferred to IEE

Mining Statistical Relations for Better Decision Making in Healthcare Processes

An important part of healthcare decision making is to understand how certain actions relate to desired and undesired outcomes. One key challenge is to deal with confounding variables, i.e., variables that influence the relation between actions and outcomes. Existing techniques aim to uncover the underlying statistical relations between actions and outcomes, but either do not account for confounding variables or only consider the process or case level instead of the event level. Therefore, this paper proposes a novel relation mining approach for healthcare processes that 1) explicitly accounts for confounding variables at the event level, and 2) transparently communicates the effect of the confounding variables to the user. We demonstrate the applicability and importance of our approach using two evaluation experiments. We use a real-world healthcare dataset to show that the identified relations indeed provide important input for decision making in healthcare processes. We use a synthetic dataset to illustrate the importance of our approach in the general setting of causal model estimation

Hidden in Plain Sight: A Machine Learning Approach for Detecting Prostitution Activity in Phoenix, Arizona

Prostitution has been a topic of study for decades, yet many questions remain about where prostitution occurs. Difficulty in identifying prostitution activity is often attributed to the hidden and seemingly victimless nature of the crime. Despite numerous challenges associated with policing street prostitution, these encounters become more difficult to identify when they take place indoors, especially in locations away from public view, such as hotels. The purpose of this paper is to develop a strategy for identifying hotel facilities and surrounding areas that may be experiencing elevated levels of prostitution activity using high-volume, user-generated data, namely hotel reviews written by guests and posted to Travelocity.com. A unique synthesis of methods including data mining, natural language processing, machine learning, and basic spatial analysis are combined to identify facilities that may require additional law enforcement resources and/or social/health service outreach. Prostitution hotspots are identified within the city of Phoenix, Arizona and policy implications are discussed

Sidebar : desalination opens a new spigot for water utilities

Water-supply ; Federal Reserve District, 5th