Clinical practice and the social issues of foster care

Issues of values and commitment to children affect social provisions. A brief review of the historical background of the placement of children suggests that many of the current problems of staffing, funding and legal provisions are perpetuation of neglect of the basic needs of children. this neglect often results in a need for placement that might have been prevented by appropriate preventive supports. Lack of money is not considered the problem. Rather, political issues, values, and little commitment to children determine how the nation allocates its money.Editor of the Child & Adolescent Social Work Journal.     

Children,adolescents and social work: A statement from the editor

Current social policy affects clinical social work with children, adolescents, and their families. As a result of inadequate funding, many resources that are needed for prevention and therapeutic work are not adequate for the needs of the client group. Gaps in resources affect the welfare of children and their families and often contribute to the causes of mental health problems. Other issues related to service systems and professional arrangements affect the identity and status of clinical social workers and interfere with optimal professional activities.     

The triangle: The worker,the client,the agency

The power of the agency and its ability to enhance or detract from therapeutic work is discussed in relation to therapeutic failures. Most commonly a triangle involving the worker, the client, and the agency confound the problem. The historical base of this problem and present day influences on worker and client are reviewed. Suggestions are made for system changes to increase the power of the worker and to improve therapeutic work.This article is a revised version of a paper presented at the annual meeting of the Illinois Society of Clinical Social Work, April 25, 1981.     

Refined Inference on Long Memory in Realized Volatility

There is an emerging consensus in empirical finance that realized volatility series typically display long range dependence with a memory parameter (d) around 0.4 (Andersen et al., 2001 Andersen , T. G. , Bollerslev , T. , Diebold , F. X. , Labys , P. ( 2001 ). The distribution of realized exchange rate volatility . Journal of the American Statistical Association 96 ( 453 ): 42 – 55 .[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]; Martens et al., 2004 Martnes , M. , Van Dijk , D. , De Pooter , M. ( 2004 ). Modeling and forecasting S&P 500 volatility: Long memory, structural breaks and nonlinearity. Tinbergen Institute Discussion Paper 2004-067/4 . [Google Scholar]). The present article provides some illustrative analysis of how long memory may arise from the accumulative process underlying realized volatility. The article also uses results in Lieberman and Phillips (2004 Lieberman , O. , Phillips , P. C. B. ( 2004 ). Expansions for the distribution of the maximum likelihood estimator of the fractional difference parameter . Econometric Theory 20 ( 3 ): 464 – 484 . [Google Scholar], 2005 Lieberman , O. , Phillips , P. C. B. ( 2005 ). Expansions for approximate maximum likelihood estimators of the fractional difference parameter . The Econometrics Journal 8 : 367 – 379 . [Google Scholar]) to refine statistical inference about d by higher order theory. Standard asymptotic theory has an O(n ^?1/2) error rate for error rejection probabilities, and the theory used here refines the approximation to an error rate of o(n ^?1/2). The new formula is independent of unknown parameters, is simple to calculate and user-friendly. The method is applied to test whether the reported long memory parameter estimates of Andersen et al. (2001 Andersen , T. G. , Bollerslev , T. , Diebold , F. X. , Labys , P. ( 2001 ). The distribution of realized exchange rate volatility . Journal of the American Statistical Association 96 ( 453 ): 42 – 55 .[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) and Martens et al. (2004 Martnes , M. , Van Dijk , D. , De Pooter , M. ( 2004 ). Modeling and forecasting S&P 500 volatility: Long memory, structural breaks and nonlinearity. Tinbergen Institute Discussion Paper 2004-067/4 . [Google Scholar]) differ significantly from the lower boundary (d = 0.5) of nonstationary long memory, and generally confirms earlier findings.     

Teaching Note—Ask the Audience: Using Student Response Systems in Social Work Education

Social work educators are uniquely tasked with balancing content while helping students evaluate personal biases and develop ethical conduct necessary for social work professionalism. Social work education may benefit from technology like Student Response Systems (SRS) that allow educators to pose questions on sensitive topics in real time while simultaneously eliciting students' anonymous responses and leading discussion. This article reviews literature on SRS, also known as “clickers,” and presents findings from a survey regarding the use of SRS in a BSW-level social policy class. A model is presented to guide educators in their decisions to incorporate SRS into the classroom, and implications for social work education are discussed.     

Refined Inference on Long Memory in Realized Volatility

There is an emerging consensus in empirical finance that realized volatility series typically display long range dependence with a memory parameter (d) around 0.4 (Andersen et al., 2001; Martens et al., 2004). The present article provides some illustrative analysis of how long memory may arise from the accumulative process underlying realized volatility. The article also uses results in Lieberman and Phillips (2004, 2005) to refine statistical inference about d by higher order theory. Standard asymptotic theory has an O(n^-1/2) error rate for error rejection probabilities, and the theory used here refines the approximation to an error rate of o(n^-1/2). The new formula is independent of unknown parameters, is simple to calculate and user-friendly. The method is applied to test whether the reported long memory parameter estimates of Andersen et al. (2001) and Martens et al. (2004) differ significantly from the lower boundary (d = 0.5) of nonstationary long memory, and generally confirms earlier findings.