CMHS/CSAT Collaborative Demonstration Program for Homeless Individuals

The Collaborative Demonstration Program for Homeless Individuals is providing an opportunity for several community agencies in the United States to explore innovative intervention and treatment strategies to address the needs of homeless people with co-occurring (mental illness and substance abuse) disorders. This cooperative agreement, funded jointly by the Center for Mental Health Services (CMHS) and the Center for Substance Abuse Treatment (CSAT), is a $3 million project supporting programs in various community agencies to develop, document, and evaluate programs for persons who are homeless with co-occurring disorders to be used as models in other communities. The program models include: Assertive Community Treatment, Psychosocial Rehabilitation, Therapeutic Community, and an integrated treatment model. The first phase of the two-phase project funded 16 programs in the U.S. to develop manuals within their primary treatment modalities for homeless people with co-occurring disorders. For Phase II of the project, six of the 16 model programs were competitively selected to demonstrate program impact through formal program evaluation efforts. The data derived from the various projects will be compiled into a compendium of information for state agencies and community service providers throughout the U.S.     

Choice of the ridge factor from the correlation matrix determinant

Ridge regression is the alternative method to ordinary least squares, which is mostly applied when a multiple linear regression model presents a worrying degree of collinearity. A relevant topic in ridge regression is the selection of the ridge parameter, and different proposals have been presented in the scientific literature. Since the ridge estimator is biased, its estimation is normally based on the calculation of the mean square error (MSE) without considering (to the best of our knowledge) whether the proposed value for the ridge parameter really mitigates the collinearity. With this goal and different simulations, this paper proposes to estimate the ridge parameter from the determinant of the matrix of correlation of the data, which verifies that the variance inflation factor (VIF) is lower than the traditionally established threshold. The possible relation between the VIF and the determinant of the matrix of correlation is also analysed. Finally, the contribution is illustrated with three real examples.     

Multivariate regression analysis of panel data with binary outcomes applied to unemployment data

Summary  In panel studies binary outcome measures together with time stationary and time varying explanatory variables are collected over time on the same individual. Therefore, a regression analysis for this type of data must allow for the correlation among the outcomes of an individual. The multivariate probit model of Ashford and Sowden (1970) was the first regression model for multivariate binary responses. However, a likelihood analysis of the multivariate probit model with general correlation structure for higher dimensions is intractable due to the maximization over high dimensional integrals thus severely restricting ist applicability so far. Czado (1996) developed a Markov Chain Monte Carlo (MCMC) algorithm to overcome this difficulty. In this paper we present an application of this algorithm to unemployment data from the Panel Study of Income Dynamics involving 11 waves of the panel study. In addition we adapt Bayesian model checking techniques based on the posterior predictive distribution (see for example Gelman et al. (1996)) for the multivariate probit model. These help to identify mean and correlation specification which fit the data well. C. Czado was supported by research grant OGP0089858 of the Natural Sciences and Engineering Research Council of Canada.     

Posterior consistency for the spectral density of non-Gaussian stationary time series

Various nonparametric approaches for Bayesian spectral density estimation of stationary time series have been suggested in the literature, mostly based on the Whittle likelihood approximation. A generalization of this approximation involving a nonparametric correction of a parametric likelihood has been proposed in the literature with a proof of posterior consistency for spectral density estimation in combination with the Bernstein–Dirichlet process prior for Gaussian time series. In this article, we will extend the posterior consistency result to non-Gaussian time series by employing a general consistency theorem for dependent data and misspecified models. As a special case, posterior consistency for the spectral density under the Whittle likelihood is also extended to non-Gaussian time series. Small sample properties of this approach are illustrated with several examples of non-Gaussian time series.     

Working and caring for a disabled adopted child during a pandemic

Integrating work and family demands can be challenging for families caring for a child with one or more disabilities. The pandemic and its changes to work, schooling and service delivery potentially added to these challenges. This exploratory mixed methods study sought to understand how the pandemic affected adoptive parents' work–life fit and service use. A total of 200 participants responded to survey questions about parenting an adopted child with a disability prior to, and after, the onset of Covid-19. More than half of the parents (59.2%) reported that it was somewhat to very difficult to integrate both work and family demands. Parents with greater access to workplace flexibility and supportive supervisors had significantly less difficulties combining work and family. Families who reported more problems with accessing mental health services, special education and respite care reported significantly more challenges with work–family fit. Parents reported increased stress due to the pandemic changes, but many also shared positive changes such as more time for family. Online services were experienced as effective for some children and reduced time spent driving to appointments. Recommendations for workplace and social service practice and policy supporting adoptive parents of children with disabilities are discussed.     

Serious case reviews: The lived experience of Black children

Despite the many high‐profile Black child deaths in England, race as a factor remains a largely underexplored factor of serious case reviews (SCRs). Evidence from analysis of SCRs indicates that race receives limited attention, or is virtually absent. Given that the main function of SCRs is to provide opportunities for learning lessons to improve practice, the way in which issues of race and culture may influence child protection processes for Black children is therefore of critical importance. In this article, we employ content analysis to examine the extent that race and cultural factors are considered in SCRs involving Black children. It is argued that race is often an important factor influencing Black children's experiences of abuse and neglect, as well as their encounters in the child protection system. This article therefore poses two key questions: (a) What questions are asked about race, ethnicity, and culture in SCRs concerning Black children? (b) How did the SCRs extract lessons to be learnt for improving practice to safeguard Black children? By extending the analysis of race and ethnicity in SCRs, this article furthers our understandings of the needs of Black children in the child protection system.