Relearning to Live Life Without Substances: A Grounded Theory of the Impact of Group Physical Exercise on Sobriety

Research has found significant benefits in using exercise as an adjunct treatment for substance use. This study used a qualitative grounded theory approach to explore the role of group exercise on maintaining sobriety. Sixteen persons (13 White men and 3 White women, age M = 31.81, SD = 8.23), recruited from a substance use recovery group that meets at a gym in an urban area in the Southeast part of the United States, were individually interviewed. Data were analyzed using Charmaz’s (2006) constructivist grounded theory strategies. Central themes revealed a parallel process between the steps taken to seek sobriety and those taken to maintain sobriety. The driving forces of sobriety success in both was 12 steps and group exercise programming that included elements of mentorship, structure, accountability, and fellowship or community. This study supported the use of physical group exercise as an adjunct treatment option for substance use treatment.     

A Spatio-Temporal Model for Functional Magnetic Resonance Imaging Data – with a View to Resting State Networks

Abstract.  Functional magnetic resonance imaging (fMRI) is a technique for studying the active human brain. During the fMRI experiment, a sequence of MR images is obtained, where the brain is represented as a set of voxels. The data obtained are a realization of a complex spatio-temporal process with many sources of variation, both biological and technical. We present a spatio-temporal point process model approach for fMRI data where the temporal and spatial activation are modelled simultaneously. It is possible to analyse other characteristics of the data than just the locations of active brain regions, such as the interaction between the active regions. We discuss both classical statistical inference and Bayesian inference in the model. We analyse simulated data without repeated stimuli both for location of the activated regions and for interactions between the activated regions. An example of analysis of fMRI data, using this approach, is presented.     

Tests in a Case–control Design Including Relatives

Abstract.  We present a new approach to handle dependencies within the general framework of case–control designs, illustrating our approach by a particular application from the field of genetic epidemiology. The method is derived for parent–offspring trios, which will later be relaxed to more general family structures. For applications in genetic epidemiology we consider tests on equality of allele frequencies among cases and controls utilizing well-known risk measures to test for independence of phenotype and genotype at the observed locus. These test statistics are derived as functions of the entries in the associated contingency table containing the numbers of the alleles under consideration in the case and the control group. We find the joint asymptotic distribution of these entries, which enables us to derive critical values for any test constructed on this basis. A simulation study reveals the finite sample behaviour of our test statistics.     

Treatment of Depressed Women: Therapeutic Issues with Hospitalized Patients and Outpatients

Summary This paper draws on experience of treating depressed women inthe U.S.A. There is little reason to suppose, however, thatsuch experience is not applicable to the British scene. It isincluded not only for its intrinsic interest but in the hopethat it will be of particular value to British social workerscurrently assuming new responsibilities in relation to the mentallyill. Editor.     

Image‐Based Empirical Importance Sampling: An Efficient Way of Estimating Intensities

Abstract. Very recently, it has been suggested in the biomedical literature to combine computerized image analysis with non‐uniform sampling to increase the efficiency of estimators of intensities of biological cell populations. We give this ingenious idea of empirical importance sampling a stochastic formulation, using point process theory and modern sampling theory. We develop statistical tools for assessing its efficiency and construct optimal model‐based estimators of intensities. Examples of applications of empirical importance sampling in microscopy are provided.     

Reflexive Modernization at the Source: Local Media Coverage of Bovine Spongiform Encephalopathy in Rural Alberta

The potential for reflexive modernization is defined by multiple factors, but the acknowledgment of risk is crucial, particularly among social groups that play a key role in risk minimization. This study offers an examination of the role of local media in response to the outbreak of bovine spongiform encephalopathy (BSE) in beef‐producing communities in rural Alberta. BSE is one of several global risk issues that reflexive modernization theorists argue have the potential to trigger a transformation toward a critically reflexive society in which such risks are minimized. Content analysis of newspapers in beef‐producing regions in Alberta, however, shows how local media framed BSE in a manner that maximized community cohesion and protection of local culture. This selective coverage of BSE in rural Alberta is quite likely to have contributed to, or at least reinforced, support for the current institutional structure of Canadian agriculture in beef‐producing regions, through the constriction of discourse.     

Generalized Linear Latent Variable Models with Flexible Distribution of Latent Variables

Abstract. We consider a semi‐nonparametric specification for the density of latent variables in Generalized Linear Latent Variable Models (GLLVM). This specification is flexible enough to allow for an asymmetric, multi‐modal, heavy or light tailed smooth density. The degree of flexibility required by many applications of GLLVM can be achieved through this semi‐nonparametric specification with a finite number of parameters estimated by maximum likelihood. Even with this additional flexibility, we obtain an explicit expression of the likelihood for conditionally normal manifest variables. We show by simulations that the estimated density of latent variables capture the true one with good degree of accuracy and is easy to visualize. By analysing two real data sets we show that a flexible distribution of latent variables is a useful tool for exploring the adequacy of the GLLVM in practice.     

Lévy‐based Modelling in Brain Imaging

Abstract. A substantive problem in neuroscience is the lack of valid statistical methods for non‐Gaussian random fields. In the present study, we develop a flexible, yet tractable model for a random field based on kernel smoothing of a so‐called Lévy basis. The resulting field may be Gaussian, but there are many other possibilities, including random fields based on Gamma, inverse Gaussian and normal inverse Gaussian (NIG) Lévy bases. It is easy to estimate the parameters of the model and accordingly to assess by simulation the quantiles of test statistics commonly used in neuroscience. We give a concrete example of magnetic resonance imaging scans that are non‐Gaussian. For these data, simulations under the fitted models show that traditional methods based on Gaussian random field theory may leave small, but significant changes in signal level undetected, while these changes are detectable under a non‐Gaussian Lévy model.