Family Therapy in a Psychiatric Hospital: A Case-Controlled Evaluation

Thirty-five patients (20 women) treated by a Family Therapy Unit based in a psychiatric hospital were compared with 35 matched control inpatients who were not treated by the Unit. Family Therapy averaged 4.5 one hour sessions and was cybernetic rather than behavioural or psychoeducational. The main outcome measure was days spent in the hospital in the year after family therapy. Women, but not men, showed a significant improvement after family therapy, reducing their days in hospital from a mean of 50 in the year before family therapy to 18 in the year after. The best response was shown by women with affective disorder. Control men and women showed a slight increase in days spent in hospital. Improvement after family therapy was associated with a markedly positive change in key relatives' attitudes to the patient, and a strongly positive attitude towards family therapy.     

Temperature schedules for simulated annealing

It is well known that the behaviour of the simulated annealing approach to optimization is crucially dependent on the choice of temperature schedule. In this paper, a dynamic programming approach is used to find the temperature schedule which is optimal for a simple minimization problem. The optimal schedule is compared with certain standard non-optimal choices. These generally perform well provided the first and last temperatures are suitably selected. Indeed, these temperatures can be chosen in such a way as to make the performance of the logarithmic schedule almost optimal. This optimal performance is fairly robust to the choice of the first temperature.The dynamic programming approach cannot be applied directly to problems of more realistic size, such as those arising in statistical image reconstruction. Nevertheless, some simulation experiments suggest that the general conclusions from the simple minimization problem do carry over to larger problems. Various families of schedules can be made to perform well with suitable choice of the first and last temperatures, and the logarithmic schedule combines good performance with reasonable robustness to the choice of the first temperature.     

What Changes MSW Students' View on the Mission of Social Work?

Little is known about how professional social work education affects students' view on the social work mission. Using a secondary analysis of larger longitudinal data collected from masters in social work (MSW) students in California, USA, this study sought to explore the relationship among the students' demographic characteristics, their primary reasons for pursuing an MSW degree, and their view on the mission of social work at the time of entering an MSW program, whether the students' view on the mission of social work changed in the course of an MSW education, and factors contributing to a change of MSW students' view on the social work mission—individual adaptation vs societal change—between the beginning and the end of the MSW program. The results showed that students' degree of social action involvement, type of field practicum, and ideological variables predicted students' view changes or adherence between entering the MSW program and graduating.     

Bayesian analysis of dynamic magnetic resonance breast images

Summary.  We describe an integrated methodology for analysing dynamic magnetic resonance images of the breast. The problems that motivate this methodology arise from a collaborative study with a tumour institute. The methods are developed within the Bayesian framework and comprise image restoration and classification steps. Two different approaches are proposed for the restoration. Bayesian inference is performed by means of Markov chain Monte Carlo algorithms. We make use of a Metropolis algorithm with a specially chosen proposal distribution that performs better than more commonly used proposals. The classification step is based on a few attribute images yielded by the restoration step that describe the essential features of the contrast agent variation over time. Procedures for hyperparameter estimation are provided, so making our method automatic. The results show the potential of the methodology to extract useful information from acquired dynamic magnetic resonance imaging data about tumour morphology and internal pathophysiological features.     

Methods to test for equality of two normal distributions

Statistical tests for two independent samples under the assumption of normality are applied routinely by most practitioners of statistics. Likewise, presumably each introductory course in statistics treats some statistical procedures for two independent normal samples. Often, the classical two-sample model with equal variances is introduced, emphasizing that a test for equality of the expected values is a test for equality of both distributions as well, which is the actual goal. In a second step, usually the assumption of equal variances is discarded. The two-sample t test with Welch correction and the F test for equality of variances are introduced. The first test is solely treated as a test for the equality of central location, as well as the second as a test for the equality of scatter. Typically, there is no discussion if and to which extent testing for equality of the underlying normal distributions is possible, which is quite unsatisfactorily regarding the motivation and treatment of the situation with equal variances. It is the aim of this article to investigate the problem of testing for equality of two normal distributions, and to do so using knowledge and methods adequate to statistical practitioners as well as to students in an introductory statistics course. The power of the different tests discussed in the article is examined empirically. Finally, we apply the tests to several real data sets to illustrate their performance. In particular, we consider several data sets arising from intelligence tests since there is a large body of research supporting the existence of sex differences in mean scores or in variability in specific cognitive abilities.     

Markov Random Fields with Higher-order Interactions

Discrete-state Markov random fields on regular arrays have played a significant role in spatial statistics and image analysis. For example, they are used to represent objects against background in computer vision and pixel-based classification of a region into different crop types in remote sensing. Convenience has generally favoured formulations that involve only pairwise interactions. Such models are in themselves unrealistic and, although they often perform surprisingly well in tasks such as the restoration of degraded images, they are unsatisfactory for many other purposes. In this paper, we consider particular forms of Markov random fields that involve higher-order interactions and therefore are better able to represent the large-scale properties of typical spatial scenes. Interpretations of the parameters are given and realizations from a variety of models are produced via Markov chain Monte Carlo. Potential applications are illustrated in two examples. The first concerns Bayesian image analysis and confirms that pairwise-interaction priors may perform very poorly for image functionals such as number of objects, even when restoration apparently works well. The second example describes a model for a geological dataset and obtains maximum-likelihood parameter estimates using Markov chain Monte Carlo. Despite the complexity of the formulation, realizations of the estimated model suggest that the representation is quite realistic.     

Moderators of the relationship between daily work stressors and mood

Consistent with recent data showing a relationship between daily stress and mood, the authors investigated whether job specific factors would moderate the relationship between daily work stress and daily mood. Forty-three subjects from one teaching hospital initially completed questionnaires assessing the hypothesized moderator variables (emotional and informational support from the supervisor, job involvement, co-worker satisfaction, supervision satisfaction, and satisfaction with the meaningfulness of work). Thereafter, they completed questionnaires at the end of each workday assessing the number and negative perception of daily work stress and daily mood. After controlling for the effects of the different number of days for which data were available for each subject, the results showed that job involvement and all three facets of job satisfaction (supervision, co-worker, and work itself) moderated the relationship between the negative perception of daily work stress and mood. Neither emotional nor informational support from the supervisor moderated this relationship. Likewise, none of these variables moderated the relationship between the number of daily work events and mood. The results are discussed in terms of further research on daily work stress, and expanding and refining the focus on moderator variables.