Deception in Family Therapy: Recognition,Implications, and Intervention

Deception, in its many forms, is present in families and relationships across the globe. The impact of the presence and discovery of family secrets or deceptive acts on the individual, the family unit, and the therapeutic process, particularly in the field of marriage and family therapy, has not been thoroughly studied or discussed. Further, deceptive acts during the therapeutic process may have implications for effective treatment planning. This article defines the various forms of deception and its systemic effects on relationships and on the therapeutic process. Possible interventions to address deception in a family therapeutic setting are proposed.     

Teachers’ Perceptions of Childcare and Preschool Expulsion

Previous research outlines the correlates of childcare expulsion in the USA, yet researchers know little about how these correlates produce expulsion. This in‐depth qualitative analysis of 30 childcare providers’ accounts of expulsion finds a patterned process to expulsion: Teachers search for causes and solutions to challenging behaviours. When interventions fail, overwhelmed teachers shift their focus from ‘struggling’ children to ‘bad families’. Once the explanation of behaviour changes from within to outside of the child, expulsion is imminent. Interventions in teachers’ understandings, not only in children's behaviours, are discussed as a possible way to reduce expulsion.     

Deaf access to justice in Northern Ireland: rethinking ‘Reasonable Adjustment’ in the Disability Discrimination Act

Abstract

This article provides findings of a qualitative study exploring the interactions of eight Deaf participants and one hearing ally with the justice system in Northern Ireland, where the Disability Discrimination Act requires solicitors to make ‘reasonable adjustments’ in order to provide effective access to Deaf clients. Three thematic categories emerged: (a) barriers to accessing justice, (b) work Deaf people do for access, and (c) the need to educate solicitors about access. A central strain ran through these themes: the idea that ‘reasonable adjustment’ must reflect the value of sign language interpreters in facilitating effective communication access for all the parties.     

A qualitative investigation of risk and protective factors for interpersonal violence in adults on the autism spectrum

Abstract

Individuals on the autism spectrum are at an increased risk of experiencing violence. This study sought to understand the opinions of adults on the spectrum regarding potential risk and protective factors for interpersonal violence. Twenty-two adults (12 men; 18 to 53 years of age) participated in semi-structured qualitative interviews. Interviews were recorded, transcribed, and analysed. The results of the qualitative analysis identified individual and contextual risk and protective factors. Protective themes focused on individual needs (awareness of the self and the environment, interpersonal knowledge/skills, skill building strategies) and supportive contexts (benefits of a trusted person, advocates, fostering acceptance and inclusion). There is a need for strategies to decrease the vulnerability to, and the impact of, interpersonal violence. Individuals on the spectrum have the capacity to provide important information regarding safety promotion and risk factors for interpersonal violence, and their expertise should play a role in guiding efforts that promote safety.     

On pseudo-values for regression analysis in competing risks models

For regression on state and transition probabilities in multi-state models Andersen et al. (Biometrika 90:15–27, 2003) propose a technique based on jackknife pseudo-values. In this article we analyze the pseudo-values suggested for competing risks models and prove some conjectures regarding their asymptotics (Klein and Andersen, Biometrics 61:223–229, 2005). The key is a second order von Mises expansion of the Aalen-Johansen estimator which yields an appropriate representation of the pseudo-values. The method is illustrated with data from a clinical study on total joint replacement. In the application we consider for comparison the estimates obtained with the Fine and Gray approach (J Am Stat Assoc 94:496–509, 1999) and also time-dependent solutions of pseudo-value regression equations.     

SEMIPARAMETRIC REGRESSION AND GRAPHICAL MODELS

Semiparametric regression models that use spline basis functions with penalization have graphical model representations. This link is more powerful than previously established mixed model representations of semiparametric regression, as a larger class of models can be accommodated. Complications such as missingness and measurement error are more naturally handled within the graphical model architecture. Directed acyclic graphs, also known as Bayesian networks, play a prominent role. Graphical model-based Bayesian 'inference engines', such as bugs and vibes , facilitate fitting and inference. Underlying these are Markov chain Monte Carlo schemes and recent developments in variational approximation theory and methodology.     

VARIATIONAL BAYESIAN ANALYSIS FOR HIDDEN MARKOV MODELS

The variational approach to Bayesian inference enables simultaneous estimation of model parameters and model complexity. An interesting feature of this approach is that it also leads to an automatic choice of model complexity. Empirical results from the analysis of hidden Markov models with Gaussian observation densities illustrate this. If the variational algorithm is initialized with a large number of hidden states, redundant states are eliminated as the method converges to a solution, thereby leading to a selection of the number of hidden states. In addition, through the use of a variational approximation, the deviance information criterion for Bayesian model selection can be extended to the hidden Markov model framework. Calculation of the deviance information criterion provides a further tool for model selection, which can be used in conjunction with the variational approach.     

The generalized log-gamma mixture model with covariates: local influence and residual analysis

In a sample of censored survival times, the presence of an immune proportion of individuals who are not subject to death, failure or relapse, may be indicated by a relatively high number of individuals with large censored survival times. In this paper the generalized log-gamma model is modified for the possibility that long-term survivors may be present in the data. The model attempts to separately estimate the effects of covariates on the surviving fraction, that is, the proportion of the population for which the event never occurs. The logistic function is used for the regression model of the surviving fraction. Inference for the model parameters is considered via maximum likelihood. Some influence methods, such as the local influence and total local influence of an individual are derived, analyzed and discussed. Finally, a data set from the medical area is analyzed under the log-gamma generalized mixture model. A residual analysis is performed in order to select an appropriate model. The authors would like to thank the editor and referees for their helpful comments. This work was supported by CNPq, Brazil.