Sociometric stability and the behavioral correlates of peer acceptance in early childhood

This paper presents findings from an Australian study examining the behavioral correlates and stability of social status for preschool-aged children. The social status of an initial sample of 187 (94 boys and 93 girls) preschool children (mean age 62.4 months, SD = 4.22) was determined through sociometric assessment. Children classified as rejected, neglected and popular (n = 70) were selected for observation. Children were observed for a total of 25 minutes over a three-month period engaging in free play within their preschool centers. Results indicated that children classified as popular were more likely than rejected or neglected children to engage in cooperative play, ongoing connected conversation and to display positive affect. Popular children were less likely than rejected or neglected children to engage in parallel play, onlooker behavior or alone directed behavior. Six months after initial sociometric classification, sociometric interviews were repeated to test for stability and change. Results indicated that preschool-aged children’s social status classifications showed a moderate to high rate of stability for those children classified as popular, rejected and neglected.

The future of cognitive and behavioral therapies in the prevention and early management of psychosis : opportunities and risks

Behavioral and cognitive interventions for people with psychosis have a long and distinguished history, although the evidence for their application to young people remains limited. We anticipate that the next decades will show substantial research into psychological intervention for this population. Important targets will include the management of environmental stressors, reduction of substance misuse, and promotion of early treatment. Psychological management of positive symptoms, depression, and suicidal behavior will continue to be critical objectives. Important secondary prevention goals will be the retention of cognitive functioning, vocational options, social skills, and social network support, including appropriate family support. We expect primary prevention to include both universal programs and interventions for adolescents at particularly high risk. Technical innovations will include increasing use of Internet-based intervention and behavior cueing devices. Pressures for intervention brevity will continue, as will problems with the systematic delivery of effective procedures.

Cognitive behavioral interviewing for adult disorders : a practical handbook

This is a guidebook for clinicians on how to conduct assessment interviews with patients presenting with common psychological disorders. The orientation is behavioural and cognitive; so the book has wide applicability, as most clinicians explicitly or implicitly accept this combination of models as a useful basis for assessing and treating these problems. The problem areas covered are: fear and anxiety problems; depression, obesity; interpersonal problems; sexual dysfunction; insomnia; headache; and substance abuse.

Latent variable models in statistical genetics

Understanding the complexities that are involved in the genetics of multifactorial diseases is still a monumental task. In addition to environmental factors that can influence the risk of disease, there is also a number of other complicating factors. Genetic variants associated with age of disease onset may be different from those variants associated with overall risk of disease, and variants may be located in positions that are not consistent with the traditional protein coding genetic paradigm. Latent Variable Models are well suited for the analysis of genetic data. A latent variable is one that we do not directly observe, but which is believed to exist or is included for computational or analytic convenience in a model. This thesis presents a mixture of methodological developments utilising latent variables, and results from case studies in genetic epidemiology and comparative genomics. Epidemiological studies have identified a number of environmental risk factors for appendicitis, but the disease aetiology of this oft thought useless vestige remains largely a mystery. The effects of smoking on other gastrointestinal disorders are well documented, and in light of this, the thesis investigates the association between smoking and appendicitis through the use of latent variables. By utilising data from a large Australian twin study questionnaire as both cohort and case-control, evidence is found for the association between tobacco smoking and appendicitis. Twin and family studies have also found evidence for the role of heredity in the risk of appendicitis. Results from previous studies are extended here to estimate the heritability of age-at-onset and account for the eect of smoking. This thesis presents a novel approach for performing a genome-wide variance components linkage analysis on transformed residuals from a Cox regression. This method finds evidence for a dierent subset of genes responsible for variation in age at onset than those associated with overall risk of appendicitis. Motivated by increasing evidence of functional activity in regions of the genome once thought of as evolutionary graveyards, this thesis develops a generalisation to the Bayesian multiple changepoint model on aligned DNA sequences for more than two species. This sensitive technique is applied to evaluating the distributions of evolutionary rates, with the finding that they are much more complex than previously apparent. We show strong evidence for at least 9 well-resolved evolutionary rate classes in an alignment of four Drosophila species and at least 7 classes in an alignment of four mammals, including human. A pattern of enrichment and depletion of genic regions in the profiled segments suggests they are functionally significant, and most likely consist of various functional classes. Furthermore, a method of incorporating alignment characteristics representative of function such as GC content and type of mutation into the segmentation model is developed within this thesis. Evidence of fine-structured segmental variation is presented.

The cost effectiveness of Naltrexone added to cognitive-behavioral therapy in the treatment of alcohol dependence

he purpose of this study was to evaluate the comparative cost of treating alcohol dependence with either cognitive behavioral therapy (CBT) alone or CBT combined with naltrexone (CBT+naltrexone). Two hundred ninety-eight outpatients dependent on alcohol who were consecutively treated for alcohol dependence participated in this study. One hundred seven (36%) patients received adjunctive pharmacotherapy (CBT+naltrexone). The Drug Abuse Treatment Cost Analysis Program was used to estimate treatment costs. Adjunctive pharmacotherapy (CBT+naltrexone) introduced an additional treatment cost and was 54% more expensive than CBT alone. When treatment abstinence rates (36.1% CBT; 62.6% CBT+naltrexone) were applied to cost effectiveness ratios, CBT+naltrexone demonstrated an advantage over CBT alone. There were no differences between groups on a preference-based health measure (SF-6D). In this treatment center, to achieve 100 abstainers over a 12-week program, 280 patients require CBT compared with 160 CBT+naltrexone. The dominant choice was CBT+naltrexone based on modest economic advantages and significant efficiencies in the numbers needed to treat.

Behavioral drivers of EIS failure

We argue that the sustained successful operation of an ES is determined by, and is dependent on, multiple organizational stakeholders. The single greatest organizational barrier to EIS success and achieving widespread organizational benefits can be attributed to the way in which different subcultures treat data critical to EIS operation. Building on Lee & Strong’s (2004) data roles we incorporate Schien’s (1996) cultural framework along with DeLone and McLean’s (2003) dimensions of IS success, unpacking the underlying drivers of behaviors as they relate to EIS data. Further, we explain the origins of data based conflict resulting in poor EIS data utilization.

A polymorphism in the dysbindin gene (DTNBP1) associated with multiple psychiatric disorders including schizophrenia

Background A number of studies have found associations between dysbindin (DTNBP1) polymorphisms and schizophrenia. Recently we identified a DTNBP1 SNP (rs9370822) that is strongly associated with schizophrenia. Individuals diagnosed with schizophrenia were nearly three times as likely to carry the CC genotype compared to the AA genotype. Methods To investigate the importance of this SNP in the function of DTNBP1, a number of psychiatric conditions including addictive behaviours and anxiety disorders were analysed for association with rs9370822. Results The DTNBP1 polymorphism was significantly associated with post-traumatic stress disorder (PTSD) as well as nicotine and opiate dependence but not alcohol dependence. Individuals suffering PTSD were more than three times as likely to carry the CC genotype compared to the AA genotype. Individuals with nicotine or opiate dependence were more than twice as likely to carry the CC genotype compared to the AA genotype. Conclusions This study provides further support for the importance of DTNBP1 in psychiatric conditions and suggests that there is a common underlying molecular defect involving DTNBP1 that contributes to the development of several anxiety and addictive disorders that are generally recognised as separate clinical conditions. These disorders may actually be different expressions of a single metabolic pathway perturbation. As our participant numbers are limited our observations should be viewed with caution until they are independently replicated.

Eigengaze : covert behavioral biometric exploiting visual attention characteristics

It is possible for the visual attention characteristics of a person to be exploited as a biometric for authentication or identification of individual viewers. The visual attention characteristics of a person can be easily monitored by tracking the gaze of a viewer during the presentation of a known or unknown visual scene. The positions and sequences of gaze locations during viewing may be determined by overt (conscious) or covert (sub-conscious) viewing behaviour. This paper presents a method to authenticate individuals using their covert viewing behaviour, thus yielding a unique behavioural biometric. A method to quantify the spatial and temporal patterns established by the viewer for their covert behaviour is proposed utilsing a principal component analysis technique called `eigenGaze'. Experimental results suggest that it is possible to capture the unique visual attention characteristics of a person to provide a simple behavioural biometric.

Bayesian methodology for genetics of complex diseases

Genetic research of complex diseases is a challenging, but exciting, area of research. The early development of the research was limited, however, until the completion of the Human Genome and HapMap projects, along with the reduction in the cost of genotyping, which paves the way for understanding the genetic composition of complex diseases. In this thesis, we focus on the statistical methods for two aspects of genetic research: phenotype definition for diseases with complex etiology and methods for identifying potentially associated Single Nucleotide Polymorphisms (SNPs) and SNP-SNP interactions. With regard to phenotype definition for diseases with complex etiology, we firstly investigated the effects of different statistical phenotyping approaches on the subsequent analysis. In light of the findings, and the difficulties in validating the estimated phenotype, we proposed two different methods for reconciling phenotypes of different models using Bayesian model averaging as a coherent mechanism for accounting for model uncertainty. In the second part of the thesis, the focus is turned to the methods for identifying associated SNPs and SNP interactions. We review the use of Bayesian logistic regression with variable selection for SNP identification and extended the model for detecting the interaction effects for population based case-control studies. In this part of study, we also develop a machine learning algorithm to cope with the large scale data analysis, namely modified Logic Regression with Genetic Program (MLR-GEP), which is then compared with the Bayesian model, Random Forests and other variants of logic regression.