Vulnerability to adverse consequences of drinking and problem drinker status as predicted by risky drinking behaviours, drug use, sex differences and affect : a test of multiple models /

Although alcohol problems and alcohol consumption are related, consumption does not fully account for differences in vulnerability to alcohol problems. Therefore, other factors should account for these differences. Based on previous research, it was hypothesized that risky drinking behaviours, illicit and prescription drug use, affect and sex differences would account for differences in vulnerability to alcohol problems while statistically controlling for overall alcohol consumption. Four models were developed that were intended to test the predictive ability of these factors, three of which tested the predictor sets separately and a fourth which tested them in a combined model. In addition, two distinct criterion variables were regressed on the predictors. One was a measure of the frequency that participants experienced negative consequences that they attributed to their drinking and the other was a measure of the extent to which participants perceived themselves to be problem drinkers. Each of the models was tested on four samples from different populations, including fIrst year university students, university students in their graduating year, a clinical sample of people in treatment for addiction, and a community sample of young adults randomly selected from the general population. Overall, support was found for each of the models and each of the predictors in accounting for differences in vulnerability to alcohol problems. In particular, the frequency with which people become intoxicated, frequency of illicit drug use and high levels of negative affect were strong and consistent predictors of vulnerability to alcohol problems across samples and criterion variables. With the exception of the clinical sample, the combined models predicted vulnerability to negative consequences better than vulnerability to problem drinker status. Among the clinical and community samples the combined model predicted problem drinker status better than in the student samples.

For the love of a barking lady: an exploration of mental illness in the light of Max Scheler's "Invisible person"

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Posttraumatic stress disorder symptoms might be destroying your intimacy : a test of mediational models in a community sample of couples

The present research focused on the pathways through which the symptoms of posttraumatic stress disorder (PTSD) may negatively impact intimacy. Previous research has confirmed a link between self-reported PTSD symptoms and intimacy; however, a thorough examination of mediating paths, partner effects, and secondary traumatization has not yet been realized. With a sample of 297 heterosexual couples, intraindividual and dyadic models were developed to explain the relationships between PTSD symptoms and intimacy in the context of interdependence theory, attachment theory, and models of selfpreservation (e.g., fight-or-flight). The current study replicated the findings of others and has supported a process in which affective (alexithymia, negative affect, positive affect) and communication (demand-withdraw behaviour, self-concealment, and constructive communication) pathways mediate the intraindividual and dyadic relationships between PTSD symptoms and intimacy. Moreover, it also found that the PTSD symptoms of each partner were significantly related; however, this was only the case for those dyads in which the partners had disclosed most everything about their traumatic experiences. As such, secondary traumatization was supported. Finally, although the overall pattern of results suggest a total negative effect of PTSD symptoms on intimacy, a sex difference was evident such that the direct effect of the woman's PTSD symptoms were positively associated with both her and her partner's intimacy. I t is possible that the Tend-andBefriend model of threat response, wherein women are said to foster social bonds in the face of distress, may account for this sex difference. Overall, however, it is clear that PTSD symptoms were negatively associated with relationship quality and attention to this impact in the development of diagnostic criteria and treatment protocols is necessary.

Automatic Inference of Graph Models for Complex Networks with Genetic Programming

Complex networks can arise naturally and spontaneously from all things that act as a part of a larger system. From the patterns of socialization between people to the way biological systems organize themselves, complex networks are ubiquitous, but are currently poorly understood. A number of algorithms, designed by humans, have been proposed to describe the organizational behaviour of real-world networks. Consequently, breakthroughs in genetics, medicine, epidemiology, neuroscience, telecommunications and the social sciences have recently resulted. The algorithms, called graph models, represent significant human effort. Deriving accurate graph models is non-trivial, time-intensive, challenging and may only yield useful results for very specific phenomena. An automated approach can greatly reduce the human effort required and if effective, provide a valuable tool for understanding the large decentralized systems of interrelated things around us. To the best of the author's knowledge this thesis proposes the first method for the automatic inference of graph models for complex networks with varied properties, with and without community structure. Furthermore, to the best of the author's knowledge it is the first application of genetic programming for the automatic inference of graph models. The system and methodology was tested against benchmark data, and was shown to be capable of reproducing close approximations to well-known algorithms designed by humans. Furthermore, when used to infer a model for real biological data the resulting model was more representative than models currently used in the literature.

Capacity to Attend to the Needs of Persons with Dual Diagnoses in the Criminal Justice System: Views of Mental Health and Criminal Justice Professionals

Currently, individuals with intellectual disabilities are overrepresented within the Criminal Justice System (Griffiths, Taillon-Wasmond & Smith, 2002). A primary problem within the Criminal Justice System is the lack of distinction between mental illness and intellectual disabilities within the Criminal Code. Due to this lack of distinction and the overall lack of identification procedures in the Criminal Justice System, individuals with disabilities will often not receive proper accommodations to enable them to play an equitable role in the justice system. There is increasing evidence that persons with intellectual disabilities are more likely than others to have their rights violated, not use court supports and accommodations as much as they should, and be subject to miscarriages of justice (Marinos, 2010). In this study, interviews were conducted with mental health (n=8) and criminal justice professionals (n=8) about how individuals with dual diagnosis are received in the Criminal Justice System. It was found that criminal justice professionals lack significant knowledge about dual diagnosis, including effective identification and therefore appropriate supports and accommodations. Justice professionals in particular were relatively ill-prepared in dealing effectively with this population. One finding to highlight is that there is misunderstanding between mental health professionals and justice professionals about who ought to take responsibility and accountability for this population.

Network Similarity Measures and Automatic Construction of Graph Models using Genetic Programming

A complex network is an abstract representation of an intricate system of interrelated elements where the patterns of connection hold significant meaning. One particular complex network is a social network whereby the vertices represent people and edges denote their daily interactions. Understanding social network dynamics can be vital to the mitigation of disease spread as these networks model the interactions, and thus avenues of spread, between individuals. To better understand complex networks, algorithms which generate graphs exhibiting observed properties of real-world networks, known as graph models, are often constructed. While various efforts to aid with the construction of graph models have been proposed using statistical and probabilistic methods, genetic programming (GP) has only recently been considered. However, determining that a graph model of a complex network accurately describes the target network(s) is not a trivial task as the graph models are often stochastic in nature and the notion of similarity is dependent upon the expected behavior of the network. This thesis examines a number of well-known network properties to determine which measures best allowed networks generated by different graph models, and thus the models themselves, to be distinguished. A proposed meta-analysis procedure was used to demonstrate how these network measures interact when used together as classifiers to determine network, and thus model, (dis)similarity. The analytical results form the basis of the fitness evaluation for a GP system used to automatically construct graph models for complex networks. The GP-based automatic inference system was used to reproduce existing, well-known graph models as well as a real-world network. Results indicated that the automatically inferred models exemplified functional similarity when compared to their respective target networks. This approach also showed promise when used to infer a model for a mammalian brain network.

Flourishing in the face of mental illness: A heuristic examination of the contribution of leisure to creating a meaningful life

The purpose of this research was to examine the ways in which individuals with mental illness create a life of purpose, satisfaction and meaning. The data supported the identification of four common themes: (1) the power of leisure in activation, (2) the power of leisure in resiliency, (3) the power of leisure in identity and (4) the power of leisure in reducing struggle. Through an exploration of the experience of having a mental illness, this project supports that leisure provides therapeutic benefits that transcend through negative life events. In addition, this project highlights the individual nature of recovery as a process of self-discovery. Through the creation of a visual model, this project provides a benchmark for how a small group of individuals have experienced living well with mental illness. As such, this work brings new thought to the growing body of mental health and leisure studies literature.

Object-Oriented Genetic Programming for the Automatic Inference of Graph Models for Complex Networks

Complex networks are systems of entities that are interconnected through meaningful relationships. The result of the relations between entities forms a structure that has a statistical complexity that is not formed by random chance. In the study of complex networks, many graph models have been proposed to model the behaviours observed. However, constructing graph models manually is tedious and problematic. Many of the models proposed in the literature have been cited as having inaccuracies with respect to the complex networks they represent. However, recently, an approach that automates the inference of graph models was proposed by Bailey [10] The proposed methodology employs genetic programming (GP) to produce graph models that approximate various properties of an exemplary graph of a targeted complex network. However, there is a great deal already known about complex networks, in general, and often specific knowledge is held about the network being modelled. The knowledge, albeit incomplete, is important in constructing a graph model. However it is difficult to incorporate such knowledge using existing GP techniques. Thus, this thesis proposes a novel GP system which can incorporate incomplete expert knowledge that assists in the evolution of a graph model. Inspired by existing graph models, an abstract graph model was developed to serve as an embryo for inferring graph models of some complex networks. The GP system and abstract model were used to reproduce well-known graph models. The results indicated that the system was able to evolve models that produced networks that had structural similarities to the networks generated by the respective target models.