Qualitative and quantitative research methods were used to study instances of sudden gains within the case load of a private practice practitioner. Five clients whose progress was marked by such changes were contrasted with the views of five clients whose progress was marked by significant setbacks. Results from the quantitative analyses indicated that clients who experienced sudden gains during therapy tended to retain their therapeutic gains over a 2-year time period. In contrast, individuals who experienced setbacks in therapy generally continued to be distressed at the 2-year reassessment. Clients who experienced sudden gains were more distressed prior to treatment and were more satisfied with their experience looking back. A stronger working alliance was found amongst those who experienced sudden gains, although there was no difference between the groups' ratings regarding the strength of the therapeutic bond. Qualitative results suggested that therapy was helpful in bringing about many changes in clients' lives, but clients who experienced sudden gains generally recalled more positive aspects of therapy, demonstrated greater utilization of therapeutic techniques, endorsed more long-term changes, accepted more responsibility for their treatment outcomes, and were less likely to react negatively to therapeutic techniques. Clients who experienced setbacks in therapy were generally less optimistic about the future, felt that they had regressed since termination, and demonstrated more resistance to therapeutic techniques.

Submitted in fulfillment of the requirements for the Degree in Masters of Technology in Nursing, Durban University of Technology, Durban, South Africa, 2015. / Background The current health care system in South Africa and its diverse settings of health care delivery system require a nurse who can make decisions, think critically, solve problems and work effectively in a team. Traditional nursing education teaching strategies have over the years relied on didactic and often passive approaches to learning. In pursuit of quality, academics and students must be continually engaged in a process of finding opportunities for improving the teaching and learning process. Purpose of the study The purpose of this study was to evaluate the structure and the process in case based learning at the University of Technology. Methodology This study is qualitative in nature, governed by an interpretive paradigm. This is a case study, which enabled the researcher to merge student interview data with records in order to gain insight into the activities and details of case based learning as practised at the University of Technology under study. Most importantly, the case study method was deemed appropriate for the current study, since case-based learning as a pedagogical approach (and a case) cannot be abstracted from its context for the purposes of study. Case based learning is evaluated in its context namely, the undergraduate nursing programme, using the Donabedian framework of structure, process and product. Results The study recorded that students were positive towards case based learning though some identified dynamics of working in groups as demerits of case based learning. The structures that are in place in the programme and the CBL processes are adequate and support CBL. There are however areas that need attention such as the qualification of the programme coordinator, the size of the class-rooms and the service of the computer laboratory. Conclusion The study found that apart from a few minor discrepancies, case based learning is sufficiently implemented, and experienced as invaluable by students, at the University of Technology under study.

Este trabalho apresenta a formulação de um sistema híbrido de apoio à decisão que, através de técnicas adaptativas, permite que múltiplos dispositivos sejam utilizados de forma colaborativa para encontrar uma solução para um problema de tomada de decisão. É proposta uma estratégia particular para o trabalho colaborativo que restringe o acesso aos dispositivos mais lentos com base na dificuldade encontrada pelos dispositivos mais rápidos para solucionar um problema específico. As soluções encontradas por cada dispositivo são propagadas aos demais, permitindo que cada um deles agregue estas novas soluções com o auxílio de técnicas adaptativas. É feito um estudo sobre aprendizagem de máquina mediante incertezas para verificar e minimizar os impactos negativos que uma nova solução, possivelmente errônea, possa ter. O sistema híbrido proposto é apresentado numa aplicação particular, utilizando testes padronizados para compará-lo com os dispositivos individuais que o compõem e com sistemas híbridos de mesma finalidade. Através destes testes, é mostrado que dispositivos consolidados, mesmo que de naturezas distintas, podem ser utilizados de maneira colaborativa, permitindo não só calibrar um compromisso entre o tempo de resposta e a taxa de acerto, mas também evoluir de acordo com o histórico de problemas processados. / This work presents a formulation of a hybrid decision-making system that employs adaptive techniques as a way to coordinate multiple devices in order to make a collaborative decision. The strategy proposed here is to restrict the use of slower devices, based on how difficult the specific problem is - easier problems may be solved on faster devices. Each device is able to learn through solutions given by the others, aggregating new knowledge with the aid of adaptive techniques. In order to evaluate and minimize the negative impact those new solutions may have, a study concerning machine learning under uncertainty is carried out. A particular application of this system has been tested and compared, not only to each individual device that is part of the system itself, but to similar hybrid systems as well. It is shown that even devices of distinct natures may be reused in a collaborative manner, making it possible to calibrate the trade-off between hit rate and response time, and to evolve according to the input stimuli received as well.

Este trabalho apresenta a formulação de um sistema híbrido de apoio à decisão que, através de técnicas adaptativas, permite que múltiplos dispositivos sejam utilizados de forma colaborativa para encontrar uma solução para um problema de tomada de decisão. É proposta uma estratégia particular para o trabalho colaborativo que restringe o acesso aos dispositivos mais lentos com base na dificuldade encontrada pelos dispositivos mais rápidos para solucionar um problema específico. As soluções encontradas por cada dispositivo são propagadas aos demais, permitindo que cada um deles agregue estas novas soluções com o auxílio de técnicas adaptativas. É feito um estudo sobre aprendizagem de máquina mediante incertezas para verificar e minimizar os impactos negativos que uma nova solução, possivelmente errônea, possa ter. O sistema híbrido proposto é apresentado numa aplicação particular, utilizando testes padronizados para compará-lo com os dispositivos individuais que o compõem e com sistemas híbridos de mesma finalidade. Através destes testes, é mostrado que dispositivos consolidados, mesmo que de naturezas distintas, podem ser utilizados de maneira colaborativa, permitindo não só calibrar um compromisso entre o tempo de resposta e a taxa de acerto, mas também evoluir de acordo com o histórico de problemas processados. / This work presents a formulation of a hybrid decision-making system that employs adaptive techniques as a way to coordinate multiple devices in order to make a collaborative decision. The strategy proposed here is to restrict the use of slower devices, based on how difficult the specific problem is - easier problems may be solved on faster devices. Each device is able to learn through solutions given by the others, aggregating new knowledge with the aid of adaptive techniques. In order to evaluate and minimize the negative impact those new solutions may have, a study concerning machine learning under uncertainty is carried out. A particular application of this system has been tested and compared, not only to each individual device that is part of the system itself, but to similar hybrid systems as well. It is shown that even devices of distinct natures may be reused in a collaborative manner, making it possible to calibrate the trade-off between hit rate and response time, and to evolve according to the input stimuli received as well.

Dem Web 2.0 werden - nicht selten mit euphorischem Unterton - hinsichtlich Interaktion, Selbstorganisation und Nutzbarmachung kollektiver Intelligenz enorme Nutzenpotentiale nachgesagt. Ansätze mit Bezug zum Unternehmenskontext werden unter dem Stichwort Enterprise 2.0 behandelt und beschäftigen sich vorrangig mit der Unterstützung des betrieblichen Wissensmanagements. Speziell für die zunehmend durch Komplexität sowie intensive Interaktionsprozesse geprägte Fabrikplanung lassen sich durch einen zielgerichteten Einsatz von Web 2.0-Werkzeugen positive Effekte erwarten. Zielstellung dieser Arbeit ist die Entwicklung einer Methode zum Einsatz von Web 2.0-Werkzeugen in der Fabrikplanung. Hierfür erfolgt zunächst eine Bestandsaufnahme relevanter Ansätze und Begriffe in diesen Bereichen. Anschließend wird auf Grundlage system-, handlungs- und tätigkeitstheoretischer Überlegungen ein situativer Forschungsansatz begründet. Die Methodenentwicklung erfolgt als problemspezifische Ausgestaltung des Fall-basierten Schließens. Sie ist in ein entsprechend angepasstes Vorgehen der morphologisch-typologischen Theorieentwicklung eingebettet und basiert auf einer umfassenden Analyse hierfür relevanter Theorien, Modelle und Ansätze. Die Methode beruht auf einer kontinuierlichen Erfassung und Wiederverwendung von Erfahrungswissen. Sie wird abschließend evaluiert, wobei u.a. ein Prototyp entwickelt wird, der den praktischen Einsatz der entwickelten Methode unterstützt. / The Web 2.0 is supposed to have huge potential for the support of interaction, selforganization and the utilization of collective intelligence. Approaches related to an enterprise context are discussed with the keyword Enterprise 2.0 and mainly deal with potentials to support the operational knowledge management. A systematic approach for the use of web-based collaborative tools is expected to generate positive effects on modern factory planning, which faces increasing complexity and dynamic interactions. The objective of this work is to develop a methodical approach for the use of web-based collaborative tools in factory planning. Therefore, in the first part of this thesis an overview of relevant approaches and terms in the areas of Web 2.0 and factory planning is being worked out. In a second step, a situational approach is identified as an appropriate view after due consideration and contextual discussion of system, action and activity theory. The development of the methodical approach is based on a problem-specific adaptation of case-based reasoning. It is embedded into an elaborated procedure of morphologic-typological theory building and bases on a comprehensive analysis of relevant theories, models and approaches. The evolved method relies on continuous collection and reutilisation of experiential knowledge. It is evaluated through different methods, inter alia by the construction of a prototype that supports its practical use.

This was a retrospective study of a community-based methadone maintenance treatment (MMT) program in Toronto. Participants (N = 170) were federally sentenced adult male offenders admitted to this voluntary program between 1997 and 2009 while subject to community supervision following incarceration. The primary investigation examined correlates of treatment responsivity, with principal outcome measures including MMT clients’ rates of: (i) illicit drug use; and (ii) completion of conditional (parole) or statutory release (SR). For a subset (n = 74), recidivism rates were examined after a 9-year interval. Findings included strong convergent evidence from logistic regression and ROC analyses that an empirically and theoretically derived set of five variables was a stable and highly significant (p <.001) predictor of release outcome. Using five factors related to risk (work/school status, security level of releasing institution, total PCL-R score, history of institutional drug use, and days at risk), release outcome was predicted with an overall classification accuracy of 88%, with high specificity (86%) and sensitivity (89%). The logistic regression model generated an R2 of .55 and the accompanying AUC was .89, both substantial. Work/school status had an extremely large positive association with successful completion of community supervision, accounting for > half of the total variance explained by the five-factor model and increasing the estimated odds of successful release outcome by > 15-fold. Also, when in the MMT program, clients' risk taking behaviour was significantly moderated, with low overall base rates of illicit drug use, yet the rate of parole/SR revocation (71%) was high. The 9-year follow-up showed a high mortality rate (15%) overall. Revocation of release while in the MMT program was associated with a significantly higher rate and more violent recidivism at follow-up. Results are discussed within the context of: (a) Andrews' and Bonta's psychology of criminal conduct; (b) the incompatibility of a harm reduction treatment model with an abstinence-based parole decision-making model; (c) changing drug use profiles among MMT clients; (d) a strength-based approach to correctional intervention focusing on educational and vocational retraining initiatives; and (e) creation of a user friendly case-based screening algorithm for prediction of release outcome for new releases.

This was a retrospective study of a community-based methadone maintenance treatment (MMT) program in Toronto. Participants (N = 170) were federally sentenced adult male offenders admitted to this voluntary program between 1997 and 2009 while subject to community supervision following incarceration. The primary investigation examined correlates of treatment responsivity, with principal outcome measures including MMT clients’ rates of: (i) illicit drug use; and (ii) completion of conditional (parole) or statutory release (SR). For a subset (n = 74), recidivism rates were examined after a 9-year interval. Findings included strong convergent evidence from logistic regression and ROC analyses that an empirically and theoretically derived set of five variables was a stable and highly significant (p <.001) predictor of release outcome. Using five factors related to risk (work/school status, security level of releasing institution, total PCL-R score, history of institutional drug use, and days at risk), release outcome was predicted with an overall classification accuracy of 88%, with high specificity (86%) and sensitivity (89%). The logistic regression model generated an R2 of .55 and the accompanying AUC was .89, both substantial. Work/school status had an extremely large positive association with successful completion of community supervision, accounting for > half of the total variance explained by the five-factor model and increasing the estimated odds of successful release outcome by > 15-fold. Also, when in the MMT program, clients' risk taking behaviour was significantly moderated, with low overall base rates of illicit drug use, yet the rate of parole/SR revocation (71%) was high. The 9-year follow-up showed a high mortality rate (15%) overall. Revocation of release while in the MMT program was associated with a significantly higher rate and more violent recidivism at follow-up. Results are discussed within the context of: (a) Andrews' and Bonta's psychology of criminal conduct; (b) the incompatibility of a harm reduction treatment model with an abstinence-based parole decision-making model; (c) changing drug use profiles among MMT clients; (d) a strength-based approach to correctional intervention focusing on educational and vocational retraining initiatives; and (e) creation of a user friendly case-based screening algorithm for prediction of release outcome for new releases.

Healthcare professionals such as speech and language therapists are expected to change their practice throughout their career. However, from a practice perspective, there is a lack of knowledge around what practice change is, what it really takes, and why there are different trajectories. Consequently, therapists, managers and commissioners lack empirical evidence on which to base decisions about enabling practice change. In addition, intervention researchers lack basic sociological research around implementation that could inform their research designs, reporting and impact. This case-based sociological inquiry, underpinned by critical realist assumptions, was designed to address this knowledge gap. It includes a two-stage qualitative synthesis of 53 (then 16) studies where speech and language therapists explained the work of their practice in depth, and a primary qualitative study focused on one professional jurisdiction, children with speech sound difficulties (SSD). Forty two speech and language therapists from three NHS areas and independent practice in Scotland participated in individual interviews or self-organised pairs or focus groups to discuss in depth how and why they had changed their practice with these children. A variety of comparative methods were used to detail, understand and explain this particular aspect of the social world. The resulting theory of SSD practice change comprises six configured cases of practice change (Transforming; Redistributing; Venturing; Personalising; Delegating; Refining) emerging from an evolving and modifiable practice context. The work that had happened across four key aspects of this context (Intervention; Candidacy; Caseload; Service) explained what made each case possible, and how practice had come to be one way rather than another. Among its practical applications, the theory could help services plan more realistic practice change. In addition, the inductively developed layered model of SSD intervention change has the potential to contribute to speech and language therapy education as well as methodological discussions around complex interventions.

O presente trabalho apresenta um estudo feito para a elaboração de um modelo de análise de crédito para micro, pequenas e médias empresas (MPME) utilizando Inteligência Artificial. Apresenta, também, uma contribuição de um novo método de raciocínio baseado em casos, denominado FISKNN, que utiliza medida de similaridade presente nos métodos KNN e KNN-Fuzzy, e um sistema de inferência Fuzzy para decidir se a classe de um determinado caso é a classe do elemento mais próximo ou a classe da maioria dos K elementos selecionados para análise. Compara-se o método FISKNN com os métodos tradicionais KNN e KNN-Fuzzy utilizando os dados do Machine Learning Repository da Universidade da Califórnia, e apresentam-se três estudos de casos com bases de dados selecionadas das informações provenientes de solicitações de financiamento através do Cartão BNDES. / This work presents an investigation of a model of credit analysis for micro, small and medium size enterprises based on artificial intelligence techniques. The novelty is a cases-based reasoning, denoted by FISKNN, which uses a measure of similarity present in the KNN and KNN-Fuzzy methods, and a Fuzzy Inference System to decide between the class of the nearest case and the class of the majority of K elements selected for the analysis. One compares the FISKNN methods with the more traditional ones, KNN and KNNFuzzy, using data from the Machine Learning Repository of the University of California, and one presents three study cases with data bases selected from the set of financing applications to the BNDES Card.

O presente trabalho apresenta um estudo feito para a elaboração de um modelo de análise de crédito para micro, pequenas e médias empresas (MPME) utilizando Inteligência Artificial. Apresenta, também, uma contribuição de um novo método de raciocínio baseado em casos, denominado FISKNN, que utiliza medida de similaridade presente nos métodos KNN e KNN-Fuzzy, e um sistema de inferência Fuzzy para decidir se a classe de um determinado caso é a classe do elemento mais próximo ou a classe da maioria dos K elementos selecionados para análise. Compara-se o método FISKNN com os métodos tradicionais KNN e KNN-Fuzzy utilizando os dados do Machine Learning Repository da Universidade da Califórnia, e apresentam-se três estudos de casos com bases de dados selecionadas das informações provenientes de solicitações de financiamento através do Cartão BNDES. / This work presents an investigation of a model of credit analysis for micro, small and medium size enterprises based on artificial intelligence techniques. The novelty is a cases-based reasoning, denoted by FISKNN, which uses a measure of similarity present in the KNN and KNN-Fuzzy methods, and a Fuzzy Inference System to decide between the class of the nearest case and the class of the majority of K elements selected for the analysis. One compares the FISKNN methods with the more traditional ones, KNN and KNNFuzzy, using data from the Machine Learning Repository of the University of California, and one presents three study cases with data bases selected from the set of financing applications to the BNDES Card.