The purpose of the present study were to (a) explore the relationship between the frequency of use of Rapid Response Teams (RRTs) by hospital staff nurses and the support received from RRTs; (b) to investigate staff nurses’ perceptions of their individual level, group level and organizational level learning as a result of single or multiple exposures to the RRT; (c) to identify predictors of learning outcomes and (d) to identify overall impressions and advantages and disadvantages of the RRT. A mail survey was used to collect data. The response responses rate was 33%, 131 registered nurses responded to the survey (pre-test = 12, study = 119). The results of Pearson r correlation suggest that a high frequency of access of RRTs was positively related to process support (r = .25, p < .01). Also, perceived content and process support from RRTs was positively related to maintenance and building of staff nurses’ mental models regarding patient deterioration pertaining to self, group and organization. Multiple regression analyses show that sociodemographic and independent variables predict organizational learning outcomes (mental model maintenance and building). Overall impressions of the RRTs were high. A content analysis of nurses’ comments indicated that there were more advantages to having the RRTs than disadvantages. This study suggests that RRTs are influential in changing nurses’ perceptions about managing patient deterioration. Training programs for RRTs should include both content and process support, which may enhance building and maintaining mental models. / Thesis (Master, Nursing) -- Queen's University, 2008-09-25 21:27:44.682

Voluntary and community organisations are fundamental to society because they are major stakeholders in building the social capital that underpins healthy and well-functioning communities. Yet many of these organisations are small and possess limited resources when measured against the challenges and needs that they address. This raises the issue of the capacity of organisations within the sector to operate effectively. Within the range of capacity issues, governance is consistently rated as an area requiring development. This research seeks to contribute to a better understanding of issues impacting on the governance capacity of voluntary and community sector organisations within the overall context of capacity building. A focused study in the Taranaki region examines the factors impacting on the governance of community organisations providing social services. The research identifies the level of governance capacity demonstrated within the organisations studied and also explores the level of awareness around the need to enhance governance capacity. The research examines a range of frameworks and models used to build governance capacity to see if they could be adapted for the Taranaki region. The study suggests that, although a number of frameworks and models are useful, every situation is different, and models must be responsive to the social and cultural context and the particular history and mission of each organisation. As a result, the study concludes that further work should be undertaken to develop a model of governance for the voluntary and community sector.

Institutional and epistemological differences between science and management present a challenge to the implementation of sustainable environmental management. Environmental problems are complex and require at least multidisciplinary, but most effectively transdisciplinary approaches for learning, understanding, decision-making and problem solving. This means building bridges between institutional and epistemological differences. The role of system dynamics modelling in integrating environmental science and management is examined in this thesis. An action research methodology is adopted where, over cycles of case studies, the practical application of system dynamics modelling is evaluated. The role of system dynamics modelling in the management of coastal sand dunes, tourism, threatened species management and water management is explored in the case studies. It has been found that system dynamics modelling is a potentially powerful tool for integrating environmental science and management, principally assisting communication between scientists and management stakeholders. System dynamics group model-building, in particular, has the potential to facilitate stakeholder learning and assist stakeholders to think holistically about the complex systems they are trying to manage. It was also found that engaging stakeholders in system dynamics group model-building process is difficult. A model of factors influencing the adoption of system dynamics group model-building has been developed from this research. The model can be applied to assess the suitability of potential case studies and identify potential weaknesses that need to be addressed if the approach is to succeed.

Voluntary and community organisations are fundamental to society because they are major stakeholders in building the social capital that underpins healthy and well-functioning communities. Yet many of these organisations are small and possess limited resources when measured against the challenges and needs that they address. This raises the issue of the capacity of organisations within the sector to operate effectively. Within the range of capacity issues, governance is consistently rated as an area requiring development. This research seeks to contribute to a better understanding of issues impacting on the governance capacity of voluntary and community sector organisations within the overall context of capacity building. A focused study in the Taranaki region examines the factors impacting on the governance of community organisations providing social services. The research identifies the level of governance capacity demonstrated within the organisations studied and also explores the level of awareness around the need to enhance governance capacity. The research examines a range of frameworks and models used to build governance capacity to see if they could be adapted for the Taranaki region. The study suggests that, although a number of frameworks and models are useful, every situation is different, and models must be responsive to the social and cultural context and the particular history and mission of each organisation. As a result, the study concludes that further work should be undertaken to develop a model of governance for the voluntary and community sector.

This thesis develops a method for automatically constructing, visualizing and describing a large class of models, useful for forecasting and finding structure in domains such as time series, geological formations, and physical dynamics. These models, based on Gaussian processes, can capture many types of statistical structure, such as periodicity, changepoints, additivity, and symmetries. Such structure can be encoded through kernels, which have historically been hand-chosen by experts. We show how to automate this task, creating a system that explores an open-ended space of models and reports the structures discovered. To automatically construct Gaussian process models, we search over sums and products of kernels, maximizing the approximate marginal likelihood. We show how any model in this class can be automatically decomposed into qualitatively different parts, and how each component can be visualized and described through text. We combine these results into a procedure that, given a dataset, automatically constructs a model along with a detailed report containing plots and generated text that illustrate the structure discovered in the data. The introductory chapters contain a tutorial showing how to express many types of structure through kernels, and how adding and multiplying different kernels combines their properties. Examples also show how symmetric kernels can produce priors over topological manifolds such as cylinders, toruses, and Möbius strips, as well as their higher-dimensional generalizations. This thesis also explores several extensions to Gaussian process models. First, building on existing work that relates Gaussian processes and neural nets, we analyze natural extensions of these models to deep kernels and deep Gaussian processes. Second, we examine additive Gaussian processes, showing their relation to the regularization method of dropout. Third, we combine Gaussian processes with the Dirichlet process to produce the warped mixture model: a Bayesian clustering model having nonparametric cluster shapes, and a corresponding latent space in which each cluster has an interpretable parametric form.

This diploma thesis is about measuring of family house, creating drawings and visualization of results. The family house is located in the Brno-Žabovřesky The house was surveyed by the classical geodetic method using a survey net built using GNSS (Global Navigation Satellite System). The results of field measurements were processed in the Groma program, graphic outputs (footprints of individual floors, sections and 3D model) in the MicroStation application. The documentation can be used mainly for the planned reconstruction of the family house and other related purposes.

京都大学 / 0048 / 新制・課程博士 / 博士(エネルギー科学) / 甲第20729号 / エネ博第357号 / 新制||エネ||70(附属図書館) / 京都大学大学院エネルギー科学研究科エネルギー基礎科学専攻 / (主査)教授 片平 正人, 教授 森井 孝, 教授 木下 正弘 / 学位規則第4条第1項該当 / Doctor of Energy Science / Kyoto University / DGAM

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The standard methodology when building statistical models has been to use one of several algorithms to systematically search the model space for a good model. If the number of variables is small then all possible models or best subset procedures may be used, but for data sets with a large number of variables, a stepwise procedure is usually implemented. The stepwise procedure of model selection was designed for its computational efficiency and is not guaranteed to find the best model with respect to any optimality criteria. While the model selected may not be the best possible of those in the model space, commonly it is almost as good as the best model. Many times there will be several models that exist that may be competitors of the best model in terms of the selection criterion, but classical model building dictates that a single model be chosen to the exclusion of all others. An alternative to this is Bayesian model averaging (BMA), which uses the information from all models based on how well each is supported by the data. Using BMA allows a variance component due to the uncertainty of the model selection process to be estimated. The variance of any statistic of interest is conditional on the model selected so if there is model uncertainty then variance estimates should reflect this. BMA methodology can also be used for variable assessment since the probability that a given variable is active is readily obtained from the individual model posterior probabilities. The multivariate methods considered in this research are principal components analysis (PCA), canonical variate analysis (CVA), and canonical correlation analysis (CCA). Each method is viewed as a particular multivariate extension of univariate multiple regression. The marginal likelihood of a univariate multiple regression model has been approximated using the Bayes information criteria (BIC), hence the marginal likelihood for these multivariate extensions also makes use of this approximation. One of the main criticisms of multivariate techniques in general is that they are difficult to interpret. To aid interpretation, BMA methodology is used to assess the contribution of each variable to the methods investigated. A second issue that is addressed is displaying of results of an analysis graphically. The goal here is to effectively convey the germane elements of an analysis when BMA is used in order to obtain a clearer picture of what conclusions should be drawn. Finally, the model uncertainty variance component can be estimated using BMA. The variance due to model uncertainty is ignored when the standard model building tenets are used giving overly optimistic variance estimates. Even though the model attained via standard techniques may be adequate, in general, it would be difficult to argue that the chosen model is in fact the correct model. It seems more appropriate to incorporate the information from all plausible models that are well supported by the data to make decisions and to use variance estimates that account for the uncertainty in the model estimation as well as model selection. / Ph. D.