Implementation of hospital-wide computer information system :a case study of this change process

This case study examines the impact of a computer information system as it was being implemented in one Ontario hospital. The attitudes of a cross section of the hospital staff acted as a barometer to measure their perceptions of the implementation process. With The Mississauga Hospital in the early stages of an extensive computer implementation project, the opportunity existed to identify staff attitudes about the computer system, overall knowledge and compare the findings with the literature. The goal of the study was to develop a greater base about the affective domain in the relationship between people and the computer system. Eight exploratory questions shaped the focus of the investigation. Data were collected from three sources: a survey questionnaire, focused interviews, and internal hospital documents. Both quantitative and qualitative data were analyzed. Instrumentation in the study consisted of a survey distributed at two points in time to randomly selected hospital employees who represented all staff levels.Other sources of data included hospital documents, and twenty-five focused interviews with staff who replied to both surveys. Leavitt's socio-technical system, with its four subsystems: task, structure, technology, and people was used to classify staff responses to the research questions. The study findings revealed that the majority of respondents felt positive about using the computer as part of their jobs. No apparent correlations were found between sex, age, or staff group and feelings about using the computer. Differences in attitudes, and attitude changes were found in potential relationship to the element of time. Another difference was found in staff group and perception of being involved in the decision making process. These findings and other evidence about the role of change agents in this change process help to emphasize that planning change is one thing, managing the transition is another.

An assessment of autism knowledge in the medical field

Once thought to be rare, pervasive developmental disorders (PDDs) are now recognized as the most common neurological disorders affecting children and one of the most common developmental disabilities (DD) in Canada (Autism Society of Canada, 2006). Recent reports indicate that PDDs currently affect 1 in 150 children (Centre for Disease Control and Prevention, 2007). The purpose of this research was to provide an understanding of medical resident and practicing physicians' basic knowledge regarding PDDs. With a population of children with PDDs who present with varying symptoms, the ability for medical professionals to provide general information, diagnosis, appropriate referrals, and medical care can be quite complex. A basic knowledge of the disorder is only a first step in providing adequate medical care to individuals with autism and their families. An updated version of Stone's (1987) Autism survey was administered to medical residents at four medical schools in Canada and currently practicing physicians at three medical schools and one community health network. As well, a group of professionals specializing in the field ofPDDs, participating in research and clinical practice, were surveyed as an 'expert' group to act as a control measure. Expert responses were consistent with current research in the field. General findings indicated few differences in overall knowledge between residents and physicians, with misconceptions evident in areas such as the nature of the disorder, qualitative characteristics of autism, and effective interventions. Results were also examined by specialty and, while pediatricians demonstrated additional accurate 11 knowledge regarding the nature of the disorder and select qualitative impairments, both residents and practicing physicians demonstrated misconceptions about PDDs. This preliminary study replicated the findings of Stone (1987) and Heidgerken (2005) concerning several misconceptions of PDDs held by residents and practicing physicians. Future research should focus on additional replications with validated measures as well as the gathering of qualitative information, in order to inform the medical profession of the need for education in PDDs at training and professional levels.

Interaction and knowledge exchange among academic business librarians in Ontario

A research project submitted to the Faculty of Extension, University of Alberta in partial fulfillment of the requirements for the degree of Master of Arts in Communications and Technology in 2005.

Anxiety-Related Disorders in Primary-Junior Grades (K-3): Teacher Perceptions and Knowledge

This study used a descriptive case study design to analyze teachers’ experiences of anxiety-related conditions and emotions in the primary-junior grades (K-3). The study sought to examine (a) educators’ perceptions of anxiety conditions and how such interpretations influence their teaching practice; (b) teachers’ knowledge of the diagnostic processes, symptomology, and emotions related to anxiety disorders; (c) primary teachers’ knowledge of and experience with emotional regulation strategies and therapeutic approaches for anxiety; and (d) additional strategies and knowledge that should be available to help students. The study adopted Bronfenbrenner’s (1986) Ecological Model to frame participants’ experiences and perspectives, as well as the impact of several factors (e.g., school, home) and individuals (e.g. teachers, parents, students) on students’ anxiety and the participants’ perspectives. Through in-person interviews, participants shared their experiences with and knowledge about students in their teaching practice who had experienced anxiety-related conditions and emotions. Four major themes emerged from the data: symptoms and situational contexts; knowledge of strategies and interventions; understanding and perspectives of students; anxious emotional responses; and challenges. The study contributes to the literature by providing the real-life perspectives and experiences of primary-junior teachers (K-3) related to students experiencing anxiety. The study provides further information for educators, administrators, and research regarding any additional support and knowledge that should be implemented to further assist educators and students in regards to anxiety.

GA approach for finding Rough Set decision rules based on bireducts

Feature selection plays an important role in knowledge discovery and data mining nowadays. In traditional rough set theory, feature selection using reduct - the minimal discerning set of attributes - is an important area. Nevertheless, the original definition of a reduct is restrictive, so in one of the previous research it was proposed to take into account not only the horizontal reduction of information by feature selection, but also a vertical reduction considering suitable subsets of the original set of objects. Following the work mentioned above, a new approach to generate bireducts using a multi--objective genetic algorithm was proposed. Although the genetic algorithms were used to calculate reduct in some previous works, we did not find any work where genetic algorithms were adopted to calculate bireducts. Compared to the works done before in this area, the proposed method has less randomness in generating bireducts. The genetic algorithm system estimated a quality of each bireduct by values of two objective functions as evolution progresses, so consequently a set of bireducts with optimized values of these objectives was obtained. Different fitness evaluation methods and genetic operators, such as crossover and mutation, were applied and the prediction accuracies were compared. Five datasets were used to test the proposed method and two datasets were used to perform a comparison study. Statistical analysis using the one-way ANOVA test was performed to determine the significant difference between the results. The experiment showed that the proposed method was able to reduce the number of bireducts necessary in order to receive a good prediction accuracy. Also, the influence of different genetic operators and fitness evaluation strategies on the prediction accuracy was analyzed. It was shown that the prediction accuracies of the proposed method are comparable with the best results in machine learning literature, and some of them outperformed it.