Tumaini - Hope

This thesis explored the question “In what ways are nurses’ sense making and meaning making affected by culture and context on a medical mission in Kakamega, Kenya?” A qualitative inquiry took place during a nurse-led medical mission in Kakamega, Kenya. Eight nurses’ journals, including the researcher, were examined for themes around the cultural and contextual factors upon which nurses reflected. A subsequent focus group was conducted with 5 of these nurses following the mission to confirm and clarify the data and explore any new themes identified. Findings demonstrated that as nurses compared their lived experience in Canada to the conditions they were encountering in Kenya, they became increasingly aware of gaps in their understandings. As they attempted to bridge the gaps of their inexperience, coping emerged as a significant theme by which nurses dealt with these unique cultural and contextual circumstances. The results imply the need for a stringent recruitment and interview process when considering participants for a mission and the necessity of comprehensive premission education for nurses. Primarily, it is essential to provide emotional support for nurses during and following the mission. It can be inferred from the implications of this study how organizations must be diligent in preparing nurses for all aspects of the mission including the significance of a unified team process.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and deterministic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel metaheuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS metaheuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and determinis- tic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel meta–heuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS meta–heuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Therapeutic riding : learning and recovery for people with disabilities

The purpose ofthis study was to explore the process oftherapeutic riding as an experiential and holistic approach to learning and recovery for people with disabilities as perceived by the providers oftherapeutic riding. To enhance the connection between theory and practice and to suggest future research, the researcher endeavoured to develop a theory that contributed to the knowledge base oftherapeutic riding, animal-assisted therapy and education, experiential education, and experiential therapy in addition to contributing to connections among them. This topic was investigated because ofthe lack ofresearch about the process of therapeutic riding, particularly from learning and a recovery perspective. Few studies have addressed how therapeutic riding outcomes are achieved or how the therapeutic riding process actually works. This study was identified as grounded theory using qualitative data through interviews and narrative reflections with therapeutic riding providers, a researcher's journal, field notes, and written documents. Grounded theory analysis was used to analyze the qualitative data. This consisted ofdoing open, axial, and selective coding. This study provided detailed descriptions ofthe research approach, researcher's involvement, participant and site selection, data collection and analysis, methodological assumptions and limitations, credibility established, and ethical considerations. The findings ofthe data analysis revealed the theme ofrelationships as central to the learning and recovery process oftherapeutic riding for people with disabilities. The significance ofthe team relationships, the horse and rider relationship, and the providers and rider relationship was found. The essential components ofthe learning and recovery process were presented in a diagram in the selective coding phase. Goals oftherapeutic riding included psycho-education; behavioural and social; physical; and equestrian. Parts ofthe process ofhow outcomes were achieved included motivation; "opens new doors;" risk; task analysis; control; communication; and environmental factors. Outcomes of therapeutic riding included independence and mobility; confidence; and transfer abilities or skills. The implications ofthese findings for theory, practice, and further research were also. explored.

Socio-demographic factors, smoking, symptoms, morbidities and pulmonary function and quality of life in individuals with a heavy smoking history

Objective: To determine which socio-demographic, exposure, morbidity and symptom variables are associated with health-related quality of life among former and current heavy smokers. Methods: Cross sectional data from 2537 participants were studied. All participants were at ≥2% risk of developing lung cancer within 6 years. Linear and logistic regression models utilizing a multivariable fractional polynomial selection process identified variables associated with health-related quality of life, measured by the EQ-5D. Results: Upstream and downstream associations between smoking cessation and higher health-related quality of life were evident. Significant upstream associations, such as education level and current working status and were explained by the addition of morbidities and symptoms to regression models. Having arthritis, decreased forced expiratory volume in one second, fatigue, poor appetite or dyspnea were most highly and commonly associated with decreased HRQoL. Discussion: Upstream factors such as educational attainment, employment status and smoking cessation should be targeted to prevent decreased health-related quality of life. Practitioners should focus treatment on downstream factors, especially symptoms, to improve health-related quality of life.