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Most disaster management studies only assess community resilience immediately following the event with no further follow-up. Accordingly, there is a lack of research being conducted to determine whether communities truly recover over time after a disaster strikes. Thus, the purpose of this research was to examine the different factors and dimensions that facilitate or hinder community resilience more than a decade post-disaster using present day Walkerton, Ontario (16 years after the effects of the 2000 water contamination outbreak). This exploratory study utilized an interpretive description qualitative methodology. Semi-structured interviews and focus groups were conducted with a purposeful sample of 29 Walkerton community members. The data were transcribed verbatim and coded using conventional content analysis to identify themes inductively. Several barriers and enabling factors were identified in maintaining community resilience under non-crisis conditions in the community. A conceptual model was developed based on the study’s findings to demonstrate the application of the life course approach within an existing community resilience framework. This model contributes to the field of disaster management in demonstrating the various ways that a disaster affects the subsequent life course of individuals post-disaster. It highlights the need to integrate a community-centred approach in disaster management to yield more effective and efficient mitigation, preparation, response, and recovery strategies. / Thesis / Master of Public Health (MPH)

This project, which used a qualitative, explanatory case-study design explored the transition to practice of new graduate Registered Nurses (RNs) and Registered Practical Nurses (RPNs) in long-term care (LTC). Specifically, this study focused on the self-described transition to practice experience of the new graduate nurse (NGN), the contextual factors present in LTC that influenced this transition to practice, and how the transition experience was similar and different for the new graduate RN and RPN. Both NGNs and LTC directors were included in the study. In total, 7 NGNs and 2 LTC directors participated in semi-structured interviews. The NGN participants were employed as a nurse in one of the two LTC sites and had been working as a nurse for less than one year. Data were collected through Key Informants (NGNs and LTC directors), and Key Documents (LTC policies and orientation material). Results of this study introduced six contextual factors present in LTC that influence the transition to practice of NGNs and five processes that, as a result of the contextual factors, accelerate the transition to practice experience. This described accelerated transition to practice refutes the previously universally applied transition to practice theory and contributes new knowledge and understanding to the transition to practice experience of the NGN in LTC and more specifically how the new graduate RPN experiences transitioning. The findings also described the many similarities and some differences between the transition to practice experience of the new graduate RN and RPN in LTC. With increasing demands on the long-term care sector, these findings will be of interest to a broad audience including policy makers, educators, LTC directors and administrators, as well as nursing students and NGNs. It is anticipated that these results will direct further research on this topic, and inform policy, practice, and educational programs. / Thesis / Master of Science (MSc)

Long-term care facility network in Ontario, and in Canada as a whole, encounters critical issues regarding balancing demand with capacity. Even worse, it is faced with rising demand in the coming years. Moreover, there is an urgent need to provide long-term care for patients in their own language (particularly French). This study proposes a dynamic Mixed-Integer Linear Programming model based on the current standing of the long-term care system in Ontario, which simultaneously optimizes the time and location of constructing new long-term care facilities, adjusting the capacity (namely, human resources and beds) of each facility dynamically, and the assignment of patients to the facilities based on their demand region, gender, language, and age group over a finite time horizon. We apply the diversity-support constraints, based on patients’ gender and language, to save patients from loneliness and to comply with the Canadian values of providing care. Finally, we validate the model by performing a case study in Hamilton, Ontario. An extensive set of numerical analyses are explored to provide deeper insights into the whole issue. One set of such analysis is an extensive simulation study to examine the effect of distributional uncertainty in some of the input parameters on the optimal results, hence providing a much more realistic understanding of the optimization model. / Thesis / Master of Science (MSc)