Comparative analysis of a moderating and mediating model of stress, appraisal, and coping in Hispanic and non-Hispanic Alzheimer's disease caregivers

This dissertation utilizes a cross-sectional study to examine the phenomenon of caregiving within a theoretically grounded stress, appraisal, and coping model. Hispanic and non-Hispanic caregivers were studied to examine the factors associated with variance in caregiver appraisal, coping, and outcomes of caregiving strain (depression and somatic complaints) and caregiving gain (life satisfaction, mastery, and personal gain). A purposive sampling strategy was used to recruit 204 Alzheimer's disease caregivers in South Florida. A self-report questionnaire was used to collect demographic data, and to measure stress, appraisal, coping, and psychological well-being of caregivers. Regression equations were developed to compare moderating and mediating models of appraisal and coping. Emotion-focused coping skills were found to significantly moderate the effects of stress (F [1,195] = 4.62, p < .05), explaining approximately 21% of the variance in satisfaction was found to moderate the effects of stress (F [1,195] = 7.09; p < .05), explaining approximately 27% of the variance in personal gain and approximately 8% of the variance in life satisfaction (F [1,195] = 4.14; p < .05). Appraisal of Burden was found to significantly mediate the effects of stress, explaining approximately 30% of the variance in somatic complaints (F [1,196] = 31.60; p < .001) and 32% of the variance in depression (F [1,196] = 38.18; p < .001). The results of the analyses indicate that appraisal and coping skills are important variables in the stress process. The results of this study underscore the importance of accounting for positive and negative outcomes in providing a fuller understanding of the stress, appraisal and coping process of Alzheimer's Disease caregivers. ^

A non-linear discrete stochastic optimization model for Advanced Access patient appointment scheduling

Access to healthcare is a major problem in which patients are deprived of receiving timely admission to healthcare. Poor access has resulted in significant but avoidable healthcare cost, poor quality of healthcare, and deterioration in the general public health. Advanced Access is a simple and direct approach to appointment scheduling in which the majority of a clinic's appointments slots are kept open in order to provide access for immediate or same day healthcare needs and therefore, alleviate the problem of poor access the healthcare. This research formulates a non-linear discrete stochastic mathematical model of the Advanced Access appointment scheduling policy. The model objective is to maximize the expected profit of the clinic subject to constraints on minimum access to healthcare provided. Patient behavior is characterized with probabilities for no-show, balking, and related patient choices. Structural properties of the model are analyzed to determine whether Advanced Access patient scheduling is feasible. To solve the complex combinatorial optimization problem, a heuristic that combines greedy construction algorithm and neighborhood improvement search was developed. The model and the heuristic were used to evaluate the Advanced Access patient appointment policy compared to existing policies. Trade-off between profit and access to healthcare are established, and parameter analysis of input parameters was performed. The trade-off curve is a characteristic curve and was observed to be concave. This implies that there exists an access level at which at which the clinic can be operated at optimal profit that can be realized. The results also show that, in many scenarios by switching from existing scheduling policy to Advanced Access policy clinics can improve access without any decrease in profit. Further, the success of Advanced Access policy in providing improved access and/or profit depends on the expected value of demand, variation in demand, and the ratio of demand for same day and advanced appointments. The contributions of the dissertation are a model of Advanced Access patient scheduling, a heuristic to solve the model, and the use of the model to understand the scheduling policy trade-offs which healthcare clinic managers must make. ^