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The purpose of this study is to assess the perceptions of social workers employed in Florida hospitals in relation to the core values of their profession and the alignment of those values within the culture of their current work setting. The conceptual framework for the study was from organizational behavior theory specific to culture, values, and trust. The Competing Values Framework (Cameron & Quinn, 1999) provided a method to distinguish co-existing competing values within an organization. The research findings indicated that 65% of the professional social workers who participated in the study perceived that the core values of their profession are very much in alignment with the written mission statement of their hospital. However, less than half of the respondents (42%) stated the daily business of the hospital strongly reflected the mission statement. The social workers perceived the current culture of hospitals in Florida as being closely clustered among four cultures: clan, adhocracy, market, and hierarchy. However, they would prefer a stronger clan culture and less of a market culture in the future. A large majority (85%) of all respondents communicated that their work assignments allowed them to demonstrate their professional values on a regular basis. However, only 63% stated that they trusted that their hospital valued the knowledge and skills of their profession.

Management scholars have become increasingly interested in the role of organizational context. As part of this trend, research on work climates has thrived. This contemporary climate research differs from traditional approaches by concentrating on facet-specific climate types like service or innovation, rather than general, global conceptualizations of climate. Consequently, the climate literature has become fragmented and disorderly. I seek to remedy this in my dissertation. Specifically, I propose and test an integrated model of work climate that examines both molar and facet-specific climates. Chapter 1 is a review of the organizational work climate literature. This review seeks to review, reorganize, and reintegrate the climate literature. In addition, this review brought to light an issue that hinders the integration of the climate literatures: the literature does not contain a quality instrument for assessing the general characteristics of the molar work climate of an organization. In Chapter 2, I develop a theoretically-driven measure of work climate by drawing on the competing values framework (Quinn & Rohrbaugh, 1983). Preliminary results from three studies suggest that the proposed four-component model of molar work climate appears to be viable. The results indicate the instrument has internal reliability. Further, the results demonstrate discriminant, convergent, and criterion-related validity. In Chapter 3, I propose and test an integrated model of work climate by drawing on bandwidth-fidelity theory (Cronbach & Gleser, 1957). I predict that facet-specific climates will be more strongly related to specific outcomes and molar climates will be more strongly related to global outcomes. Further, I suggest weaker, indirect relationships between molar climate and specific outcomes and between facet-specific climates and global outcomes. The results indicate support for my predictions.

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yes / Thomas S. Kuhn is famous both for his work on the Copernican Revolution and his ‘paradigm’ view of scientific revolutions. But Kuhn later abandoned the notion of paradigm (and related notions) in favour of a more ‘evolutionary’ view of the history of science. Kuhn’s position therefore moved closer to ‘continuity’ models of scientific progress, for instance ‘chain-of-reasoning’ models, originally championed by D. Shapere. The purpose of this paper is to contribute to the debate around Kuhn’s new ‘developmental’ view and to evaluate these competing models with reference to some major innovations in the history of cosmology, from Copernicanism to modern cosmology. This evaluation is made possible through some unexpected overlap between Kuhn’s earlier discontinuity model and various versions of the later continuity models. It is the thesis of this paper that the ‘chain-of-reasoning’ model accounts better for the cosmological evidence than both Kuhn’s early paradigm model and his later developmental view of the history of science.

Understanding the lifetime behavior of their products is crucial to the success of any company in the manufacturing and engineering industries. Statistical methods for lifetime data are a key component to achieving this level of understanding. Sometimes a statistical procedure must be updated to be adequate for modeling specific data as is discussed in Chapter 2. However, there are cases in which the methods used in industrial standards are themselves inadequate. This is distressing as more appropriate statistical methods are available but remain unused. The research in Chapter 4 deals with such a situation. The research in Chapter 3 serves as a combination of both scenarios and represents how both statisticians and engineers from the industry can join together to yield beautiful results. After introducing basic concepts and notation in Chapter 1, Chapter 2 focuses on lifetime prediction for a product consisting of multiple components. During the production period, some components may be upgraded or replaced, resulting in a new ``generation" of component. Incorporating this information into a competing risks model can greatly improve the accuracy of lifetime prediction. A generalized competing risks model is proposed and simulation is used to assess its performance. In Chapter 3, optimal and compromise test plans are proposed for constant amplitude fatigue testing. These test plans are based on a nonlinear physical model from the fatigue literature that is able to better capture the nonlinear behavior of fatigue life and account for effects from the testing environment. Sensitivity to the design parameters and modeling assumptions are investigated and suggestions for planning strategies are proposed. Chapter 4 considers the analysis of ADDT data for the purposes of estimating a thermal index. The current industry standards use a two-step procedure involving least squares regression in each step. The methodology preferred in the statistical literature is the maximum likelihood procedure. A comparison of the procedures is performed and two published datasets are used as motivating examples. The maximum likelihood procedure is presented as a more viable alternative to the two-step procedure due to its ability to quantify uncertainty in data inference and modeling flexibility. / Ph. D.

2024 / Contemporary randomized trials frequently assess treatment effects across multiple time-to-event outcomes. In scenarios involving competing risks, prioritized outcomes, or informative censoring, alternatives to conventional methods to estimate and test for treatment effects are needed. For competing risks data, we proposed a doubly robust estimator for the difference in the restricted mean times lost to a specific cause. The estimator relies on non-parametric pseudo-observations of the cumulative incidence function, and therefore does not rely on the proportional hazard assumption. We evaluated the performance of the estimator in different scenarios of model misspecification. We applied the estimator to compare the event-free time lost to disease progression in the POPLAR and OAK studies for non-small-cell lung cancer. For prioritized time-to-event outcomes, we compared the performance of novel tests that prioritize events with higher clinical importance to traditional tests that do not. None of the tests was uniformly best when component-wise treatment effects varied. As these tests differ in how they characterize the treatment effect over the entire disease course, we proposed a generalizable framework to quantify the information used and ignored by each test. Under the Gumbel survival copula model, we also derived analytically the true value of the treatment effect corresponding to each test. We illustrated these methods using a five-component prioritized outcome in the SPRINT randomized trial. For informative censoring, we considered the issue of differential censoring between randomization groups in oncology trials. We assessed the impact of informative censoring on the treatment effect estimation, as well as on the performance of generalized log-rank tests under a delayed effect setting. We showed how to generate informative censoring data from survival copulas with piece-wise exponential marginals. We also derived the relationship between the copula rank correlation and the probability of informative censoring. We showed how to use this relationship to guide the choice of an adequate copula model to analyze informative censoring data. / 2027-02-12T00:00:00Z

The typical research of Alzheimer's disease includes a series of cognitive states. Multi-state models are often used to describe the history of disease evolvement. Competing risks models are a sub-category of multi-state models with one starting state and several absorbing states. Analyses for competing risks data in medical papers frequently assume independent risks and evaluate covariate effects on these events by modeling distinct proportional hazards regression models for each event. Jeong and Fine (2007) proposed a parametric proportional sub-distribution hazard (SH) model for cumulative incidence functions (CIF) without assumptions about the dependence among the risks. We modified their model to assure that the sum of the underlying CIFs never exceeds one, by assuming a proportional SH model for dementia only in the Nun study. To accommodate left censored data, we computed non-parametric MLE of CIF based on Expectation-Maximization algorithm. Our proposed parametric model was applied to the Nun Study to investigate the effect of genetics and education on the occurrence of dementia. After including left censored dementia subjects, the incidence rate of dementia becomes larger than that of death for age < 90, education becomes significant factor for incidence of dementia and standard errors for estimates are smaller. Multi-state Markov model is often used to analyze the evolution of cognitive states by assuming time independent transition intensities. We consider both constant and duration time dependent transition intensities in BRAiNS data, leading to a mixture of Markov and semi-Markov processes. The joint probability of observing a sequence of same state until transition in a semi-Markov process was expressed as a product of the overall transition probability and survival probability, which were simultaneously modeled. Such modeling leads to different interpretations in BRAiNS study, i.e., family history, APOE4, and sex by head injury interaction are significant factors for transition intensities in traditional Markov model. While in our semi-Markov model, these factors are significant in predicting the overall transition probabilities, but none of these factors are significant for duration time distribution.