Sense-making in theory and practice: a metatheoretical foundation and application for health information seeking

This thesis attempts to provide deeper historical and theoretical grounding for sense-making, thereby illustrating its applicability to practical information seeking research. In Chapter One I trace the philosophical origins of Brenda Dervin’s theory known as “sense making,” reaching beyond current scholarship that locates the origins of sense-making in twentieth-century Phenomenology and Communication theory and find its rich ontological, epistemological, and etymological heritage that dates back to the Pre-Socratics. After exploring sense-making’s Greek roots, I examine sense-making’s philosophical undercurrents found in Hegel’s Phenomenology of Spirit (1807), where he also returns to the simplicity of the Greeks for his concept of sense. With Chapter Two I explore sense-making methodology and find, in light of the Greek and Hegelian dialectic, a dialogical bridge connecting sense-making’s theory with pragmatic uses. This bridge between Dervin’s situation and use occupies a distinct position in sense-making theory. Moreover, building upon Brenda Dervin’s model of sense-making, I use her metaphors of gap and bridge analogy to discuss the dialectic and dialogic components of sense making. The purpose of Chapter Three is pragmatic – to gain insight into the online information-seeking needs, experiences, and motivation of first-degree relatives (FDRs) of breast cancer survivors through the lens of sense-making. This research analyses four questions: 1) information-seeking behavior among FDRs of cancer survivors compared to survivors and to undiagnosed, non-related online cancer information seekers in the general population, 2) types of and places where information is sought, 3) barriers or gaps and satisfaction rates FDRs face in their cancer information quest, and 4) types and degrees of cancer information and resources FDRs want and use in their information search for themselves and other family members. An online survey instrument designed to investigate these questions was developed and pilot tested. Via an email communication, the Susan Love Breast Cancer Research Foundation distributed 322,000 invitations to its membership to complete the survey, and from March 24th to April 5th 10,692 women agreed to take the survey with 8,804 volunteers actually completing survey responses. Of the 8,804 surveys, 95% of FDRs have searched for cancer information online, and 84% of FDRs use the Internet as a sense-making tool for additional information they have received from doctors or nurses. FDRs report needing much more information than either survivors or family/friends in ten out of fifteen categories related to breast and ovarian cancer. When searching for cancer information online, FDRs also rank highest in several of sense-making’s emotional levels: uncertainty, confusion, frustration, doubt, and disappointment than do either survivors or friends and family. The sense-making process has existed in theory and praxis since the early Greeks. In applying sense–making’s theory to a contemporary problem, the survey reveals unaddressed situations and gaps of FDRs’ information search process. FDRs are a highly motivated group of online information seekers whose needs are largely unaddressed as a result of gaps in available online information targeted to address their specific needs. Since FDRs represent a quarter of the population, further research addressing their specific online information needs and experiences is necessary.

Soft computing based parameter identification in pavements and geomechanical systems

Accurate estimation of road pavement geometry and layer material properties through the use of proper nondestructive testing and sensor technologies is essential for evaluating pavement’s structural condition and determining options for maintenance and rehabilitation. For these purposes, pavement deflection basins produced by the nondestructive Falling Weight Deflectometer (FWD) test data are commonly used. The nondestructive FWD test drops weights on the pavement to simulate traffic loads and measures the created pavement deflection basins. Backcalculation of pavement geometry and layer properties using FWD deflections is a difficult inverse problem, and the solution with conventional mathematical methods is often challenging due to the ill-posed nature of the problem. In this dissertation, a hybrid algorithm was developed to seek robust and fast solutions to this inverse problem. The algorithm is based on soft computing techniques, mainly Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs) as well as the use of numerical analysis techniques to properly simulate the geomechanical system. A widely used pavement layered analysis program ILLI-PAVE was employed in the analyses of flexible pavements of various pavement types; including full-depth asphalt and conventional flexible pavements, were built on either lime stabilized soils or untreated subgrade. Nonlinear properties of the subgrade soil and the base course aggregate as transportation geomaterials were also considered. A computer program, Soft Computing Based System Identifier or SOFTSYS, was developed. In SOFTSYS, ANNs were used as surrogate models to provide faster solutions of the nonlinear finite element program ILLI-PAVE. The deflections obtained from FWD tests in the field were matched with the predictions obtained from the numerical simulations to develop SOFTSYS models. The solution to the inverse problem for multi-layered pavements is computationally hard to achieve and is often not feasible due to field variability and quality of the collected data. The primary difficulty in the analysis arises from the substantial increase in the degree of non-uniqueness of the mapping from the pavement layer parameters to the FWD deflections. The insensitivity of some layer properties lowered SOFTSYS model performances. Still, SOFTSYS models were shown to work effectively with the synthetic data obtained from ILLI-PAVE finite element solutions. In general, SOFTSYS solutions very closely matched the ILLI-PAVE mechanistic pavement analysis results. For SOFTSYS validation, field collected FWD data were successfully used to predict pavement layer thicknesses and layer moduli of in-service flexible pavements. Some of the very promising SOFTSYS results indicated average absolute errors on the order of 2%, 7%, and 4% for the Hot Mix Asphalt (HMA) thickness estimation of full-depth asphalt pavements, full-depth pavements on lime stabilized soils and conventional flexible pavements, respectively. The field validations of SOFTSYS data also produced meaningful results. The thickness data obtained from Ground Penetrating Radar testing matched reasonably well with predictions from SOFTSYS models. The differences observed in the HMA and lime stabilized soil layer thicknesses observed were attributed to deflection data variability from FWD tests. The backcalculated asphalt concrete layer thickness results matched better in the case of full-depth asphalt flexible pavements built on lime stabilized soils compared to conventional flexible pavements. Overall, SOFTSYS was capable of producing reliable thickness estimates despite the variability of field constructed asphalt layer thicknesses.