Educating Grandparents of Grandchildren with Type I Diabetes Using Simulation: A Dissertation

The purpose of this study was to explore the feasibility of using human patient simulation (HPS) to teach Type 1 diabetes (T1DM) management to grandparents of grandchildren with T1DM. Thirty grandparents (11 male, 19 female) of young grandchildren (aged 12 and under) with T1DM were recruited from an urban medical center. Experimental group (n = 14) grandparents received hands-on visual T1DM management education using an HPS intervention, and control group (n = 16) grandparents received similar education using a non-HPS intervention. Post-intervention, researchers interviewed twelve grandparents (50% HPS, 50% non-HPS) who scored highest and lowest on the Hypoglycemia Fear Survey. Using a mixed-method design, researchers integrated study instrument data and post-intervention interview data to describe grandparent’s experience learning T1DM management. Post-intervention, grandparent scores for knowledge, confidence, and fear showed no significant difference by group assignment, however, all grandparent scores showed improvement from Time 1 to Time 2. Grandparents described how taking part in T1DM education heightened their awareness of T1DM risks. GP T1DM knowledge gains aided GPs to make sense of T1DM risks. Newfound T1DM knowledge enhanced GP T1DM management confidence. Improved T1DM knowledge and confidence helped to defuse T1DM management fear. Although study instruments did not measure significant difference between grandparents who received the HPS intervention and those who did not, the consistency of larger HPS-taught grandparent score improvement is suggestive of a benefit for HPS

Employing dynamic fuzzy membership functions to assess environmental performance in the supplier selection process

The proposed system illustrates that logic fuzzy can be used to aid management in assessing a supplier's environmental performance in the supplier selection process. A user-centred hierarchical system employing scalable fuzzy membership functions implement human priorities in the supplier selection process, with particular focus on a supplier's environmental performance. Traditionally, when evaluating supplier performance, companies have considered criteria such as price, quality, flexibility, etc. These criteria are of varying importance to individual companies pertaining to their own specific objectives. However, with environmental pressures increasing, many companies have begun to give more attention to environmental issues and, in particular, to their suppliers’ environmental performance. The framework presented here was developed to introduce efficiently environmental criteria into the existing supplier selection process and to reflect on its relevant importance to individual companies. The system presented attempts to simulate the human preference given to particular supplier selection criteria with particular focus on environmental issues when considering supplier selection. The system considers environmental data from multiple aspects of a suppliers business, and based on the relevant impact this will have on a Buying Organization, a decision is reached on the suitability of the supplier. This enables a particular supplier's strengths and weaknesses to be considered as well as considering their significance and relevance to the Buying OrganizationPeer reviewe

The rhizome and the tree: changing metaphors for information organisation

Purpose – The paper aims to review Deleuze and Guttari's concept of the rhizome as a model for information organisation. Design/methodology/approach – This is a critical review of selected literature. Findings – The rhizome concept is a promising model for understanding hyperlinked information services. It may be of practical value, particularly if it can be integrated with more traditional forms of information organisation. More research, conceptual and practical, is needed before this can be achieved. Research limitations/implications – The literature review is not comprehensive, and the conclusions are open-ended. Originality/value – This is the only paper to review the rhizome concept in this way

Learning to remember: The early ontogeny of episodic memory

AbstractOver the past 60 years the neural correlates of human episodic memory have been the focus of intense neuroscientific scrutiny. By contrast, neuroscience has paid substantially less attention to understanding the emergence of this neurocognitive system. In this review we consider how the study of memory development has evolved. In doing so, we concentrate primarily on the first postnatal year because it is within this time window that the most dramatic shifts in scientific opinion have occurred. Moreover, this time frame includes the critical age (∼9 months) at which human infants purportedly first begin to demonstrate rudimentary hippocampal-dependent memory. We review the evidence for and against this assertion, note the lack of direct neurocognitive data speaking to this issue, and question how demonstrations of exuberant relational learning and memory in infants as young as 3-months old can be accommodated within extant models. Finally, we discuss whether current impasses in the infant memory literature could be leveraged by making greater use of neuroimaging techniques, such as magnetic resonance imaging (MRI), which have been deployed so successfully in adults

Creating national weights for a patient-level longitudinal database

Onur Başer (MEF Author)##nofulltext##To create a nationally-representative estimate from longitudinal data by controlling for sociodemographic factors and health status. The Agency for Healthcare Research and Quality’s (AHRQ) Medicare Expenditures Panel Survey (MEPS) was used as the basis for adjustment methodology. MEPS is a data source representing health insurance coverage cost and utilization, and comprises several large-scale surveys of families, individuals, employers, and health care providers. Using these data, we created subset populations. We then used multivariate logistic regression to construct demographics and case-mix-based weights, which were applied to create a population sample that is similar to the national population. The weight was derived using the inverse probability of the weighting approach, as well as a raking mechanism. We compared the results with the projected number of persons in the US population in the same categories to examine the validity of the weights. The following variables were used in the logistic regression: Age group, gender, race, location, income level and health status (Charlson Comorbidity Index scores and chronic condition diagnosis). Relative to MEPS data, patients included in the private insurance data were more likely to be male, older, to have a chronic condition, and to be white (p=0.0000). Adjusted weighted values for patients in the commercial group ranged from 15.47 to 36.36 (median: 16.91). Commercial insurance and MEPS data populations were similar in terms of their socioeconomic and clinical categories. As an outcomes measure, the predicted annual number of patients with prescription claims from private insurance data was 6 963 034. The annual number of statin users were predicted as 6 709 438 using weighted MEPS data. National projections of large-scale patient longitudinal databases require adjustment utilizing demographic factors and case-mix differences related to health status

Comparing Design Ground Snow Load Prediction in Utah and Idaho

Snow loads in the western United States are largely undefined due to complex geography and climates, leaving the individual states to publish detailed studies for their region, usually through the local Structural Engineers Association (SEAs). These associations are typically made up of engineers not formally trained to develop or evaluate spatial statistical methods for their regions and there is little guidance from ASCE 7. Furthermore, little has been written to compare the independently developed design ground snow load prediction methods used by various western states. This paper addresses this topic by comparing the accuracy of a variety of spatial methods for predicting 50-year (i.e., design) ground snow loads in Utah and Idaho. These methods include, among others, the current Utah snow load equations, Idaho’s normalized ground snow loads based on inverse distance weighting, two forms of kriging, and the authors’ adaptation of the Parameter-elevation Relationships on Independent Slopes Model (PRISM). The accuracy of each method is evaluated by measuring the mean absolute error using 10-fold cross validation on data sets obtained from Idaho’s 2015 snow load report, Utah’s 1992 snow load report, and a new Utah ground snow load data set. These results show that regression-based kriging and PRISM methods have the lowest cross-validated errors across all three data sets. These results also show that normalized ground snow loads, which are a common way of accounting for elevation in traditional interpolation methods, do not fully account for the effect of elevation on ground snow loads within the considered data sets. The methodologies and cautions outlined in this paper provide a framework for an objective comparison of snow load estimation methods for a given region as state SEAs look to improve their future design ground snow predictions. Such comparisons will aid states looking to amend or improve their current ground snow load requirements