Primary Stroke Education Using the ASCVD Risk Calculator and the Stroke Knowledge Test in Primary Care

Approved May 2020 by the faculty of UMKC in partial fulfillment of the requirements for the degree of Doctor of Nursing PracticeThe incidence of stroke is expected to rise with the aging population, and the healthcare cost is predicted to triple by 2030. Stroke, as the leading cause of disability, warrants a stroke prevention education program in a primary care setting to provide quality healthcare. The purpose of the quality improvement project is to increase awareness of stroke risk factors and symptoms within a primary care setting. The increased awareness will contribute to preventing devastating stroke outcomes. A quality improvement project with a quasi-experimental, single group, pre, and posttest design, implemented a stroke prevention education at a small Midwest family practice clinic. The participants received stroke prevention education using educational materials from the Center for Disease Control and Prevention, and the Atherosclerotic Cardiovascular Disease risk calculator estimated the ten-year risk of having a cardiovascular event. The Stroke Knowledge Test assessed the effectiveness of the educational intervention on stroke knowledge. Implementation of the intervention was conducted from September 2019 to February 2020. Of the 25 participants, 68% were female, 88.5% were Caucasian, and 68% completed high school. Stroke knowledge statistically increased from pre to posttest (p <0.001). The educational intervention was successful at increasing stroke knowledge. The impact on healthcare is to increase primary prevention strategies for stroke in a family practice. Further intention is to decrease the incidence and disability from stroke

From primal sketches to the recovery of intensity and reflectance representations

A local change in intensity (edge) is a characteristic that is preserved when an image is filtered through a bandpass filter. Primal sketch representations of images, using the bandpass-filtered data, have become a common process since Marr proposed his model for early human vision. Here, researchers move beyond the primal sketch extraction to the recovery of intensity and reflectance representations using only the bandpass-filtered data. Assessing the response of an ideal step edge to the Laplacian of Gaussian (NAb/A squared G) filter, they found that the resulting filtered data preserves the original change of intensity that created the edge in addition to the edge location. Using the filtered data, they can construct the primal sketches and recover the original (relative) intensity levels between the boundaries. It was found that the result of filtering an ideal step edge with the Intensity-Dependent Spatial Summation (IDS) filter preserves the actual intensity on both sides of the edge, in addition to the edge location. The IDS filter also preserves the reflectance ratio at the edge location. Therefore, one can recover the intensity levels between the edge boundaries as well as the (relative) reflectance representation. The recovery of the reflectance representation is of special interest as it erases shadowing degradations and other dependencies on temporal illumination. This method offers a new approach to low-level vision processing as well as to high data-compression coding. High compression can be gained by transmitting only the information associated with the edge location (edge primitives) that is necessary for the recover

2017 HRS/EHRA/ECAS/APHRS/SOLAECE expert consensus statement on catheter and surgical ablation of atrial fibrillation: executive summary.

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2017 HRS/EHRA/ECAS/APHRS/SOLAECE expert consensus statement on catheter and surgical ablation of atrial fibrillation: executive summary.

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withdrawn 2017 hrs ehra ecas aphrs solaece expert consensus statement on catheter and surgical ablation of atrial fibrillation

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Pendidikan kejuruan : pengajaran, kurikulum, perencanaan

v, 204 p. : ilus. ; 21 cm

Pendidikan kejuruan : pengajaran, kurikulum, perencanaan

v, 204 p. : ilus. ; 21 cm

Diffusion In Social Networks: A Model Of Member Diffusion Behavior

As more of our society participates online to perform everyday activities from shopping to socializing, the traditional methods of diffusion are changing. These changes are important, not only for individuals participating, but also for the organizations and businesses that are entering the online space. Information of new innovations and ideas spread through societies by means of interaction and communication (Rogers 1976). Understanding how diffusion functions online gives all the stake holders information that can be used to optimize their online experiences. Historically, diffusion followed orderly paths dictated by networks constructed from relationships of who had contact with whom (Bala and Goyal 1998). Today, the ability to reach others has shortcut the traditional paths. Creating communities comprised of relationship possibilities is only bounded by technologies. Diffusion in today's communities requires an understanding of the member behaviors that lead to the formation of the relationships that become the pathways enabling exchange. Based on fundamental theories about social networks and diffusion, this research proposes a Member Diffusion Behavior Model (MDB) that explains member diffusion behavior with three antecedents: Informal Sharing (IS), Promotion (Pr), and Persistent Conversation (PC). The findings of this research toile in three areas, the testing of the behavior model, the discovery of the behaviors from conversation structure, and the identification of the influence of seed members on diffusion. Hypotheses testing provides support for the IS and PC paths in the MDB model. Using a new structure based method, these two behaviors IS and PC are shown to be successfully discovered from the conversation structure. When the top defusing members identified using the structure metrics are used to seed the diffusion process, these members are found to be more effective than members selected using the traditional degree centrality method. This research contributes to the understanding of diffusion in social networks in several important ways. First, it provides a theoretical model to explain member diffusion behavior by integrating theories of diffusion and social networks. Second, it designs methods and develops metrics for measuring diffusion behavior in social networks by integrating social network analysis and information retrieval techniques. Third, it helps to gain insight into the diffusion process by exploring how members with influencing behaviors affect the network