3 resultados para Academic Medical Centers

em Dalarna University College Electronic Archive


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Syfte: Syftet med studien var att beskriva distriktssköterskors upplevelser och erfarenheter av patientundervisning till patienter med diabetes samt att identifiera aspekter som kan relateras till ett personcentrerat förhållningssätt. Metod: Deskriptiv design med kvalitativ ansats med semistrukturerade intervjuer användes vid datainsamlingen. Vid urvalet användes strategiskt urval. Nio intervjuer utfördes med distriktssköterskor på sex olika vårdcentraler i Mellansverige. Vid analysen användes kvalitativ innehållsanalys. Resultat: Distriktssköterskorna önskade mer kunskap om invandrares kost- och motionsvanor. Distriktssköterskorna uttryckte att det var roligt och spännande med patientundervisning och att det var viktigt att de var engagerade. Distriktssköterskorna ville arbeta mer i team och de upplevde att de hade för lite tid avsatt för patientundervisning. De ansåg att de arbetade personcentrerat men det var svårare att arbeta personcentrerat vid gruppundervisning än vid enskild undervisning. Slutsats: Distriktssköterskor bör ha god kunskap om kulturella skillnader hos patienter med diabetes. Distriktssköterskorna upplever brist på tid- och resurser och önskar samarbeta mera i team. Det är viktigt med ett personcentrerat förhållningssätt där distriktssköterskorna utgår från den enskilda individen. En distriktssköterska som är engagerad och trivs med sitt arbete kan lättare klara av det ökade trycket och arbetsbelastningen.

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Unplanned hospital readmissions increase health and medical care costs and indicate lower the lower quality of the healthcare services. Hence, predicting patients at risk to be readmitted is of interest. Using administrative data of patients being treated in the medical centers and hospitals in the Dalarna County, Sweden, during 2008 – 2016 two risk prediction models of hospital readmission are built. The first model relies on the logistic regression (LR) approach, predicts correctly 2,648 out of 3,392 observed readmission in the test dataset, reaching a c-statistics of 0.69. The second model is built using random forests (RF) algorithm; correctly predicts 2,183 readmission (out of 3,366) and 13,198 non-readmission events (out of 18,982). The discriminating ability of the best performing RF model (c-statistic 0.60) is comparable to that of the logistic model. Although the discriminating ability of both LR and RF risk prediction models is relatively modest, still these models are capable to identify patients running high risk of hospital readmission. These patients can then be targeted with specific interventions, in order to prevent the readmission, improve patients’ quality of life and reduce health and medical care costs.

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The Survivability of Swedish Emergency Management Related Research Centers and Academic Programs: A Preliminary Sociology of Science Analysis Despite being a relatively safe nation, Sweden has four different universities supporting four emergency management research centers and an equal and growing number of academic programs. In this paper, I discuss how these centers and programs survive within the current organizational environment. The sociology of science or the sociology of scientific knowledge perspectives should provide a theoretical guide. Yet, scholars of these perspectives have produced no research on these related topics. Thus, the population ecology model and the notion of organizational niche provide my theoretical foundation. My data come from 26 interviews from those four institutions, the gathering of documents, and observations. I found that each institution has found its own niche with little or no competition – with one exception. Three of the universities do have an international focus. Yet, their foci have minimal overlap. Finally, I suggest that key aspects of Swedish culture, including safety, and a need aid to the poor, help explain the extensive funding these centers and programs receive to survive.