108 research outputs found

    An Exploration of Effective Patient Education with an Emphasis on Concussion

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    Concussion is a prevalent healthcare issue in the US, with approximately 1.6-3.8 million sports and recreation-related concussions each year in all ages. A concussion can be defined as a traumatic brain injury caused by biomechanical forces. When an athlete sustains a concussion, a physiologic cascade of events occurs. The most common signs and symptoms of a concussion include: loss of balance, disorientation, headache and confusion. Concussion assessments are important in order to determine the presence of an impairment and there are a multitude of tests that clinicians can use in order to isolate each type of damage. Studies have shown that behavioral regulation and active treatment are key components to a fast and successful recovery from a concussion. Data regarding patient education in specialty clinics, such as those focused on concussion, is limited. This is a concern due to the need for education both prior to the injury and after the concussion is diagnosed. Health education, also known as patient education, refers to the process of providing information to individuals and allowing them to make knowledgeable decisions regarding their healthcare. In order to maximize the effectiveness of health education, professionals should be aware that the delivery of the information should be tailored to the learning preferences of each individual patient. Finding ways to overcome the disconnect in knowledge transfer between healthcare professionals and patients is essential for better treatment outcomes. Since limited time with the provider is shown to be the most significant barrier to quality patient education, utilizing time spent in the waiting room is essential to overcome this

    Where do the elderly die? The impact of nursing home utilisation on the place of death. Observations from a mortality cohort study in Flanders

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    BACKGROUND: Most of the research concerning place of death focuses on terminally ill patients (cancer patients) while the determinants of place of death of the elderly of the general population are not intensively studied. Studies showed the influence of gender, age, social-economical status and living arrangements on the place of death, but a facet not taken into account so far is the influence of the availability of nursing homes. METHODS: We conducted a survey of deaths, between January 1999 and December 2000 in a small densely populated area in Belgium, with a high availability of nursing homes (within 5 to 10 km of the place of residence of every elderly). We determined the incidence of total mortality (of subjects >60 years) from local official death registers that we consulted via the priest or the mortician of the local parish, to ask where the decedent had died and whether the deceased had lived in a nursing home. We compared the distribution of the places of death between parishes with a nursing home and with parishes without nursing home. RESULTS: 240 women and 217 men died during the two years study period. Only 22% died at home, while the majority (78%) died in an institutional setting, either a hospital (50%) or a nursing home (28%). Place of death was influenced by individual factors (age and gender) and the availability of a nursing home in the 'own' parish. The chance of in-hospital death was 65% higher for men (95% Confidence Interval [CI]: 14 to 138%; p = 0.008) and decreased by 4% (CI: -5.1% to -2.5%; p < 0.0001) for each year increase in age. Independent of gender and age, the chance of in-hospital death was 41% (CI: -60% to -13%; p = 0.008) lower in locations with a nursing home. CONCLUSION: Demographic, but especially social-contextual factors determine where elderly will end their life. The majority of elderly in Flanders die in an institution. Age, gender and living situation are predictors of the place of death but the embedment of a nursing home in the local community seems to be a key predictor

    Modern MT: A New Open-Source Machine Translation Platform for the Translation Industry

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    Modern MT (www.modernmt.eu) is a three-year Horizon 2020 innovation action (2015–2017) to develop new open-source machine translation technology for use in translation production environments, both fully automatic and as a back-end in interactive post-editing scenarios. Led by Translated srl, the project consortium also includes the Fondazione Bruno Kessler (FBK), the University of Edinburgh, and TAUS B.V. Modern MT has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 645487 (call ICT-17-2014)

    Complexities of learning with computer-based tools: A case of inquiry about sound and music in elementary school

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    Computer-based technology is increasingly becoming available for students at all grade levels in schools, and its promise and power as a learning tool is being extolled by many. From a constructive perspective, if individuals actively construct meaning from their experiences, then simply having particular tools to work with via a computer doesn't ensure that desired learning will result. Thus, it is important to examine how students construct meaning while using such tools. This study examined what fourth grade students learned from the use of two computer-based tools intended to help them understand sound and music: software that emulated an oscilloscope and allowed students to view sound waves from audio input; and software that turned the computer into an electronic keyboard, which provided students with standard pitches for comparison purposes. Principles of selective attention and pior knowledge and experiences —foundational ideas of a constructivist epistemology—were useful in understanding learning outcomes from inquiry with these tools. Our findings provide critical information for future instruction with the goal of supporting learning about sound and music from such tools. They also indicate the need for more studies examining learning from computer-based tools in specific contexts, to advance our understanding of how teachers can mediate student activity with computer-based tools to support the development of conceptual understanding.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45183/1/10956_2005_Article_BF01677126.pd

    Quantile regression for overdispersed count data: a hierarchical method

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    Abstract Generalized Poisson regression is commonly applied to overdispersed count data, and focused on modelling the conditional mean of the response. However, conditional mean regression models may be sensitive to response outliers and provide no information on other conditional distribution features of the response. We consider instead a hierarchical approach to quantile regression of overdispersed count data. This approach has the benefits of effective outlier detection and robust estimation in the presence of outliers, and in health applications, that quantile estimates can reflect risk factors. The technique is first illustrated with simulated overdispersed counts subject to contamination, such that estimates from conditional mean regression are adversely affected. A real application involves ambulatory care sensitive emergency admissions across 7518 English patient general practitioner (GP) practices. Predictors are GP practice deprivation, patient satisfaction with care and opening hours, and region. Impacts of deprivation are particularly important in policy terms as indicating effectiveness of efforts to reduce inequalities in care sensitive admissions. Hierarchical quantile count regression is used to develop profiles of central and extreme quantiles according to specified predictor combinations

    Distributed Multimedia Learning Environments: Why and How?

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