21,472 research outputs found

    Search Engine Optimisation in UK news production

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    This is an Author's Accepted Manuscript of an article published in Journalism Practice, 5(4), 462 - 477, 2011, copyright Taylor & Francis, available online at: http://www.tandfonline.com/10.1080/17512786.2010.551020.This paper represents an exploratory study into an emerging culture in UK online newsrooms—the practice of Search Engine Optimisation (SEO), which assesses its impact on news production. Comprising a short-term participant observational case study at a national online news publisher, and a series of semi-structured, in-depth interviews with SEO professionals at three further UK media organisations, the author sets out to establish how SEO is operationalised in the newsroom, and what consequences these practices have for online news production. SEO practice is found to be varied and application is not universal. Not all UK news organisations are making the most of SEO even though some publishers take a highly sophisticated approach. Efforts are constrained by time, resources and management support, as well as off-page technical issues. SEO policy is found, in some cases, to inform editorial policy, but there is resistance to the principal of SEO driving decision-making. Several themes are established which call for further research

    Leveraging mobile health technology and research methodology to optimize patient education and self-management support for advanced cancer pain

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    Funding: National Institutes of Health [R21 NR017745, PI, Enzinger]; Friends of Dana-Farber Cancer Institute. Availability of data and material: The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.Peer reviewedPostprin

    Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

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    Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems
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