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Background: Literature for a systematic review on the student experience of e-learning is located across a range of subject areas including health, education, social science, library and information science. Objectives: To assess the merits and shortcomings of using different search techniques in retrieval of evidence in the social science literature. Methods: A conventional subject search was undertaken as the principal method of identifying the literature for the review. Four supplementary...
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Systematic reviews are researches requiring great attention to detail. They may well necessitate considerable investment of effort to ensure relevant data are identified, extracted, synthesized, written up and disseminated. These tasks have already been greatly refined and, in some cases, simplified, by machines. The last two decades have seen remarkable progress in machine-assisted production of reviews – the next two should see much more.
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Systematic reviews, a cornerstone of evidence-based medicine, are not produced quickly enough to support clinical practice. The cost of production, availability of the requisite expertise and timeliness are often quoted as major contributors for the delay. This detailed survey of the state of the art of information systems designed to support or automate individual tasks in the systematic review, and in particular systematic reviews of randomized controlled clinical trials, reveals trends...
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The large and growing number of published studies, and their increasing rate of publication, makes the task of identifying relevant studies in an unbiased way for inclusion in systematic reviews both complex and time consuming. Text mining has been offered as a potential solution: through automating some of the screening process, reviewer time can be saved. The evidence base around the use of text mining for screening has not yet been pulled together systematically; this systematic review...
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Automation of the parts of systematic review process, specifically the data extraction step, may be an important strategy to reduce the time necessary to complete a systematic review. However, the state of the science of automatically extracting data elements from full texts has not been well described. This paper performs a systematic review of published and unpublished methods to automate data extraction for systematic reviews.
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