The Development of Search Filters for One Health Articles Using CAB Abstracts Descriptors

Jeanine M. Williamson, Maggie Albro, Steven D. Milewski, Brianne Dosch, Niki Cobb, Melanie A. Dixson
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Abstract

AbstractThis study seeks to develop and test the recall of two search hedges for One Health articles. CAB Abstracts was searched, and the first 100 relevance-ranked results were downloaded. The most frequent co-occurrences of CAB descriptors were used to develop a hedge. A second hedge was developed using the descriptors and related natural language keywords. The natural language hedge had better recall (100% and 95%, respectively) than the co-occurrence hedge (24% and 86%, respectively). When searching a broad-based topic area like One Health, there is a need for expansive language to incorporate multiple expressions of a concept.Keywords: CAB abstracts descriptorsOne Healthsearch filterssearch hedges Disclosure statementNo potential conflict of interest was reported by the authors.
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基于CAB摘要描述符的健康文章搜索过滤器的开发
摘要本研究旨在开发和测试一篇健康文章的两个搜索限制的召回。检索CAB Abstracts,下载相关度排名前100位的结果。CAB描述符的最频繁的共现被用来开发对冲。第二个对冲是使用描述符和相关的自然语言关键字开发的。自然语言对冲的召回率(分别为100%和95%)高于共现对冲(分别为24%和86%)。在搜索基础广泛的主题领域(如One Health)时,需要使用扩展的语言来包含一个概念的多种表达。关键词:CAB摘要描述词one health搜索过滤器搜索对冲披露声明作者未报告潜在的利益冲突。
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1.30
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4
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