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

This 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.