Wind, Lauren L.Briganti, JonathanBrown, Anne M.Neher, Timothy P.Davis, Meghan F.Durso, Lisa M.Spicer, TannerLansing, Stephanie2021-04-092021-04-092021-04-03Wind, L.L.; Briganti, J.S.; Brown, A.M.; Neher, T.P.; Davis, M.F.; Durso, L.M.; Spicer, T.; Lansing, S. Finding What Is Inaccessible: Antimicrobial Resistance Language Use among the One Health Domains. Antibiotics 2021, 10, 385.http://hdl.handle.net/10919/103000The success of a One Health approach to combating antimicrobial resistance (AMR) requires effective data sharing across the three One Health domains (human, animal, and environment). To investigate if there are differences in language use across the One Health domains, we examined the peer-reviewed literature using a combination of text data mining and natural language processing techniques on 20,000 open-access articles related to AMR and One Health. Evaluating AMR key term frequency from the European PubMed Collection published between 1990 and 2019 showed distinct AMR language usage within each domain and incongruent language usage across domains, with significant differences in key term usage frequencies when articles were grouped by the One Health sub-specialties (2-way ANOVA; <i>p</i> &lt; 0.001). Over the 29-year period, “antibiotic resistance” and “AR” were used 18 times more than “antimicrobial resistance” and “AMR”. The discord of language use across One Health potentially weakens the effectiveness of interdisciplinary research by creating accessibility issues for researchers using search engines. This research was the first to quantify this disparate language use within One Health, which inhibits collaboration and crosstalk between domains. We suggest the following for authors publishing AMR-related research within the One Health context: (1) increase title/abstract searchability by including both antimicrobial and antibiotic resistance related search terms; (2) include “One Health” in the title/abstract; and (3) prioritize open-access publication.application/pdfenCreative Commons Attribution 4.0 Internationalone healthantimicrobial resistanceantibiotic resistancehumananimalenvironmenttext data miningnatural language processingcommon languageAMRARArticle - Refereed2021-04-09https://doi.org/10.3390/antibiotics10040385