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The purpose of the study was to explore differences in Google search autocompletes between English and Spanish-speaking users during the first wave of the coronavirus disease 2019 (COVID-19) pandemic. Twenty-nine individuals who were in areas with shelter-in-place state orders participated in a virtual focus group meeting to understand the algorithm bias of COVID-19 Google autocompletes. The three focus group meetings lasted for 90-120 minutes. A codebook was created and transcripts were coded using NVivo qualitative software with a 95% intercoder reliability between two coders. Thematic analysis was used to analyze the data. Among the 29 participants, six self-identified as White, seven as Black/African American, five as American Indian or Alaska Native, four as Asian Indian, and three as Native Hawaiian or Pacific Islander. In terms of ethnicity, 21 participants identified as Hispanic/Latino. The themes that emerged from the study were: (1) autocompletes evoked fear and stress; (2) skepticism and hesitation towards autocomplete search; (3) familiarity with COVID-19 information impacts outlook on autocomplete search; (4) autocompletes can promote preselection of searches; and (5) lesser choice of autocomplete results for Spanish-speaking searchers. Spanish speakers expressed concerns and hesitation due to social factors and lack of information about COVID-19.
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http://dx.doi.org/10.1002/jcop.23013 | DOI Listing |
Health Informatics J
December 2024
School of Communication & Information, Rutgers University, New Brunswick, NJ, USA.
To audit and compare search autocomplete results in Spanish and English during the early COVID-19 pandemic in the New York metropolitan area. The pandemic led to significant online search activity about the disease, its spread, and remedies. As gatekeepers, search engines like Google can influence public opinion.
View Article and Find Full Text PDFJ Community Psychol
July 2024
Department of Urban-Global Public Health, Rutgers School of Public Health, Newark, NJ, USA.
The purpose of the study was to explore differences in Google search autocompletes between English and Spanish-speaking users during the first wave of the coronavirus disease 2019 (COVID-19) pandemic. Twenty-nine individuals who were in areas with shelter-in-place state orders participated in a virtual focus group meeting to understand the algorithm bias of COVID-19 Google autocompletes. The three focus group meetings lasted for 90-120 minutes.
View Article and Find Full Text PDFAMIA Annu Symp Proc
February 2007
Office of High Performance Computing and Communications, National Library of Medicine, 8600 Rockville Pike, Bethesda, Maryland 20894, USA.
Medical terminology is challenging even for healthcare personnel. Spelling errors can make searching MEDLINE/PubMed ineffective. We developed a utility that provides MeSH term and Specialist Lexicon Vocabulary suggestions as it is typed on a search page.
View Article and Find Full Text PDF