ArticleJMIR diabetes2016
Evaluating the Accuracy of Google Translate for Diabetes Education Material.
Article in JMIR diabetes, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
28 citing papers in PubMed, 74 citations in OpenAlex.
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- The Use of Machine Translation for Outreach and Health Communication in Epidemiology and Public Health: Scoping Review.JMIR public health and surveillance · 2023Article
- Validation testing of a language translation device for suitability in assisting Australian radiation therapists to communicate with Mandarin-speaking patients.Technical innovations & patient support in radiation oncology · 2023Article
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- Language Assistance Services in Nonfederally Funded Safety-Net Medical Clinics in the United States.Health equity · 2022Article
- Testing the use of translation apps to overcome everyday healthcare communication in Australian aged-care hospital wards-An exploratory study.Nursing open · 2022Article
- Comprehension Profile of Patient Education Materials in Endocrine Care.Kansas journal of medicine · 2022Article
- A Pragmatic Assessment of Google Translate for Emergency Department Instructions.Journal of general internal medicine · 2021Article
- Enabling Nurse-Patient Communication With a Mobile App: Controlled Pretest-Posttest Study With Nurses and Non-English-Speaking Patients.JMIR nursing · 2021Article
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Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundApproximately 21% of the US population speaks a language other than English at home; many of these individuals cannot effectively communicate in English. Hispanic and Chinese Americans, in particular, are the two largest minority groups having low health literacy in the United States. Fortunately, machine-generated translations represent a novel tool that non-English speakers can use to receive and relay health education information when human interpreters are not available.
objectiveThe purpose of this study was to evaluate the accuracy of the Google Translate website when translating health information from English to Spanish and English to Chinese.
methodsThe pamphlet, "You are the heart of your family…take care of it," is a health education sheet for diabetes patients that outlines six tips for behavior change. Two professional translators translated the original English sentences into Spanish and Chinese. We recruited 6 certified translators (3 Spanish and 3 Chinese) to conduct blinded evaluations of the following versions: (1) sentences translated by Google Translate, and (2) sentences translated by a professional human translator. Evaluators rated the sentences on four scales: fluency, adequacy, meaning, and severity. We performed descriptive analysis to examine differences between these two versions.
resultsCronbach's alpha values exhibited high degrees of agreement on the rating outcome of both evaluator groups: .919 for the Spanish evaluators and .972 for the Chinese evaluators. The readability of the sentences in this study ranged from 2.8 to 9.0 (mean 5.4, SD 2.7). The correlation coefficients between the grade level and translation accuracy for all sentences translated by Google were negative (eg, r
conclusionGoogle produced a more accurate translation from English to Spanish than English to Chinese. Some sentences translated by Google from English to Chinese exhibit the potential to result in delayed patient care. We recommend continuous training and credential practice standards for professional medical translators to enhance patient safety as well as providing health education information in multiple languages.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.