Evidence map›Paper›PMID 30291056›Full record

ArticleJMIR diabetes2016

Evaluating the Accuracy of Google Translate for Diabetes Education Material.

Xuewei Chen, Sandra Acosta, Adam Etheridge Barry

Open access · goldAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed
10.9field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

28 citing papers in PubMed, 74 citations in OpenAlex.

  1. Article
  2. Observational
  3. Article
  4. Article
  5. Review
  6. Article
  7. Review
  8. When is good, good enough? On considerations of machine translation in patient education.Journal of cancer education : the official journal of the American Association for Cancer Education · 2024
    Article
  9. Article
  10. Review
  11. Providing care to marginalised communities: a qualitative study of community pharmacy teams.The British journal of general practice : the journal of the Royal College of General Practitioners · 2024
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Race/Ethnicity, Nativity Status, and Patient Portal Access and Use.Journal of health care for the poor and underserved · 2021
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Xuewei ChenTransdisciplinary Center for Health Equity Research, Department of Health and Kinesiology, Texas A&M University, College Station, TX, United States.ORCID http://orcid.org/0000-0001-6391-1459
Sandra AcostaTexas A&M University, Department of Educational Psychology, Texas A&M University, College Station, TX, United States.ORCID http://orcid.org/0000-0002-6433-3256
Adam Etheridge BarryTexas A&M University, Department of Health and Kinesiology, Texas A&M University, College Station, TX, United States.ORCID http://orcid.org/0000-0001-6527-6866
Texas A&M University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

diabeteshealth communicationhealth educationhealth literacyhuman interpreterlanguage translationmachine translationtranslator

Identifiers

PMID30291056
PMCPMC6238856
OpenAlexW2508198244

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.