Evaluating the Accuracy of Google Translate for Diabetes Education Material
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Approximately 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. OBJECTIVE: The 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. METHODS: The 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. RESULTS: =-.660), which indicates that Google provided accurate translation for simple sentences. However, the likelihood of incorrect translation increased when the original English sentences required higher grade levels to comprehend. The Chinese human translator provided more accurate translation compared to Google. The Spanish human translator, on the other hand, did not provide a significantly better translation compared to Google. CONCLUSION: Google 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it