Taylor Rachel M, Crichton Nicola, Moult Beki, Gibson Faith
School of Health & Social Care London South Bank University London UK; Cancer Clinical Trials Unit University College London Hospitals NHS Foundation Trust LondonUK.
School of Health & Social Care London South Bank University London UK.
Nurs Open. 2015 Jan 29;2(1):14-23. doi: 10.1002/nop2.13. eCollection 2015 Apr.
This study investigates whether machine translation could help with the challenge of enabling the inclusion of ethnic diversity in healthcare research.
A two phase, prospective observational study.
Two machine translators, Google Translate and Babylon 9, were tested. Translation of the Strengths and Difficulties Questionnaire (SDQ) from 24 languages into English and translation of an English information sheet into Spanish and Chinese were quality scored. Quality was assessed using the Translation Assessment Quality Tool.
Only six of the 48 translations of the SDQ were rated as acceptable, all from Google Translate. The mean number of acceptably translated sentences was higher (= 0·001) for Google Translate 17·1 (sd 7·2) than for Babylon 9 11 (sd 7·9). Translation by Google Translate was better for Spanish and Chinese, although no score was in the acceptable range. Machine translation is not currently sufficiently accurate without editing to provide translation of materials for use in healthcare research.
本研究调查机器翻译是否有助于应对在医疗保健研究中纳入种族多样性所面临的挑战。
一项分为两个阶段的前瞻性观察性研究。
对两款机器翻译工具谷歌翻译和巴比伦9号进行测试。对从24种语言翻译成英语的长处与困难问卷（SDQ）以及将一份英语信息表翻译成西班牙语和中文的翻译进行质量评分。使用翻译评估质量工具评估质量。
SDQ的48个翻译中只有6个被评为可接受，均来自谷歌翻译。谷歌翻译可接受翻译句子的平均数量更高（=0.001），为17.1（标准差7.2），而巴比伦9号为11（标准差7.9）。谷歌翻译对西班牙语和中文的翻译更好，尽管没有分数处于可接受范围内。目前，未经编辑的机器翻译不够准确，无法提供用于医疗保健研究的材料翻译。
