谷歌翻译在心理健康信息中的错误分析：评估准确性、可理解性及其对多语言医疗沟通的影响
Google Translate Error Analysis for Mental Healthcare Information: Evaluating Accuracy, Comprehensibility, and Implications for Multilingual Healthcare Communication
AI总结：
本研究分析谷歌翻译在心理健康信息翻译中的准确性、可理解性及错误，发现医学术语和流畅性存在挑战，强调需定制化翻译引擎和人工审校以确保多语言医疗沟通质量。
摘要：
本研究探讨了使用谷歌翻译（GT）翻译心理健康（MHealth）信息的情况，并通过分析从英语到波斯语、阿拉伯语、土耳其语、罗马尼亚语和西班牙语的MHealth领域GT输出，评估其准确性、可理解性及其对多语言医疗沟通的影响。研究使用了两个数据集，分别来自英国国家医疗服务体系网站上的MHealth信息和皇家精神科医学院的信息传单。目标语言的母语者人工评估了GT翻译，重点关注医学术语准确性、可理解性以及关键的句法/语义错误。GT输出分析揭示了准确翻译医学术语的挑战，尤其是在阿拉伯语、罗马尼亚语和波斯语中。流畅性问题在多种语言中普遍存在，影响理解，主要出现在阿拉伯语和西班牙语中。关键错误出现在特定情境中，如项目符号格式，尤其是在波斯语、土耳其语和罗马尼亚语中。尽管在较长文本的翻译中看到了改进，但仍需提高医学和心理健康术语的准确性及流畅性，同时解决格式问题以提供更无缝的用户体验。研究结果强调了为MHealth翻译使用定制化翻译引擎的必要性，以及仅依赖机器翻译医疗内容所面临的挑战，突出了人工审校者在多语言医疗沟通中的关键作用。
英文摘要：
This study explores the use of Google Translate (GT) for translating mental healthcare (MHealth) information and evaluates its accuracy, comprehensibility, and implications for multilingual healthcare communication through analysing GT output in the MHealth domain from English to Persian, Arabic, Turkish, Romanian, and Spanish. Two datasets comprising MHealth information from the UK National Health Service website and information leaflets from The Royal College of Psychiatrists were used. Native speakers of the target languages manually assessed the GT translations, focusing on medical terminology accuracy, comprehensibility, and critical syntactic/semantic errors. GT output analysis revealed challenges in accurately translating medical terminology, particularly in Arabic, Romanian, and Persian. Fluency issues were prevalent across various languages, affecting comprehension, mainly in Arabic and Spanish. Critical errors arose in specific contexts, such as bullet-point formatting, specifically in Persian, Turkish, and Romanian. Although improvements are seen in longer-text translations, there remains a need to enhance accuracy in medical and mental health terminology and fluency, whilst also addressing formatting issues for a more seamless user experience. The findings highlight the need to use customised translation engines for Mhealth translation and the challenges when relying solely on machine-translated medical content, emphasising the crucial role of human reviewers in multilingual healthcare communication.
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