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Recently, the number of Myanmar workers immigrating to Thailand has increased continuously. High health-care cost and language problems are relevant impediments for them to get access to services from hospitals. Therefore, drugstores have become a preferential health care service for these people when suffering from an ailment. Nevertheless, problems in communication between Thai pharmacists and Myanmar patients still persist. To alleviate this problem, we expected that Google Translate can be used as a communication tool. The aim of this study was to evaluate the accuracy of Google Translate in translating conversation texts used in drugstores among Thai, English and Burmese prior to being implemented in a real situation. Two levels of language structure were evaluated, i.e., the word level evaluated by the averages of acceptance rates and the phrase or sentence level evaluated by adequacy and fluency. The results demonstrated that the quality of the texts translated from Google Translate was varying in a pharmaceutical context. Inaccuracy occurred in both the word level and the phrase or sentence level. Thus, Google Translate might be only appropriately used as an initial tool and outputs of translation should be modified to be more accurate. Pharmacists should use English as a source language instead of Thai to translate into Burmese. Likewise, in case when patients use Burmese as a source language, the language displayed should be English.
2. Tharathep C, Thamroj N, Jaritake P. A study of service seeking and service using behaviour to improve health financial system and provide suitable services corresponding with requirement of foregin labors: Cases study from Samutsakorn Province and Rayong Province. Nonthaburi: Health Systems Research Institute; 2011. (in Thai).
3. Korsanan S. Roles, characteristics and abilities of pharmacists working in accredited drug stores. Journal of HR Intelligence. 2012;7(2): 47-52. (in Thai).
4. Giese A, Uyar M, Uslucan HH, Becker S, Henning BF. How do hospitalised patients with Turkish migration background estimate their language skills and their comprehension ofmedical information - a prospective cross-sectional study and comparison to native patients in Germany to assess the language barrier and the need for translation. BMC Health Serv Res. 2013;13:196.
5. Karliner LS, Hwang ES, Nickleach D, Kaplan CP. Language barriers and patient-centered breast cancer care. Patient Educ Couns. 2011;84(2):223-8.
6. Li C, Son N, Abdulkerim BA, Jordan CA, Son CGE. Overcoming communication barriers to healthcare for culturally and linguistically diverse patients. N Am J Med Sci. 2017;10(3):103-9.
7. Meuter RF, Gallois C, Segalowitz NS, Ryder AG, Hocking J. Overcoming language barriers in healthcare: A protocol for investigating safe and effective communication when patients or clinicians use a second language. BMC Health Serv Res. 2015;15:371.
8. Google Incorperated. Google Translate [Internet]. 2006 [cited 2016 Aug 15]; Available from: https://translate. google. com? hlth
9. Boitet C, Blanchon H, Seligman M, Bellynck V. Evolution of MT with the web. Proceedings of International conference "Machine translation 25 years on". 21st-22nd November 2009; England. Bedfordshire: Cranfield University; p. 1-13.
10. Ghasemi H, Hashemian M. A comparative study of "Google Translate" translations: An error analysis of English-to-Persian and Persian-to-English translations. English Language Teaching. 2016;9(3):13-7.
11. Koehn P. Statistical machine translation. Cambridge, England: Cambridge University Press; 2009.
12. Li H, Graesser AC, Cai Z. Comparison of Google Translation with human translation. In: Eberle W, Denecke CB, editors. Proceedings of the twenty-seventh international Florida artificial intelligence research society conference. 21st-23rd May 2014; Pensacola Beach, Florida. Palo Alto, California: The AAAI Press; p. 190-5.
13. Dhakar BS, Sinha SK, Pandey KK. A survey of translation quality of English to Hindi online translation systems (Google and Bing). International Journal of Scientific and Research Publications. 2013;3(1):1-4.
14. Costa-jussà MR, Farrús M, Pons JS. Machine translation in medicine. In: Mokryš M, Lieskovský A, editors. Proceedings of The 1st virtual international conference on advanced research in scientific fields (ARSA-2012). 3rd-7st December 2012; Slovakia. Žilina, Slovakia: Institution of the University of Zilina; p.1995-8.
15. Chen X, Acosta S, Barry AE. Evaluating the accuracy of Google Translate for diabetes education material. JMIR Diabetes. 2016;1(1) :e3. doi: 10.2196/diabetes.5848.
16. Internet World Stats. Internet world users by language [Internet]. 2017 [cited 2018 Apr 26]; Available from: https://www.Internetworld stats.com/stats7.htm.