Narayan Lalit
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Natl Med J India. 2013 Jul-Aug;26(4):236-8.
In spite of a growing recognition of the importance of doctor-patient communication, the issue of language barriers to healthcare has received very little attention in India. The Indian population speaks over 22 major languages with English used as the lingua franca for biomedicine. Large-scale internal migration has meant that health workers are encountering increasing instances of language discordance within clinical settings. Research done predominantly in the West has shown language discordance to significantly affect access to care, cause problems of comprehension and adherence, and decrease the satisfaction and quality of care. Addressing language barriers to healthcare in India requires a stronger political commitment to providing non-discriminatory health services, especially to vulnerable groups such as illiterate migrant workers. Research will have to address three broad areas: the ways in which language barriers affect health and healthcare, the efficacy of interventions to overcome language barriers, and the costs of language barriers and efforts to overcome them. There is a need to address such barriers in health worker education and clinical practice. Proven strategies such as hiring multilingual healthcare workers, providing language training to health providers, employing in situ translators or using telephone interpretation services will have to be evaluated for their appropriateness to the Indian context. Internet-based initiatives, the proliferation of mobile phones and recent advances in machine translation promise to contribute to the solution.
尽管医患沟通的重要性日益受到认可,但在印度,医疗保健中的语言障碍问题却很少受到关注。印度人口使用超过22种主要语言,英语作为生物医学的通用语。大规模的国内移民意味着卫生工作者在临床环境中遇到语言不一致的情况越来越多。主要在西方进行的研究表明,语言不一致会严重影响医疗服务的可及性,引发理解和依从性问题,并降低医疗服务的满意度和质量。解决印度医疗保健中的语言障碍需要更强有力的政治承诺,以提供非歧视性的医疗服务,特别是针对文盲农民工等弱势群体。研究将不得不涉及三个广泛领域:语言障碍影响健康和医疗保健的方式、克服语言障碍的干预措施的效果,以及语言障碍的成本和克服这些障碍的努力。有必要在卫生工作者教育和临床实践中解决此类障碍。必须评估一些行之有效的策略,如雇佣多语言卫生工作者、为卫生服务提供者提供语言培训、聘用现场翻译或使用电话口译服务,看其是否适用于印度的情况。基于互联网的举措、手机的普及以及机器翻译的最新进展有望为解决这一问题做出贡献。