Farhat Hassan, Alinier Guillaume, Howland Ian, Kanoun Houcine, Chaker Khenssi Mohamed, Al Shaikh Loua, Laughton James
Ambulance Service, Hamad Medical Corporation, Doha, Qatar.
Qatar Med J. 2025 Mar 3;2025(1):19. doi: 10.5339/qmj.2025.19. eCollection 2025.
Language barriers significantly impact healthcare delivery, particularly in emergency medical services (EMS) operating in linguistically diverse environments. The demographic composition of Qatar, with its predominantly expatriate population, presents unique challenges for effective communication in pre-hospital care settings. The aim of this was to assess the opinions of personnel from the Hamad Medical Corporation Ambulance Service (HMCAS) regarding the impact of language barriers on pre-hospital emergency care.
A cross-sectional study was conducted using an anonymous survey with a five-point Likert scale among 312 frontline personnel of HMCAS. Fisher's exact and Kruskal-Wallis tests were used to compare ordinal outcomes across groups. Machine learning algorithms, including ordinal logistic regression, support vector machines (SVM), and naive Bayes, were used to develop predictive models for HMCAS staff opinions on their language learning needs.
Both bivariate and multivariate analyses revealed significant differences in the frequency of experiencing communication challenges. The most influential factors identified were strong opinions on language barriers and the willingness of staff to enhance their language skills. Variables related to using family members as interpreters showed relatively low importance. The SVM model demonstrated the best predictive capability concerning staff perceptions about language learning needs, with an accuracy of 0.50 and an average area under the curve score of 0.74.
Language barriers significantly impact pre-hospital emergency care in Qatar. The findings highlight the need for targeted interventions, such as language training programs and mobile translation apps. These strategies could enhance communication in multicultural EMS settings, improving patient care and reducing miscommunication risks. Future research should evaluate the long-term impact of these interventions on patient outcomes.
语言障碍对医疗服务的提供有重大影响，尤其是在语言环境多样的紧急医疗服务（EMS）中。卡塔尔的人口构成以外籍人口为主，这给院前护理环境中的有效沟通带来了独特挑战。本研究的目的是评估哈马德医疗公司救护车服务（HMCAS）的工作人员对语言障碍对院前急救影响的看法。
采用匿名调查，对HMCAS的312名一线人员进行五点李克特量表的横断面研究。使用费舍尔精确检验和克鲁斯卡尔 - 沃利斯检验比较各组的有序结果。使用包括有序逻辑回归、支持向量机（SVM）和朴素贝叶斯在内的机器学习算法，为HMCAS工作人员对其语言学习需求的看法建立预测模型。
双变量和多变量分析均显示在经历沟通挑战的频率上存在显著差异。确定的最有影响力的因素是对语言障碍的强烈看法以及工作人员提高语言技能的意愿。与使用家庭成员作为口译员相关的变量显示重要性相对较低。SVM模型在工作人员对语言学习需求的认知方面表现出最佳的预测能力，准确率为0.50，曲线下面积平均得分为0.74。
语言障碍对卡塔尔的院前急救有重大影响。研究结果强调了有针对性干预措施的必要性，如语言培训项目和移动翻译应用程序。这些策略可以加强多元文化EMS环境中的沟通，改善患者护理并降低沟通失误风险。未来的研究应评估这些干预措施对患者结局的长期影响。
