School of Languages and Cultures, The University of Sydney, Sydney 2006, Australia.
Department of African Studies, The University of Vienna, A-1090 Vienna, Austria.
Int J Environ Res Public Health. 2021 Sep 30;18(19):10329. doi: 10.3390/ijerph181910329.
Effective multilingual communication of authoritative health information plays an important role in helping to reduce health disparities and inequalities in developed and developing countries. Health information communication from the World Health Organization is governed by key principles including health information relevance, credibility, understandability, actionability, accessibility. Multilingual health information developed under these principles provide valuable benchmarks to assess the quality of health resources developed by local health authorities. In this paper, we developed machine learning classifiers for health professionals with or without Chinese proficiency to assess public-oriented health information in Chinese based on the definition of effective health communication by the WHO. We compared our optimized classifier (SVM) with the state-of-art Chinese readability classifier (Chinese Readability Index Explorer CRIE 3.0), and classifiers adapted from established English readability formula, Gunning Fog Index, Automated Readability Index. Our optimized classifier achieved statistically significant higher area under the receiver operator curve (AUC of ROC), accuracy, sensitivity, and specificity than those of SVM using CRIE 3.0 features and SVM using linguistic features of Gunning Fog Index and Automated Readability Index (ARI). The statistically improved performance of our optimized classifier compared to that of SVM classifiers adapted from popular readability formula suggests that evaluation of health communication effectiveness as defined by the principles of the WHO is more complex than information readability assessment. Our SVM classifier validated on health information covering diverse topics (environmental health, infectious diseases, pregnancy, maternity care, non-communicable diseases, tobacco control) can aid effectively in the automatic assessment of original, translated Chinese public health information of whether they satisfy or not the current international standard of effective health communication as set by the WHO.
有效的多语种权威健康信息传播在帮助减少发达国家和发展中国家的健康差距和不平等方面发挥着重要作用。世界卫生组织的健康信息传播受包括健康信息相关性、可信度、可理解性、可操作性和可及性在内的关键原则的约束。在这些原则下开发的多语种健康信息为评估地方卫生当局开发的健康资源的质量提供了有价值的基准。在本文中，我们为有或没有中文水平的卫生专业人员开发了机器学习分类器，根据世界卫生组织对有效健康沟通的定义，用中文评估面向公众的健康信息。我们将我们优化后的分类器（支持向量机）与最先进的中文可读性分类器（中文可读性指数探索器 3.0，Chinese Readability Index Explorer CRIE 3.0）进行了比较，并将从已建立的英语可读性公式（Gunning Fog Index、Automated Readability Index）改编的分类器与 SVM 进行了比较。我们优化后的分类器的接收者操作特征曲线（ROC 曲线下的 AUC）、准确性、敏感性和特异性均显著高于基于 CRIE 3.0 特征的 SVM 分类器和基于 Gunning Fog Index 和 Automated Readability Index 语言特征的 SVM 分类器。与基于流行可读性公式的 SVM 分类器相比，我们优化后的分类器的性能有统计学上的提高，这表明，根据世卫组织的原则评估健康信息传播的有效性比信息可读性评估更为复杂。我们在涵盖不同主题（环境卫生、传染病、妊娠、产妇保健、非传染性疾病、烟草控制）的健康信息上验证的 SVM 分类器，可以有效地帮助自动评估原始的、翻译后的中文公共卫生信息是否符合当前世卫组织设定的有效健康信息传播的国际标准。
