Assessing Communicative Effectiveness of Public Health Information in Chinese: Developing Automatic Decision Aids for International Health Professionals

Meng Ji, Adams Bodomo, Wenxiu Xie and Riliu Huang
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Meng Ji: School of Languages and Cultures, The University of Sydney, Sydney 2006, Australia
Adams Bodomo: Department of African Studies, The University of Vienna, A-1090 Vienna, Austria
Wenxiu Xie: Department of Computer Science, City University of Hong Kong, Hong Kong 518057, China
Riliu Huang: School of Languages and Cultures, The University of Sydney, Sydney 2006, Australia

IJERPH, 2021, vol. 18, issue 19, 1-11

Abstract: 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 _F5 ) 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.

Keywords: health translation; Chinese health resources; readability; health translation; Chinese health resources; readability; accessibility (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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