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.

Highlights

  • Education Materials Assessment Tool (PEMAT) which extended the evaluation of the quality of user-oriented health information from readability, understandability to include

  • The means of the remaining linguistic features in the two sets of Chinese health texts were insignificant with p larger than 0.05. These findings suggest that non-WHO original Chinese health resources were more complex orthographically, lexically, and syntactically and information was presented in larger chunks of paragraphs

  • We adapted the SVM radial basis function (RBF) model to a linear kernel SVM using the refined feature set of the Chinese Readability Index Explorer (CRIE) 3.0 system which had as many as 29 features measuring orthographic complexity, lexical complexity, information load, cognitive load, information cohesion

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Summary

Introduction

Effective multilingual communication of authoritative health information plays an important role in helping to reduce health disparities, inequalities in developed and developing countries. The evaluation of the quality of public-oriented health information focused on readability assessment [1,2,3,4,5,6,7]. In 2013, the Agency for Healthcare Research and Quality of the U.S Department of Health and Human Services developed the Patient. Education Materials Assessment Tool (PEMAT) which extended the evaluation of the quality of user-oriented health information from readability, understandability to include.

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