Evaluation of quality and readability of over-the-counter medication package inserts.

BACKGROUND: Over-the-counter (OTC) medication package inserts are vital references for healthcare professionals to make drug recommendations and influential education materials that patients commonly refer to when self-caring. However, little is known about the quality and readability of the OTC medication package inserts in China. OBJECTIVES: This study aimed at evaluating the quality and readability of OTC medication package inserts. METHODS: Package inserts of OTC medication were selected based on the market share and pharmacological category. The quality of the package inserts was evaluated based on standards adapted separately from the Chinese National Medical Products Administration (NMPA) and European Medicines Agency's (EMA) Working Group. The readability was assessed using the Patient Education Materials Assessment Tool (PEMAT) in conjunction with the Chinese Readability Index Explorer (CRIE). RESULTS: A total of 29 OTC medication package inserts consisting of 12 Western Medicine (WM) and 17 Chinese Tradition Patent Medicine (CTPM) package inserts were included. Overall, the OTC package inserts met 92% of the NMPA standards and 54% of the EMA standards. In terms of readability assessment using PEMAT, the overall median (interquartile range) understandability score was 38% (38-45%) and for actionability score was 40% (40-55%). The overall text reading level of package inserts measured by the CRIE, after removing some medical jargon, is equivalent to the median reading level for the 12th (9.5-12th) grade reading level. CONCLUSIONS: The quality of OTC medication package inserts was satisfactory under internal standards but poor under international standards. Some OTC pharmacological information is not provided due to lack of research, especially for CTPM. A more informative and comprehensive package insert may be needed to guide drug use decisions. OTC medication package inserts are not appropriate patient education materials in terms of readability. Additional materials may be developed to supplement package inserts for patient education for OTC medications.

Evaluation of the quality and readability of online information about breast cancer in China.

OBJECTIVE: This study aimed at evaluating the quality and readability of online information about breast cancer written in Chinese. METHODS: An Internet search was conducted for "breast cancer" in Chinese using the Baidu search engine. Website quality was evaluated using the DISCERN instrument, and readability was evaluated using the Chinese Readability Index Explorer (CRIE). Higher DISCERN score indicated higher quality of websites, while higher CRIE score indicated lower readability of the content of the websites. We also investigated the effects of website producer category, and the associations of search engine ranking with DISCERN and CRIE scores. RESULTS: A total of 49 websites were included. The mean overall DISCERN score was 50.27 ± 4.14, and the mean CRIE score was 6.78 ± 0.16. Websites produced by non-profit organizations had the highest overall DISCERN scores, while those produced by private individuals had the lowest CRIE scores. Search engine ranking had no significant correlation with website quality or readability. CONCLUSIONS: The quality and readability of breast cancer websites in Chinese were not satisfactory, and they varied among different website producer categories. PRACTICE IMPLICATIONS: Website producers should seek to provide more accurate, comprehensive, and easy-to-understand information to better meet the needs of breast cancer patients. In addition, search engines should revise algorithms to promote websites with higher quality and accessibility.

CLAD: A corpus-derived Chinese Lexical Association Database.

The application of word associations has become increasingly widespread. However, the association norms produced by traditional free association tests tend not to exceed 10,000 stimulus words, making the number of associated words too small to be representative of the overall language. In this study we used text corpora totaling over 400 million Chinese words, along with a multitude of association measures, to automatically construct a Chinese Lexical Association Database (CLAD) comprising the lexical association of over 80,000 words. Comparison of the CLAD with a database of traditional Chinese word association norms shows that word associations extracted from large text corpora are similar in strength to those elicited from free association tests but contain a much greater number of associative word pairs. Additionally, the relatively small numbers of participants involved in the creation of traditional norms result in relatively coarse scales of association measurement, whereas the differentiation of association strengths is greatly enhanced in the CLAD. The CLAD provides researchers with a great supplement to traditional word association norms. A query website at www.chinesereadability.net/LexicalAssociation/CLAD/ affords access to the database.

CRIE: An automated analyzer for Chinese texts.

Textual analysis has been applied to various fields, such as discourse analysis, corpus studies, text leveling, and automated essay evaluation. Several tools have been developed for analyzing texts written in alphabetic languages such as English and Spanish. However, currently there is no tool available for analyzing Chinese-language texts. This article introduces a tool for the automated analysis of simplified and traditional Chinese texts, called the Chinese Readability Index Explorer (CRIE). Composed of four subsystems and incorporating 82 multilevel linguistic features, CRIE is able to conduct the major tasks of segmentation, syntactic parsing, and feature extraction. Furthermore, the integration of linguistic features with machine learning models enables CRIE to provide leveling and diagnostic information for texts in language arts, texts for learning Chinese as a foreign language, and texts with domain knowledge. The usage and validation of the functions provided by CRIE are also introduced.

Semi-Supervised Text Classification With Universum Learning.

Universum, a collection of nonexamples that do not belong to any class of interest, has become a new research topic in machine learning. This paper devises a semi-supervised learning with Universum algorithm based on boosting technique, and focuses on situations where only a few labeled examples are available. We also show that the training error of AdaBoost with Universum is bounded by the product of normalization factor, and the training error drops exponentially fast when each weak classifier is slightly better than random guessing. Finally, the experiments use four data sets with several combinations. Experimental results indicate that the proposed algorithm can benefit from Universum examples and outperform several alternative methods, particularly when insufficient labeled examples are available. When the number of labeled examples is insufficient to estimate the parameters of classification functions, the Universum can be used to approximate the prior distribution of the classification functions. The experimental results can be explained using the concept of Universum introduced by Vapnik, that is, Universum examples implicitly specify a prior distribution on the set of classification functions.

Constructing and validating readability models: the method of integrating multilevel linguistic features with machine learning.

Multilevel linguistic features have been proposed for discourse analysis, but there have been few applications of multilevel linguistic features to readability models and also few validations of such models. Most traditional readability formulae are based on generalized linear models (GLMs; e.g., discriminant analysis and multiple regression), but these models have to comply with certain statistical assumptions about data properties and include all of the data in formulae construction without pruning the outliers in advance. The use of such readability formulae tends to produce a low text classification accuracy, while using a support vector machine (SVM) in machine learning can enhance the classification outcome. The present study constructed readability models by integrating multilevel linguistic features with SVM, which is more appropriate for text classification. Taking the Chinese language as an example, this study developed 31 linguistic features as the predicting variables at the word, semantic, syntax, and cohesion levels, with grade levels of texts as the criterion variable. The study compared four types of readability models by integrating unilevel and multilevel linguistic features with GLMs and an SVM. The results indicate that adopting a multilevel approach in readability analysis provides a better representation of the complexities of both texts and the reading comprehension process.