BackgroundProton pump inhibitors (PPIs) are one of the most frequently prescribed classes of drug in the world and there is a growing number of publications on correct versus incorrect use of PPIs worldwide. The knowledge of PPIs among the medical staff is essential for improving the rationality of PPI application. The present study aimed to investigate awareness, attitude and behavior toward PPI use among medical staff in the Southwest of China.MethodsThe present descriptive-analytical study was conducted on 900 medical staff from three professional groups (300 doctors, 300 nurses and 300 pharmacists) in China. The study data were collected through a self-designed questionnaire which included demographics, awareness, attitude and behavior toward PPI use. The study was carried out in 22 hospitals in Luzhou between February and June 2018.ResultsOf 900 surveys issued, 851valid questionnaires (295doctors, 268 nurses and 288 pharmacists) were returned. Of all respondents, 33.25% were men and 66.75% were women. The score related to PPI awareness score of medical staff was low (59.47 ± 15.75). The level of awareness of pharmacist was significantly higher than that of doctors and nurses (P < 0.01), which was related to gender, age, occupation, educational level, professional title, hospital nature and hospital grade. Similarly, on the attitude towards PPI use, the pharmacists scored also significantly higher than doctors and the nurses (P < 0.01). Three hundred eighty-one of 851 medical staff had used PPI in the past 1 year, of which omeprazole was the most widely used. Among doctors, nurses and pharmacists, the usage rate of PPI was 50.85, 42.16, 40.97%, respectively. The use frequency was related to occupation and professional title. The score about the behavior toward PPIs of the nurses was also significantly lower than that of doctors and pharmacists (P < 0.01).ConclusionsThe study indicated that the medical staff lack of awareness concerning rational use of PPI in China, especially nurse. Thus, it is necessary to call for action on the improvement of PPI awareness and medication-taking behaviors to reduce PPI overuse and to promote the rationality of PPI application.
With the rapid development of NLP research, leaderboards have emerged as one tool to track the performance of various systems on various NLP tasks. They are effective in this goal to some extent, but generally present a rather simplistic one-dimensional view of the submitted systems, communicated only through holistic accuracy numbers. In this paper, we present a new conceptualization and implementation of NLP evaluation: the EXPLAIN-ABOARD, which in addition to inheriting the functionality of the standard leaderboard, also allows researchers to (i) diagnose strengths and weaknesses of a single system (e.g. what is the best-performing system bad at?) (ii) interpret relationships between multiple systems. (e.g. where does system A outperform system B? What if we combine systems A, B and C?) and (iii) examine prediction results closely (e.g. what are common errors made by multiple systems or in what contexts do particular errors occur?). So far, EXPLAINABOARD covers more than 400 systems, 50 datasets, 40 languages, and 12 tasks. 1 We not only released an online platform at the website 2 but also make our evaluation tool an API with MIT Licence at Github 3 and PyPi 4 that allows users to conveniently assess their models offline. We additionally release all output files from systems that we have run or collected to motivate "output-driven" research in the future.
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