scholarly journals Clinical skills examination as part of the Taiwan National Medical Licensing Examination

2012 ◽  
Vol 35 (2) ◽  
pp. 173-173 ◽  
Author(s):  
Keh-Min Liu ◽  
Tsuen-Chiuan Tsai ◽  
Shih-Li Tsai
Author(s):  
Rachel B. Levine ◽  
Andrew P. Levy ◽  
Robert Lubin ◽  
Sarah Halevi ◽  
Rebeca Rios ◽  
...  

Purpose: United States (US) and Canadian citizens attending medical school abroad often desire to return to the US for residency, and therefore must pass US licensing exams. We describe a 2-day United States Medical Licensing Examination (USMLE) step 2 clinical skills (CS) preparation course for students in the Technion American Medical School program (Haifa, Israel) between 2012 and 2016.Methods: Students completed pre- and post-course questionnaires. The paired t-test was used to measure students’ perceptions of knowledge, preparation, confidence, and competence in CS pre- and post-course. To test for differences by gender or country of birth, analysis of variance was used. We compared USMLE step 2 CS pass rates between the 5 years prior to the course and the 5 years during which the course was offered.Results: Ninety students took the course between 2012 and 2016. Course evaluations began in 2013. Seventy-three students agreed to participate in the evaluation, and 64 completed the pre- and post-course surveys. Of the 64 students, 58% were US-born and 53% were male. Students reported statistically significant improvements in confidence and competence in all areas. No differences were found by gender or country of origin. The average pass rate for the 5 years prior to the course was 82%, and the average pass rate for the 5 years of the course was 89%.Conclusion: A CS course delivered at an international medical school may help to close the gap between the pass rates of US and international medical graduates on a high-stakes licensing exam. More experience is needed to determine if this model is replicable.


2020 ◽  
Vol 34 (05) ◽  
pp. 8822-8829
Author(s):  
Sheng Shen ◽  
Yaliang Li ◽  
Nan Du ◽  
Xian Wu ◽  
Yusheng Xie ◽  
...  

Question answering (QA) has achieved promising progress recently. However, answering a question in real-world scenarios like the medical domain is still challenging, due to the requirement of external knowledge and the insufficient quantity of high-quality training data. In the light of these challenges, we study the task of generating medical QA pairs in this paper. With the insight that each medical question can be considered as a sample from the latent distribution of questions given answers, we propose an automated medical QA pair generation framework, consisting of an unsupervised key phrase detector that explores unstructured material for validity, and a generator that involves a multi-pass decoder to integrate structural knowledge for diversity. A series of experiments have been conducted on a real-world dataset collected from the National Medical Licensing Examination of China. Both automatic evaluation and human annotation demonstrate the effectiveness of the proposed method. Further investigation shows that, by incorporating the generated QA pairs for training, significant improvement in terms of accuracy can be achieved for the examination QA system. 1


2007 ◽  
Vol 82 (Suppl) ◽  
pp. S101-S104 ◽  
Author(s):  
Chaitanya Ramineni ◽  
Polina Harik ◽  
Melissa J. Margolis ◽  
Brian E. Clauser ◽  
David B. Swanson ◽  
...  

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