学习outcome英文翻译.docxVIP

SummaryIn this study, the main objectives for constructing logistic and Cox regression models were accomplished. For logistic regression analysis, the explanatory variables contributing to the probability of a student passing the USMLE Step 1 were identified. It was evident that the MCAT (physical and biological sciences) scores, number of sophomore courses failed, and medical school freshman GPAs were significantly associated with the USMLE Step 1 performances. The study results confirmed that MCAT scores and medical school course performances were significant predictors of the USMLE Step 1 (Chen et al., 2001; and Haught and Walls, 2002). The implication of the study results was that the medical school should continue its effort to recruit and admit qualifying students with high MCAT scores, and to strengthen teaching and learning to ensure student success on the licensure examination.With regard to Cox regression analysis, the method indicated that academic difficulty was significantly accounted for by risk factors such as MCAT verbal reasoning score, gender, and number of sophomore courses failed. Moreover, students in the five-year curriculum track experienced academic difficulty during the first semester of their sophomore year, peaked at the second semester of the sophomore year and maintained the same level of risk through the rest of the study period. The research results were consistent with the literature stating that an increase in the relative risk for a student experiencing academic difficulty was significantly associated with a low MCAT score (Huff and Fang, 1999), and students at risk for academic difficulty remained at risk throughout the first three years of medical school (Fang, 2000). The implication of this study was that the medical school addressed academic difficulty issues through academic development and support services.在这项研究中，完成了构建Logistic和Cox回归模型的主要目标。Logistic回归分析，有助于学生通过USMLE第1步确定解释变量的概率。显然，MCAT（物理和生物科学）成绩，大二课程失败次数和医学院新生的平均成绩明显与USMLE 1的表