Predictors of first-year GPA of medical students: a longitudinal study of 1285 matriculates in China

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• 作者：Ying-Xue Zhou (1)
  Zhi-Tao Zhao (2)
  Li Li (1)
  Cheng-Song Wan (1)
  Cheng-Hua Peng (2)
  Jun Yang (1)
  Chun-Quan Ou (1)
  
  1. School of Public Health and Tropical Medicine ; Southern Medical University ; Guangzhou ; 510515 ; China
  2. Office of Academic Affairs ; Southern Medical University ; Guangzhou ; 510515 ; China
  
• 关键词：National College Entrance Examination ; First ; year GPA ; Medical students ; China ; Hierarchical linear model
• 刊名：BMC Medical Education
• 出版年：2014
• 出版时间：December 2014
• 年：2014
• 卷：14
• 期：1
• 全文大小：206 KB
• 参考文献：1. Mills, C, Heyworth, J, Rosenwax, L, Carr, S, Rosenberg, M (2009) Factors associated with the academic success of first year health science students. Adv Health Sci Educ 14: pp. 205-217 CrossRef
  2. Pascarella, ET, Terenzini, PT (2005) How College Affects Students, Volume 2: A Third Decade of Research. Jossey-Bass, San Francisco, CA
  3. Reason, RD, Terenzini, PT, Domingo, RJ (2006) First things first: developing academic competence in the first year of college. Res High Educ 47: pp. 149-175 CrossRef
  4. Education statistics: Ministry of Education of the People鈥檚 Republic of China Web site. http://www.moe.gov.cn/publicfiles/business/htmlfiles/moe/s7382/list.html
  5. Robbins, SB, Lauver, K, Le, H, Davis, D, Langley, R, Carlstrom, A (2004) Do psychosocial and study skill factors predict college outcomes? A meta-analysis. Psychol Bull 130: pp. 261-288 CrossRef
  6. Kobrin, JL, Patterson, BF (2011) Contextual factors associated with the validity of SAT scores and high school GPA for predicting first-year college grades. Educ Assess 16: pp. 207-226 CrossRef
  7. Callahan, CA, Hojat, M, Veloski, J, Erdmann, JB, Gonnella, JS (2010) The predictive validity of three versions of the MCAT in relation to performance in medical school, residency, and licensing examinations: a longitudinal study of 36 classes of Jefferson medical college. Acad Med 85: pp. 980-987 CrossRef
  8. Bai C-E, Chi W: Determinants of undergraduate GPAs in China: college entrance examination scores, high school achievement, and admission route. http://mpra.ub.uni-muenchen.de/31240/
  9. Xu, P, Zhang, Y-H, Shang, L, Zeng, Y, Wang, Y-Y, Wang, Q (2009) Correlation Analysis of the quality of medical student source and their examination performance of medical courses [in Chinese]. Northwest Med Educ 17: pp. 505-506
  10. Lu, W, Yang, Y, Zhang, J-G, Ding, W, Le, Z-L, Hu, Y-Q (2012) Study of medical undergraduates鈥?talent based on scores of National Matriculation Test and medical courses [in Chinese]. J Shanghai Jiaotong Univ (Med Sci) 32: pp. 1373-1377
  11. Yuan, H, Lei, J (2009) Correlation analysis of the college performance and college entrance exam scores of nurses [in Chinese]. Sichuan J Physiol Sci 31: pp. 94-95
  12. House, JD (2000) Relationships between self-beliefs, academic background and achievement of undergraduate students in health sciences majors. Int J Instr Media 27: pp. 427-438
  13. Schunk, DH, Pintrich, PR, Meece, JL (2008) Motivation in education: Theory, research and applications. Upper Saddle River, NJ, Merrill-Prentice Hall
  14. Kuh, GD, Gonyea, RM, Williams, JM What students expect from college and what they get. In: Miller, TE, Bender, BE, Schuh, JH eds. (2005) Promoting Reasonable Expectations: Aligning Student Views of the College Experience. Jossey-Bass, San Francisco, CA
  15. Cole, JS, Kennedy, M, Ben-Avie, M (2009) The role of precollege data in assessing and understanding student engagement in college. New Dir Instit Res 141: pp. 55-69 CrossRef
  16. Bauer, KW, Liang, Q (2003) The effect of personality and precollege characteristics on first-year activities and academic performance. J Coll Stud Dev 44: pp. 277-290 CrossRef
  17. Pascarella, ET, Pierson, CT, Wolniak, GC, Terenzini, PT (2004) First-generation college students: additional evidence on college experiences and outcomes. J Higher Educ 75: pp. 249-284 CrossRef
  18. Astin, AW (1993) What Matters in College? Four critical years revisited. Jossey-Bass, San Francisco, CA
  19. Zheng, J-L, Saunders, KP, Shelley, MC, Whalen, DF (2002) Predictors of academic success for freshmen residence hall students. J Coll Stud Dev 43: pp. 267-283
  20. Amelink, CT (2005) Predicting academic success among first-year, first generation students. PhD thesis. Polytechnic Institute and State University, College of Engineering, Virginia
  21. Cuddy, MM, Swanson, DB, Clauser, BE (2008) A multilevel analysis of examinee gender and USMLE Step 1 performance. Acad Med 83: pp. S58-S62 CrossRef
  22. Rauschenberger, MM, Sweeder, RD (2010) Gender performance differences in biochemistry. Biochem Mol Biol Edu 38: pp. 380-384 CrossRef
  23. Hudson, RD (2012) Is there a relationship between chemistry performance and question type, question content and gender?. Sci Educ Int 23: pp. 56-83
  24. Zhang, J (2013) Sex and Economics: The Paradox of Student Gender in China. Social Science Research Network.
  25. Sirin, SR (2005) Socioeconomic status and academic achievement: a meta-analytic review of research. Rev Educ Res 75: pp. 417-453 CrossRef
  26. White, KR (1982) The relation between socioeconomic status and academic achievement. Psychol Bull 91: pp. 461-481 CrossRef
  27. Carrell, SE, Fullerton, RL, West, JE (2009) Does your cohort matter? Measuring peer effects in college achievement. J Labor Econ 27: pp. 439-464 CrossRef
  28. Nelson Laird, TF, Shoup, R, Kuh, GD, Schwarz, MJ (2008) The effects of discipline on deep approaches to student learning and college outcomes. Res High Educ 49: pp. 469-494 CrossRef
  29. Kim, YK, Sax, LJ (2011) Are the effects of student鈥揻aculty interaction dependent on academic major? An examination using multilevel modeling. Res High Educ 52: pp. 589-615 CrossRef
  30. CIRP Freshman Survey: / Higher Education Research Institute home of Cooperative Institutional Research Program. http://www.heri.ucla.edu/cirpoverview.php
  31. Schwarz, MR, Wojtczak, A, Zhou, T (2004) Medical education in China鈥檚 leading medical schools. Med Teach 26: pp. 215-222 CrossRef
  32. Dolan, CV (1994) Factor analysis of variables with 2, 3, 5 and 7 response categories: a comparison of categorical variable estimators using simulated data. Br J Math Stat Psychol 47: pp. 309-326 CrossRef
  33. Dufour, JM, Taamouti, M (2007) Further results on projection-based inference in IV regressions with weak, collinear or missing instruments. J Econometrics 139: pp. 133-153 CrossRef
  34. Ethington, CA A hierarchical linear modeling approach to studying college effects. In: Smart, JC eds. (1997) Higher Education: Handbook of Theory and Research. Agathon, New York, pp. 165-194
  35. Raudenbush, SW, Liu, X (2000) Statistical power and optimal design for multisite randomized trials. Psychol Methods 5: pp. 199-213 CrossRef
  36. Cohen, J (1988) Statistical Power Analysis for the Behavioral Sciences. Erlbaum, Hillsdale, NJ
  37. Raudenbush, SW, Bryk, AS (2002) Hierarchical linear models: applications and data analysis methods. Sage Publications, Thousand Oaks, CA
  38. Lievens, F, Buyse, T, Sackett, PR (2005) Retest effects in operational selection settings: development and test of a framework. Pers Psychol 58: pp. 981-1007 CrossRef
  39. Price, J, Dunne, M (1990) Academic performance of students admitted after repeating entrance examinations to medical school. Med Educ 24: pp. 212-218 CrossRef
  40. Wiley, A, Koenig, JA (1996) The validity of the medical college admission test for predicting performance in the first two years of medical school. Acad Med 71: pp. S83-S85 CrossRef
  41. DeBerard, MS, Spielmans, GI, Julka, DC (2004) Predictors of academic achievement and retention among college freshmen: a longitudinal study. Coll Stud J 38: pp. 66-80
  42. Cuddy, MM, Swanson, DB, Dillon, GF, Holtman, MC, Clauser, BE (2006) A multilevel analysis of the relationships between selected examinee characteristics and United States Medical Licensing Examination Step 2 clinical knowledge performance: revisiting old findings and asking new questions. Acad Med 81: pp. S103-S107 CrossRef
  43. Udida, LA, Ukwayi, JK, Ogodo, FA (2012) Parental socioeconomic background as a determinant of student鈥檚 academic performance in selected public secondary schools in Calabar Municipal Local Government Area, Cross River State, Nigeria. J Educ Pract 3: pp. 129-135
  44. The pre-publication history for this paper can be accessed here: http://www.biomedcentral.com/1472-6920/14/87/prepub
  
• 刊物主题：Medical Education; Theory of Medicine/Bioethics;
• 出版者：BioMed Central
• ISSN：1472-6920

文摘

Background Although medical education has developed rapidly in the last decade, and the National College Entrance Examination (NCEE) is used as the "gold standard" for admission to medical college in mainland China, there is a lack of literature regarding the influence of NCEE score and other factors on the academic performance of medical students. This study aimed to examine potential predictors of first-year grade point average (GPA) for medical students. Methods This study included 1,285 students who matriculated at a first-tier medical university in mainland China in 2011. The precollege motivational attitudes for each matriculate were investigated via questionnaire. A hierarchical linear model was fitted to regress first-year GPA on a 100-point scale on NCEE score and other student-level and major-level characteristics. Results NCEE score was a significant predictor of both within-major and between-major variation of first-year GPA for medical students. Majors with higher mean NCEE scores had higher mean GPAs, and higher GPAs were observed among those individuals with higher NCEE scores after controlling for major-level characteristics. First-year GPA differed by certain individual socio-demographic variables. Female students had a 2.44-higher GPA on average than did male students. NCEE repeaters had a 1.55-lower GPA than non-repeaters. First-year GPA was associated negatively with parental income but positively with academic self-concept. Conclusions NCEE score is an important predictor of the first-year GPA of medical students, but it is not the sole determinant. Individual socio-demographic characteristics and major-level characteristics should be taken into account to understand better and improve the first-year GPA of medical students.