Chinese Journal of Medical Education ›› 2023, Vol. 43 ›› Issue (3): 161-164.DOI: 10.3760/cma.j.cn115259-20220702-00849

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Current situation analysis and model optimization of general category enrollment, general training and major diversion for medical technology specialty

Cheng Ran, Jiang Lin, Liu Chang, Cao Lu, Zhou Disu, Fu Li, Zhang Xuejun, Tian Derun, Shen Yanna   

  1. School of Medical Technology, Tianjin Medical University, Tianjin 300203, China
  • Received:2022-07-02 Online:2023-03-01 Published:2023-02-24
  • Contact: Shen Yanna, Email: shenyanna@sina.com
  • Supported by:
    Social Science Major Projects of Tianjin Education Commission(2019JWZD52)

Abstract: Objective To analyze the existing problems of the general category enrollment, general training and major diversion for medical technology specialty in order to provide the reference for optimizing and improving the current talent cultivation mode and promote the innovative development of medical technology specialty. Methods In September 2021, a questionnaire survey was used to investigate among 431 students who completed major diversion from grades 2019 and 2020. General linear model was conducted on the scores of compulsory courses in the first academic year and the results of major diversion of grades 2019 and 2020 students to evaluate students' choices of majors. Results The results of 381 valid questionnaires showed that 249(65.4%) students thought that general training could improve their learning enthusiasm and provide help for major diversion, and 217(57.0%) students lacked understanding of general category enrollment policies; 285(74.8%) students were satisfied with the curriculum of general training; 105(27.6%) students felt anxious and depressed after major diversion, and thought that not being assigned to an ideal major would affect their learning enthusiasm. In grade 2019, the average weighted scores of students majoring in medical laboratory technology and medical imaging technology in the first academic year were (82.56±2.94) and (79.15±4.17) respectively, and the scores of students in grade 2020 were (83.30±2.61) and (79.29±2.93) respectively. There are statistical differences between the majors in the two grades (all P<0.001). The proportion of the first choice for the medical laboratory technology specialty of grades 2019 and 2020 are 55.3% (119/215) and 40.7% (88/216) respectively, and the proportion of other majors is less than 30.0%. The trend of major differentiation was found by statistical analyzing the scores and the applications of the students. Conclusions It is necessary to strengthen the publicity of general category enrollment and general training, provide guidance for major selection, focus on the psychological counseling for students after major diversion and explore a new evaluation mechanism for major diversion.

Key words: Education, professional, General category enrollment, General training, Major diversion, Medical technology, Optimization and improvement

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