Abstract
Objective: The concept of perfectionism comprises high standards of performance as needed in medicine, but also concerns about making mistakes and dealing with social reactions about not being perfect. Perfectionism is associated with motivation and deep learning strategies but high expression of perfectionism has been found to be associated with symptoms of stress and anxiety in students. We aim to gain insights into the longitudinal development of different dimensions of perfectionism in medical students with respect to their way of medical school admission.Methods: At the Medical Faculty of Hamburg University, 167 undergraduate medical students completed validated questionnaires (MPS-H and MPS-F) of different dimensions of perfectionism and sociodemographic data including medical school admission procedures, personality traits (BSI-10 and GSE), and symptoms of depression and anxiety (PHQ-9 and GAD-7) at the start of their first year and at half term of their second year. Results: On average, after controlling for baseline and age, a significant decrease (p≤0.05) in Self-Oriented Perfectionism was found during the first two years in students who were admitted after a waiting period (M: -12.57; 95% CI: [-21.94 – -3.35]), by other ways of medical school entrance (M: -6.36; 95% CI: [-12.71 – -0.02]), by multiple mini-interviews (HAM-Int) (M: -5.52; 95% CI: [-9.90 – -1.14]), and by a natural science test (HAM-Nat) (M: -3.41; 95% CI: [-6.71 – -0.11]. Waiting period students also showed a significant longitudinal decline in the scale Personal Standards (M: -4.62; 95% CI: [-8.04 – -1.21].Conclusions: Since medical students from all admission groups except from the high school degree group showed a significant longitudinal decrease in Self-Oriented Perfectionism, high levels of aspects of perfectionism associated with intrinsic motivation or deep learning strategies could be included medical school admission processes. Additionally, particular attention needs to be paid not to induce a loss of intrinsic motivation or deep learning strategies during undergraduate medical education.
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