인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Evaluation of clinical performance is essential in all medical school programmes. Students undergo multiple clinical placements in different disciplines and settings, and typically must pass an end-of-placement supervisor sign-off evaluation to progress. However, the validity of this sign-off model remains unclear. This study aims to assess the extent to which this assessment method predicts performance in summative medical school examinations. We compared summative knowledge and clinical skills examination scores with end-of-placement supervisor sign-off ratings of ‘knowledge’, ‘clinical skills’ and ‘practical skills’ for medical undergraduate students, across three clinical placements at Imperial College London, UK (n = 355). Statistical analysis for predictive validity was performed through Ordinary Least Squares regression. End-of-placement supervisor ratings in hospital did not significantly predict student performance in summative knowledge tests or clinical skills assessment. ‘Knowledge’ and ‘practical skills’ ratings lacked predictive validity across all supervisors. Statistically significant predictive validity was evident for GP supervisor ratings of ‘clinical skills’ and examination scores, but the effect size was educationally insignificant (p = 0.01, r2 = 0.02). End-of-placement supervisor ratings did not demonstrate educationally significant predictive validity towards end-of-year examinations. Multi-source feedback, embedded in-placement assessment, and additional formalised supervision time in supervisors’ work schedules could be beneficial to improve the educational value for the student and the clinical placement sign-off process. Different sign-off requirements could be considered for GP and hospital settings, with tailoring of constructs to suit the clinical environment.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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