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
This study aimed at demonstrating the feasibility, utility and relevance of the Bayesian Latent Class Modelling (BLCM), not assuming a gold standard, when assessing the diagnostic accuracy of the first hetero-assessment test for early detection of occupational burnout (EDTB) by healthcare professionals and the OLdenburg Burnout Inventory (OLBI). We used available data from OLBI and EDTB completed for 100 Belgian and 42 Swiss patients before and after medical consultations. We applied the Hui-Walter framework for two tests and two populations and ran models with minimally informative priors, with and without conditional dependency between diagnostic sensitivities and specificities. We further performed sensitivity analysis by replacing one of the minimally informative priors with the distribution beta1,2 at each time for all priors. We also performed the sensitivity analysis using literature-based informative priors for OLBI. Using the BLCM without conditional dependency, the diagnostic sensitivity and specificity of the EDTB were 0.91 (0.77-1.00) and 0.82 (0.59-1.00), respectively. The sensitivity analysis did not yield any significant changes in these results. The EDTB’s sensitivity and specificity obtained by a BLCM approach are better compared to the previous studies when EDTB was evaluated against OLBI, considered as a gold standard. These findings show the utility and relevance of BLCM in the absence of a gold standard.
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.
최근 본 자료 전체보기
