인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
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
The purpose of this study was to review the research on treating depression in Traditional Chinese Medicine(TCM) over the last 5 years to set the foundation for further studies. We searched for and analyzed articles about depression in CNKI(China National Knowledge Infrastructure) from January 2010 to December 2014. The results were as follows : The most frequently used diagnostic criteria was CCMD-3(The Chinese Classification of Mental Disorders-3), and the most used evaluation criteria was HAMD(Hamilton Depression Rating Scale). Prescription of decoction of medical herbs was most frequently used as a therapeutic method. Acupuncture, traditional Chinese psychotherapy, and music therapy were also used for some studies. The most frequent TCM Syndrome Differentiation Type was stagnation of liver-QI. For decoction of Chinese herbs, Soyo-san(Xiaoyao-san) and Sihosogan-san(Chaihushugan-san) were most often prescribed, and Bupleuri Radix, Paeoniae Radix Alba, Curcumae Radix, Poria cocos wolf, Angelicae Gigantis Radix, Atractylodis Rhizoma Alba were mainly used for medical herbs. BaekHoi(GV20) and Yindang(Ex-HN3) were often used as acupuncture sites. Post-Stroke Depression was the most common case of intercurrent or secondary depression. According to the Jadad Quality Assessment Scale, the quality of the reports was not high as most of the reports had a score of 3 or below. Most systematic reviews on depression were conducted by Chinese researchers. The problem with Clinical research on depression, according to those reviews, was that there were no standardized criteria for the diagnosis and treatment and the trials were usually not randomized nor controlled. We found out there are various clinical methods for treating depression in TCM, and hope that this research could provide the preliminary data for designing and conducting clinical trials for depression.
본문·목차
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.