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
Objectives: The objective of this study is to gain a qualitative insight into provincial, metropolitan and local infectious disease emergency responders’ (IDERs’) response experiences in the 2015 Middle East Respiratory Syndrome (MERS) outbreak in South Korea. Methods: The focus group interview (FGI) method was employed to collect qualitative data from 4 different focus groups of 25 representative registered IDERs over the period from Dec. 2016 to Jan. 2017. The stratified sampling method was used to select them among different ages, genders, job positions, job series and job groups of 259 IDERs. All interviews were recorded and transcribed in verbatim, and analyzed according to the qualitative thematic analysis method using NViVO software. Results: The process of being reluctant to be positioned as an IDER or at an IDER-related unit or team was based in four subcategories. The overriding theme was experiencing differences between the regulatory system called `rotation job`, which is a characteristic of civil servants` organizations that have difficulty in maintaining professionalism and persistence of work, and the actual reality of the `new respiratory infectious disease` crisis spread. In the case of massive cases caused by the spread of infectious diseases, the imbalance of powers and responsibilities of local government infectious disease officials, fear of infectious diseases and fear framing are problems. Conclusions: The insights of the study indicate the need to identify the following priorities for gradual, practicable policy reforms that would require the involvement of the central government. The regulatory limitations of current system with a shortage of human and material resources pushed the participants to experience discrepancies between the required regulation and the reality. Additional research could contribute more exemplars to support changes.
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.
최근 본 자료 전체보기
UCI(KEPA) : I410-ECN-0101-2018-517-003585590
