Cited 0 time in
한 종합병원 간호사들의 노동시간 유형에 대한 평가
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | 윤서현 | - |
| dc.contributor.author | 박주현 | - |
| dc.contributor.author | 기도형 | - |
| dc.contributor.author | 고태경 | - |
| dc.contributor.author | 강충원 | - |
| dc.contributor.author | 고동희 | - |
| dc.contributor.author | 김현주 | - |
| dc.date.accessioned | 2023-04-28T06:40:51Z | - |
| dc.date.available | 2023-04-28T06:40:51Z | - |
| dc.date.issued | 2018-12 | - |
| dc.identifier.issn | 1229-1684 | - |
| dc.identifier.issn | 2093-8462 | - |
| dc.identifier.uri | https://scholarworks.dongguk.edu/handle/sw.dongguk/8724 | - |
| dc.description.abstract | Objective: The aim of this study is to investigate the working time patterns including length, shift type, shift intensity, and special aspects of working hours for hospital nurses. Background: Working time patterns, such as long working hours and shift work with night shift, are important public health issue. However, most of the previous studies were based on the self-report data, and only a few studies have attempted to comprehensively estimate the working time pattern by considering various aspects of work. Method: The work schedule of a total of 1,254 nurses who worked at a general hospital in 2017, were analyzed using the electronic data recorded in the computerized system. We examined 15 potentially health-relevant working time factors for nurses, categorized under four main domain heading of: (1) Time of the day, (2) length of working hours, (3) shift intensity, and (4) special aspects of working hours. Results: Among a total of 1,254 nurses, most nurses (82.4%) were work with 3-shift type. 2-shfit nurses had 32.3±24.3% of the experience of more than 40 hours a week, followed by 3-shift nurses (22.5±18.7), and daytime nurses (14.7±11.6). But the experience of three consecutive night shift spells were higher among 3-shift (4.1± 6.2 times) than 2-shift nurses (0.3±0.5), and the experience of working on weekend or legal holiday was higher in 3-shift nurses (42.9±23.2%) than 2-shift (36.6±17.9) and daytime (12.7±11.3) nurses, while the number of having more than three consecutive free days was lowest in 3-shift nurses (8.9±6.0 times) compared with 2-shift (21.3± 12.6) and daytime nurses (10.3±5.9). Conclusion: 3-shift nurses had less long working hours, but high shift intensity and disadvantageous social working conditions than 2-shfit nurses, and 2-shift nurses worked with night shift and had long working hours, high shift intensity, but advantageous in social aspects than daytime nurses. Application: The results of this study might help to improve understanding of working time pattern in hospital nurses, and lay an important foundation for further research on health of nurses in Korea. | - |
| dc.format.extent | 11 | - |
| dc.language | 한국어 | - |
| dc.language.iso | KOR | - |
| dc.publisher | 대한인간공학회 | - |
| dc.title | 한 종합병원 간호사들의 노동시간 유형에 대한 평가 | - |
| dc.title.alternative | Assessment of Working Time Pattern for Hospital Nurses | - |
| dc.type | Article | - |
| dc.publisher.location | 대한민국 | - |
| dc.identifier.doi | 10.5143/JESK.2018.37.6.759 | - |
| dc.identifier.bibliographicCitation | 대한인간공학회지, v.37, no.6, pp 759 - 769 | - |
| dc.citation.title | 대한인간공학회지 | - |
| dc.citation.volume | 37 | - |
| dc.citation.number | 6 | - |
| dc.citation.startPage | 759 | - |
| dc.citation.endPage | 769 | - |
| dc.identifier.kciid | ART002426462 | - |
| dc.description.isOpenAccess | N | - |
| dc.description.journalRegisteredClass | kci | - |
| dc.subject.keywordAuthor | Working hours | - |
| dc.subject.keywordAuthor | Shift type | - |
| dc.subject.keywordAuthor | Shift intensity | - |
| dc.subject.keywordAuthor | Shift work | - |
| dc.subject.keywordAuthor | Night work | - |
| dc.subject.keywordAuthor | Nurse | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
30, Pildong-ro 1-gil, Jung-gu, Seoul, 04620, Republic of Korea+82-2-2260-3114
Copyright(c) 2023 DONGGUK UNIVERSITY. ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.
