Saf Health Work.  2016 Sep;7(3):201-207. 10.1016/j.shaw.2016.04.003.

Development of a Tailored Analysis System for Korean Working Conditions Survey

Affiliations
  • 1Medical Informatics and health Technology (MIT), Department of Healthcare Management, College of Social Science, Gachon University, Seongnam, Republic of Korea. hjseo@gachon.ac.kr

Abstract

BACKGROUND
Korean Working Conditions Surveys (KWCS), referencing European Working Conditions Surveys, have been conducted three times in order to survey working condition and develop work-related policies. However, we found three limitations for managing the collected KWCS data: (1) there was no computerized system for managing data; (2) statistical KWCS data were provided by limited one-way communication; and (3) the concept of a one-time provision of information was pursued. We suggest a web-based public service system that enables ordinary people to make greater use of the KWCS data, which can be managed constantly in the future.
METHODS
After considering data characteristics, we designed a database, which was able to have the result of all pairwise combinations with two extracted data to construct an analysis system. Using the data of the social network for each user, the tailored analysis system was developed. This system was developed with three methods: clustering and classification for building a social network, and an infographic method for improving readability through a friendly user interface.
RESULTS
We developed a database including one input entity consisting of the sociodemographic characteristics and one output entity consisting of working condition characteristics, such as working pattern and work satisfaction. A web-based public service system to provide tailored contents was completed.
CONCLUSION
This study aimed to present a customized analysis system to use the KWCS data efficiently, provide a large amount of data in a form that can give users a better understanding, and lay the ground for helping researchers and policy makers understand the characteristics.

Keyword

algorithms; cluster analysis; database; data collection; information systems
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