Association rule mining to identify critical demographic variables influencing the degree of burnout in a regional teaching hospital

Yii Ching Lee, Chih Hsuan Huang, Yi Chen Lin, Hsin Hung Wu

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

This study uses apriori algorithm of IBM SPSS Modeler 14.1 on nine questions of emotional exhaustion dimension along with ten demographic variables from a regional teaching hospital in Taiwan in 2014 to identify critical demographic variables that influence the degree of burnout. By setting up the support of 25%, confidence of 80%, and lift of 1.5, twenty nine rules are found. To further refine the rules by their similarities, seven major combinations are summarized. The major characteristics are depicted below. Female medical staffs with college/university education who are not in charge of supervisor/manager with very often direct patient contacts feel much stressful to work with people directly and all day. That is, they have relatively higher degree of burnout. In summary, four demographic variables are found to be the major variables that influence emotional exhaustion, including gender, supervisor/manager, education, and direct patient contact.

Original languageEnglish
Pages (from-to)497-502
Number of pages6
JournalTEM Journal
Volume6
Issue number3
DOIs
Publication statusPublished - 2017 Aug 1

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Information Systems
  • Education
  • Strategy and Management
  • Information Systems and Management
  • Management of Technology and Innovation

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