Abstract
This study uses kernel k-means cluster analysis to identify medical staffs with high burnout. The data collected in October to November 2014 are from the emotional exhaustion dimension of the Chinese version of Safety Attitudes Questionnaire in a regional teaching hospital in Taiwan. The number of effective questionnaires including the entire staffs such as physicians, nurses, technicians, pharmacists, medical administrators, and respiratory therapists is 680. The results show that 8 clusters are generated by kernel k-means method. Employees in clusters 1, 4, and 5 are relatively in good conditions, whereas employees in clusters 2, 3, 6, 7, and 8 need to be closely monitored from time to time because they have relatively higher degree of burnout. When employees with higher degree of burnout are identified, the hospital management can take actions to improve the resilience, reduce the potential medical errors, and, eventually, enhance the patient safety. This study also suggests that the hospital management needs to keep track of medical staffs’ fatigue conditions and provide timely assistance for burnout recovery through employee assistance programs, mindfulness-based stress reduction programs, positivity currency buildup, and forming appreciative inquiry groups.
| Original language | English |
|---|---|
| Journal | Inquiry (United States) |
| Volume | 53 |
| DOIs | |
| Publication status | Published - 2016 Jan 1 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Health Policy
Cite this
}
A new approach to identify high burnout medical staffs by kernel K-means cluster analysis in a regional teaching hospital in Taiwan. / Lee, Yii Ching; Huang, Shian Chang; Huang, Chih Hsuan; Wu, Hsin Hung.
In: Inquiry (United States), Vol. 53, 01.01.2016.Research output: Contribution to journal › Article
TY - JOUR
T1 - A new approach to identify high burnout medical staffs by kernel K-means cluster analysis in a regional teaching hospital in Taiwan
AU - Lee, Yii Ching
AU - Huang, Shian Chang
AU - Huang, Chih Hsuan
AU - Wu, Hsin Hung
PY - 2016/1/1
Y1 - 2016/1/1
N2 - This study uses kernel k-means cluster analysis to identify medical staffs with high burnout. The data collected in October to November 2014 are from the emotional exhaustion dimension of the Chinese version of Safety Attitudes Questionnaire in a regional teaching hospital in Taiwan. The number of effective questionnaires including the entire staffs such as physicians, nurses, technicians, pharmacists, medical administrators, and respiratory therapists is 680. The results show that 8 clusters are generated by kernel k-means method. Employees in clusters 1, 4, and 5 are relatively in good conditions, whereas employees in clusters 2, 3, 6, 7, and 8 need to be closely monitored from time to time because they have relatively higher degree of burnout. When employees with higher degree of burnout are identified, the hospital management can take actions to improve the resilience, reduce the potential medical errors, and, eventually, enhance the patient safety. This study also suggests that the hospital management needs to keep track of medical staffs’ fatigue conditions and provide timely assistance for burnout recovery through employee assistance programs, mindfulness-based stress reduction programs, positivity currency buildup, and forming appreciative inquiry groups.
AB - This study uses kernel k-means cluster analysis to identify medical staffs with high burnout. The data collected in October to November 2014 are from the emotional exhaustion dimension of the Chinese version of Safety Attitudes Questionnaire in a regional teaching hospital in Taiwan. The number of effective questionnaires including the entire staffs such as physicians, nurses, technicians, pharmacists, medical administrators, and respiratory therapists is 680. The results show that 8 clusters are generated by kernel k-means method. Employees in clusters 1, 4, and 5 are relatively in good conditions, whereas employees in clusters 2, 3, 6, 7, and 8 need to be closely monitored from time to time because they have relatively higher degree of burnout. When employees with higher degree of burnout are identified, the hospital management can take actions to improve the resilience, reduce the potential medical errors, and, eventually, enhance the patient safety. This study also suggests that the hospital management needs to keep track of medical staffs’ fatigue conditions and provide timely assistance for burnout recovery through employee assistance programs, mindfulness-based stress reduction programs, positivity currency buildup, and forming appreciative inquiry groups.
UR - http://www.scopus.com/inward/record.url?scp=85016107017&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85016107017&partnerID=8YFLogxK
U2 - 10.1177/0046958016679306
DO - 10.1177/0046958016679306
M3 - Article
C2 - 27895218
AN - SCOPUS:85016107017
VL - 53
JO - Inquiry
JF - Inquiry
SN - 0046-9580
ER -