Determining Cutting Points of the Maslach Burnout Inventory for Nurses to Measure Their Level of Burnout Online

Huan-Fang Lee; Hui-Ting Kuo; Cheng-Li Chang; Chia-Chen Hsu; Tsair-Wei Chien

doi:doi:10.11648/j.history.20170501.11

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Determining Cutting Points of the Maslach Burnout Inventory for Nurses to Measure Their Level of Burnout Online

Huan-Fang Lee, Hui-Ting Kuo, Cheng-Li Chang, Chia-Chen Hsu, Tsair-Wei Chien

Published in History Research (Volume 5, Issue 1)

Received: 19 December 2016 Accepted: 21 January 2017 Published: 24 February 2017

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Abstract

This study is to determine cutting points for the Chinese version of the MBI-HSS and to design an online assessment tool that instantly measures a nurse’s burnout level. We illustrate (1) the traditional way for determining the cutting points of a scale when the binary classification groups was still known, and (2) the norm-reference approach without groups of binary classifications was used to determine the cutting points on three subscales for the MBIO-HSS. An online MBIO-HSS assessment APP for smartphones was incorporated with the cutting points to instantly display the level of burnout for nurses. The cutoff points of the MBI-HSS were ≤ 21 and ≤ 32 for the Emotional subscale, ≤ 23 and ≤ 30 for the Reduced Personal Accomplishment subscale, ≤ 6 and ≤ 12 for the Depersonalization subscale, and ≤ 15 and ≤ 17 (i.e., low, moderate, and high level) for the overall scores. An available-for-download online MBI-HSS APP for nurses was developed and demonstrated.

Published in	History Research (Volume 5, Issue 1)
DOI	10.11648/j.history.20170501.11
Page(s)	1-8
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Nurse Burnout, MBI-HSS Chinese Version, Cutting Points, Prevalence

References

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APA Style

Huan-Fang Lee, Hui-Ting Kuo, Cheng-Li Chang, Chia-Chen Hsu, Tsair-Wei Chien. (2017). Determining Cutting Points of the Maslach Burnout Inventory for Nurses to Measure Their Level of Burnout Online. History Research, 5(1), 1-8. https://doi.org/10.11648/j.history.20170501.11

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ACS Style

Huan-Fang Lee; Hui-Ting Kuo; Cheng-Li Chang; Chia-Chen Hsu; Tsair-Wei Chien. Determining Cutting Points of the Maslach Burnout Inventory for Nurses to Measure Their Level of Burnout Online. Hist. Res. 2017, 5(1), 1-8. doi: 10.11648/j.history.20170501.11

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AMA Style

Huan-Fang Lee, Hui-Ting Kuo, Cheng-Li Chang, Chia-Chen Hsu, Tsair-Wei Chien. Determining Cutting Points of the Maslach Burnout Inventory for Nurses to Measure Their Level of Burnout Online. Hist Res. 2017;5(1):1-8. doi: 10.11648/j.history.20170501.11

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@article{10.11648/j.history.20170501.11,
  author = {Huan-Fang Lee and Hui-Ting Kuo and Cheng-Li Chang and Chia-Chen Hsu and Tsair-Wei Chien},
  title = {Determining Cutting Points of the Maslach Burnout Inventory for Nurses to Measure Their Level of Burnout Online},
  journal = {History Research},
  volume = {5},
  number = {1},
  pages = {1-8},
  doi = {10.11648/j.history.20170501.11},
  url = {https://doi.org/10.11648/j.history.20170501.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.history.20170501.11},
  abstract = {This study is to determine cutting points for the Chinese version of the MBI-HSS and to design an online assessment tool that instantly measures a nurse’s burnout level. We illustrate (1) the traditional way for determining the cutting points of a scale when the binary classification groups was still known, and (2) the norm-reference approach without groups of binary classifications was used to determine the cutting points on three subscales for the MBIO-HSS. An online MBIO-HSS assessment APP for smartphones was incorporated with the cutting points to instantly display the level of burnout for nurses. The cutoff points of the MBI-HSS were ≤ 21 and ≤ 32 for the Emotional subscale, ≤ 23 and ≤ 30 for the Reduced Personal Accomplishment subscale, ≤ 6 and ≤ 12 for the Depersonalization subscale, and ≤ 15 and ≤ 17 (i.e., low, moderate, and high level) for the overall scores. An available-for-download online MBI-HSS APP for nurses was developed and demonstrated.},
 year = {2017}
}

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TY  - JOUR
T1  - Determining Cutting Points of the Maslach Burnout Inventory for Nurses to Measure Their Level of Burnout Online
AU  - Huan-Fang Lee
AU  - Hui-Ting Kuo
AU  - Cheng-Li Chang
AU  - Chia-Chen Hsu
AU  - Tsair-Wei Chien
Y1  - 2017/02/24
PY  - 2017
N1  - https://doi.org/10.11648/j.history.20170501.11
DO  - 10.11648/j.history.20170501.11
T2  - History Research
JF  - History Research
JO  - History Research
SP  - 1
EP  - 8
PB  - Science Publishing Group
SN  - 2376-6719
UR  - https://doi.org/10.11648/j.history.20170501.11
AB  - This study is to determine cutting points for the Chinese version of the MBI-HSS and to design an online assessment tool that instantly measures a nurse’s burnout level. We illustrate (1) the traditional way for determining the cutting points of a scale when the binary classification groups was still known, and (2) the norm-reference approach without groups of binary classifications was used to determine the cutting points on three subscales for the MBIO-HSS. An online MBIO-HSS assessment APP for smartphones was incorporated with the cutting points to instantly display the level of burnout for nurses. The cutoff points of the MBI-HSS were ≤ 21 and ≤ 32 for the Emotional subscale, ≤ 23 and ≤ 30 for the Reduced Personal Accomplishment subscale, ≤ 6 and ≤ 12 for the Depersonalization subscale, and ≤ 15 and ≤ 17 (i.e., low, moderate, and high level) for the overall scores. An available-for-download online MBI-HSS APP for nurses was developed and demonstrated.
VL  - 5
IS  - 1
ER  -

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Author Information

Huan-Fang Lee

Departent of Nursing, National Cheng Kung University Hospital, Tainan, Taiwan
Hui-Ting Kuo

Departent of Nursing, Chi-Mei Medical Center, Tainan, Taiwan
Cheng-Li Chang

Departent of Nursing, Chi-Mei Medical Center, Tainan, Taiwan
Chia-Chen Hsu

Departent of Nursing, Chi-Mei Medical Center, Tainan, Taiwan
Tsair-Wei Chien

Department of Medical Research, Chi-Mei Medical Center, Tainan, Taiwan

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Plain Text BibTeX RIS

APA Style

Huan-Fang Lee, Hui-Ting Kuo, Cheng-Li Chang, Chia-Chen Hsu, Tsair-Wei Chien. (2017). Determining Cutting Points of the Maslach Burnout Inventory for Nurses to Measure Their Level of Burnout Online. History Research, 5(1), 1-8. https://doi.org/10.11648/j.history.20170501.11

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ACS Style

Huan-Fang Lee; Hui-Ting Kuo; Cheng-Li Chang; Chia-Chen Hsu; Tsair-Wei Chien. Determining Cutting Points of the Maslach Burnout Inventory for Nurses to Measure Their Level of Burnout Online. Hist. Res. 2017, 5(1), 1-8. doi: 10.11648/j.history.20170501.11

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AMA Style

Huan-Fang Lee, Hui-Ting Kuo, Cheng-Li Chang, Chia-Chen Hsu, Tsair-Wei Chien. Determining Cutting Points of the Maslach Burnout Inventory for Nurses to Measure Their Level of Burnout Online. Hist Res. 2017;5(1):1-8. doi: 10.11648/j.history.20170501.11

Copy | Download

@article{10.11648/j.history.20170501.11,
  author = {Huan-Fang Lee and Hui-Ting Kuo and Cheng-Li Chang and Chia-Chen Hsu and Tsair-Wei Chien},
  title = {Determining Cutting Points of the Maslach Burnout Inventory for Nurses to Measure Their Level of Burnout Online},
  journal = {History Research},
  volume = {5},
  number = {1},
  pages = {1-8},
  doi = {10.11648/j.history.20170501.11},
  url = {https://doi.org/10.11648/j.history.20170501.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.history.20170501.11},
  abstract = {This study is to determine cutting points for the Chinese version of the MBI-HSS and to design an online assessment tool that instantly measures a nurse’s burnout level. We illustrate (1) the traditional way for determining the cutting points of a scale when the binary classification groups was still known, and (2) the norm-reference approach without groups of binary classifications was used to determine the cutting points on three subscales for the MBIO-HSS. An online MBIO-HSS assessment APP for smartphones was incorporated with the cutting points to instantly display the level of burnout for nurses. The cutoff points of the MBI-HSS were ≤ 21 and ≤ 32 for the Emotional subscale, ≤ 23 and ≤ 30 for the Reduced Personal Accomplishment subscale, ≤ 6 and ≤ 12 for the Depersonalization subscale, and ≤ 15 and ≤ 17 (i.e., low, moderate, and high level) for the overall scores. An available-for-download online MBI-HSS APP for nurses was developed and demonstrated.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Determining Cutting Points of the Maslach Burnout Inventory for Nurses to Measure Their Level of Burnout Online
AU  - Huan-Fang Lee
AU  - Hui-Ting Kuo
AU  - Cheng-Li Chang
AU  - Chia-Chen Hsu
AU  - Tsair-Wei Chien
Y1  - 2017/02/24
PY  - 2017
N1  - https://doi.org/10.11648/j.history.20170501.11
DO  - 10.11648/j.history.20170501.11
T2  - History Research
JF  - History Research
JO  - History Research
SP  - 1
EP  - 8
PB  - Science Publishing Group
SN  - 2376-6719
UR  - https://doi.org/10.11648/j.history.20170501.11
AB  - This study is to determine cutting points for the Chinese version of the MBI-HSS and to design an online assessment tool that instantly measures a nurse’s burnout level. We illustrate (1) the traditional way for determining the cutting points of a scale when the binary classification groups was still known, and (2) the norm-reference approach without groups of binary classifications was used to determine the cutting points on three subscales for the MBIO-HSS. An online MBIO-HSS assessment APP for smartphones was incorporated with the cutting points to instantly display the level of burnout for nurses. The cutoff points of the MBI-HSS were ≤ 21 and ≤ 32 for the Emotional subscale, ≤ 23 and ≤ 30 for the Reduced Personal Accomplishment subscale, ≤ 6 and ≤ 12 for the Depersonalization subscale, and ≤ 15 and ≤ 17 (i.e., low, moderate, and high level) for the overall scores. An available-for-download online MBI-HSS APP for nurses was developed and demonstrated.
VL  - 5
IS  - 1
ER  -

Copy | Download