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

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.

DOI 10.11648/j.history.20170501.11
Published in History Research (Volume 5, Issue 1, January 2017)
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), 2024. Published by Science Publishing Group

Keywords

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

References
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Author Information
  • Departent of Nursing, National Cheng Kung University Hospital, Tainan, Taiwan; Nursing Department, Chung Hwa University of Medical Technology, Tainan, Taiwan; Nursing Department, National Cheng Kung University, Tainan, Taiwan

  • Departent of Nursing, Chi-Mei Medical Center, Tainan, Taiwan

  • Departent of Nursing, Chi-Mei Medical Center, Tainan, Taiwan

  • Departent of Nursing, Chi-Mei Medical Center, Tainan, Taiwan

  • Department of Medical Research, Chi-Mei Medical Center, Tainan, Taiwan; Department of Hospital and Health Care Administration, Chia-Nan University of Pharmacy and Science, Tainan, Taiwan

Cite This Article
  • 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://download.sciencepg.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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    AU  - Hui-Ting Kuo
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    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.
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