Burnout among Staff Nurses in Taiwan: Instrument Validation and Predictors Identification

  • 李 歡芳

Student thesis: Doctoral Thesis

Abstract

Abstract Background Burnout in the nursing profession is a globally important problem because it affects individual organizational and patient outcomes Burnout is a metaphor to describe people experiencing a state of emotional exhaustion similar to the extinguishing of a candle Three components emotional exhaustion (EE) personal accomplishment (PA) and depersonalization (DP) are included in burnout concepts Nurse burnout has been investigated since the 1970s in western countries The Maslach Burnout Inventory-Human Service Survey (MBI-HSS) has been widely used to measure burnout However the factor structure of the MBI-HSS differs in different cultures countries and healthcare provider systems; thus researchers have suggested that larger sample sizes expanding the data collection area and different working environments are needed to investigate burnout differences across cultures countries and healthcare Only two studies have investigated nurse burnout in Taiwan; both reported small sample sizes and focused on nurses working in specific specialties However neither identified suitable measurement instruments or predictors of burnout among Taiwanese nurses Aim The aims of this study were to (1) validate a tool suitable for measuring nurse burnout in Taiwan; (2) determine the current burnout levels and cut points of the MBI-HSS Chinese version; (3) investigate prevalence rates of nurse burnout in Taiwan; and (4) explore the predictors of nurse burnout in Taiwan Methods Research design: Cross-sectional Sample: Nurses from 483 hospitals accredited by the Taiwan Joint Commission on Hospital Accreditation and chosen using proportional stratified random sampling within a geographic area The exclusion criteria were: working in a hospital with (1) fewer than 100 beds or (2) no surgical or medical units Instrument: Two categories of data—demographic information and self-reported statements of the nurses’ perceptions of their work environment job satisfaction work engagement and mental health—are included in the current study Five different inventories were adapted to explore the nurses’ perceptions Data analysis: In addition to descriptive statistics exploratory factor analysis and confirmatory factor analysis were used to develop the instrument The cut points of the burnout-measurement instrument for each level low moderate and high were determined using level estimations and the K-mean grouping method Finally the predictors of burnout were investigated using hierarchical regression Results Factor analysis showed an adequate fit between the three-factor 20-item model in the MBI-HSS Chinese version (MBI-HSS-CV) The new structure model contained eight items for emotional exhaustion four items for depersonalization and eight items for personal accomplishment and the final result of variance was 57% The validity indexes of the factor structure were x2/df = 3 59 GFI = 0 92 AGFI = 0 90 and RMSEA = 0 05 There were three levels of burnout The level of burnout was low if scores of emotional exhaustion was less than 21 depersonalization was less than 6 and personal accomplishment was greater than 25; it is moderate if scores of emotional exhaustion ranged from 22 to 32 depersonalization 7 to 13 and personal accomplishment 16 to 24; and it was high if scores of emotional exhaustion was greater than 33 depersonalization was greater than 14 and personal accomplishment was less than 15 Eighty percent of the surveyed nurses reported more than moderate emotional exhaustion 66% reported more than moderate depersonalization and 75% reported more than moderate low personal accomplishment Related factors for Taiwanese nurse burnout were age mental health job satisfaction work engagement and work environment The most significant predictors were mental health and work engagement The explained variances for each component were 35% 18% and 39% for emotional exhaustion personal depersonalization and accomplishment respectively
Date of Award2015 Jan 27
Original languageEnglish
SupervisorMiao-Fen Yen (Supervisor)

Cite this

Burnout among Staff Nurses in Taiwan: Instrument Validation and Predictors Identification
歡芳, 李. (Author). 2015 Jan 27

Student thesis: Doctoral Thesis