||Burnout among Staff Nurses in Taiwan: Instrument Validation and Predictors Identification
||Institute of Allied Health Sciences
MBI-HSS Chinese version
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.
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.
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.
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.
TABLE OF CONTENTS
LIST OF TABLES......x
LIST OF FIGURES......xi
CHAPTER 1 INTRODUCTION....1
CHAPTER 2 LITERATURE REVIEW.....4
2.1 Research on the history of burnout..4
2.2 Definition of burnout.....6
2.3 Nurse burnout in various countries..9
2.4 Burnout-related factors....11
2.5 Nurse outcomes related to burnout..24
2.6 Measuring burnout in nurses: Maslach Burnout Inventory-Human Services Survey....26
2.7 Research framework and hypothesis..29
CHAPTER 3 METHODS......32
3.1 Research design and data sources...32
3.2 Data collected in this study...35
3.3 Measurement instruments....35
3.4 Data analysis......39
CHAPTER 4 RESULTS......41
4.1 Factorial structure of the MBI-HSS Chinese version.41
4.2 Levels and cut points of the MBI-HSS Chinese version.48
4.3 The prevalence rate of burnout for nurses in Taiwan.50
4.4 The predictors of burnout for nurses in Taiwan.50
CHAPTER 5 DISCUSSION.....55
5.1 The factor structure of the Maslach Burnout Inventory Chinese version......55
5.2 Levels and cut points of the Maslach Burnout Inventory Chinese version......60
5.3 The prevalence rate of burnout for nurses in Taiwan.62
5.4 The predictors of burnout for nurses in Taiwan.64
CHAPTER 6 LIMITATIONS AND FUTURE RESEARCHES....…..66
CHAPTER 7 CONCLUSIONS....67
CHAPTER 8 REFERENCES.....68
Table 1. Demographic characteristics of the participants.34
Table 2. Critical ratio, correlations, item mean (SD), and skew of Maslach
Burnout Inventory-Human Services Survey (MBI-HSS)..44
Table 3. The communality and factor loadings on structure matrix after re-structure MI-HSS...45
Table 4. K-mean grouping method of MBI-HSS Chinese version for emotional exhaustion, personal accomplishment, and
Table 5. Level and cut points of MBI-HSS Chinese version.49
Table 6. Prevalence rate of burnout among Taiwanese nurses.......50
Table 7. Burnout subscale scores by demographic variables.52
Table 8. Correlation among continuous variables..53
Table 9 Hierarchical regressions to predict burnout and subscale scores......54
Figure 1. Research framework...31
Figure 2. Modification structure of MBI-HSS..47
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