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Correspondence to: Huong Thi Thu Nguyen, RN, MPH, Division of Health Sciences, Graduate School of Medical Sciences, Kanazawa University, 5-11-80 Kodatsuno, Ishikawa 920094, Japan.
This study aimed to create a Vietnamese version of both the Maslach Burnout Inventory-General Survey (MBI-GS) and Areas of Worklife Scale (AWS) to assess the burnout state of Vietnamese clinical nurses and to develop a causal model of burnout of clinical nurses.
Methods
We conducted a descriptive design using a cross-sectional survey. The questionnaire was hand divided directly by nursing departments to 500 clinical nurses in three hospitals. Vietnamese MBI-GS and AWS were then examined for reliability and validity. We used the revised exhaustion +1 burnout classification to access burnout state. We performed path analysis to develop a Vietnamese causal model based on the original model by Leiter and Maslach's theory.
Results
We found that both scales were reliable and valid for assessing burnout. Among nurse participants, the percentage of severe burnout was 0.7% and burnout was 15.8%, and 17.2% of nurses were exhausted. The best predictor of burnout was “on-duty work schedule” that clinical nurses have to work for 24 hours. In the causal model, we also found similarity and difference pathways in comparison with the original model.
Conclusion
Vietnamese MBI-GS and AWS were applicable to research on occupational stress. Nearly one-fifth of Vietnamese clinical nurses were working in burnout state. The causal model suggested a range of factors resulting in burnout, and it is necessary to consider the specific solution to prevent burnout problem.
]. In Vietnam, stress is rising as a common problem which has been studied in variety of fields, such as psychology, physiology, and medical aspect. But the research on job stress was still limited [
]. Different individuals of job stress, such as general workers or medical staff, were examined. Typically, Pham investigated work-related depression at a shoe-manufacturing factory [
]. According to Nguyen and Ta's survey in 2005 implemented at three central hospitals, doctors had the highest stress levels compared with nurses and other health workers [
] found that nearly half of nurses had high stress. High level of job stress may result in burnout. The job burnout among clinical nurses in Vietnam has been concerned, but no study has been found at all.
The Vietnamese researchers used several different measurement scales to assess stress level. Le et al [
] to stress evaluation although they did not examine the validity and reliability of this scale. The instrument that has been validated to measure job stress in Vietnam is the Job Content Questionnaire (JCQ) by Karasek [
]. According to the NIOSH, job stress can be defined as the harmful physical and emotional responses that occur when the requirements of the job do not match the capabilities, resources, or needs of the worker. Job stress can lead to poor health and even injury. The NIOSH has created the NIOSH general job stress questionnaire including both job stress and job stressor measurement. The JCQ preferred to mention on three sub-domains of psychological demands, decision latitude, and social support at work. Leiter and Maslach [
] identified six key domains that lead to burnout; then, the AWS has been generated and suggested as a reasonable scale to estimate burnout at work. AWS's validity and reliability have been shown in USA, Canada, Finland, and Italy [
]. However, only the JCQ has been translated and validated in Vietnam, impeding further progress in research. Because there is a need for more scales to be translated and validated for research purposes in Vietnam, in this study, a Vietnamese version of the AWS will be generated.
To deal with job stress, a number of job stress measures are recommended. The General Health Questionnaire [
] is used to detect psychological issues such as depression or anxiety. It assesses the respondent's current state and asks if that differs from his or her usual state. It is therefore sensitive to short-term psychiatric disorders but not long-standing attributes of the respondent. Further, the Maslach Burnout Inventory (MBI) [
] has been used in many studies to assess burnout level. The MBI has been adapted to many kinds of people with different scales of MBI-Human Services Survey for human service professions, MBI-Educator Survey for educator, and MBI-General Survey (MBI-GS) for general workers [
]. The MBI has not been introduced to researchers on occupational stress in Vietnam. This study will be the first to translate and examine the MBI-GS for validation in Vietnam. It is essential to assess job stressor and stress with globally used scales for further development of occupational health in Vietnam.
Our objectives are as follows. First, we create a Vietnamese version of MBI-GS and AWS. Then, we assess the burnout state of Vietnamese clinical nurses using the translated MBI-GS. Finally, we develop a causal model of burnout among clinical nurses in Vietnam using two scales. We apply Leiter and Maslach's theory [
Leiter and Maslach developed the causal model of burnout. They collected data from a normative sample in USA, Canada, Finland, and Italy which included health-care professionals such as nurses, using the MBI-GS and AWS. Furthermore, they tested the model applying to Canadian nurses. Therefore, Leiter and Maslach's model might be working in describing nurses' burnout in Vietnam.
Methods
Study design
A cross-sectional study was used to carry out the survey.
Setting and sample
Participants were clinical nurses working in different departments at three public hospitals in Hai Phong, Vietnam: one general hospital and two specialty hospitals (a children's hospital and an obstetric hospital).
Ethical considerations
Nurse participants in this survey were volunteers and were anonymous. Participants fully understood the research and agreed to respond to the questionnaire. Instruments using in this study has been approved according to Permissions Letter issued by Mind Garden. This study was approved by the Kanazawa University Medical Ethics Committee, Japan (Approval no. 649-1) dated on January 6, 2016. This research conforms to the provisions of the Declaration of Helsinki in 1995 (as revised in Edinburgh 2000). We obtained the consent of the managing board of the three hospitals and departments before implementing the survey.
Measurements
Three sets of questionnaires were administered to the participants: (1) a demographic questionnaire, (2) a MBI-GS questionnaire [
]. The demographic questionnaire related to sex, age, marital status, children, age of children, years of nursing work, workplace, nursing position, on-duty work schedule, and education level.
The MBI-GS consists of three scales totaling 16 items: exhaustion with five items, cynicism with five items, and professional efficacy with six items. Each item is scored using a 7-point Likert scale (never, a few times a year, once a month, a few times a month, once a week, a few times a week, and daily) ranging from 0 to 6. We obtained permission from Mind Garden to use and translate the MBI-GS into the Vietnamese language. The translation process entailed two phases: (1) forward translation, involving translation of the MBI-GS from English to Vietnamese by a bilingual researcher; and (2) back-translation, involving two independent bilingual translators, both of whom majored in English teaching and translation. Afterward, two researchers carefully compared the back-translated version with the original English version item-by-item to evaluate conceptual equivalence.
The 28-item AWS questionnaire consists of six key domains: workload (5 items), control (4), rewards (4), community (5), fairness (6), and value (4). Each scale contains both a positive word item [e.g., “I have enough time to do what's important in my job” (workload)] and a negative word item [e.g., “I do not get recognized for my contributions at work” (rewards)]. Participants indicated their degree of agreement with these statements using a 5-point Likert scale ranging from 1 (strongly disagree) to 3 (hard to decide) to 5 (strongly agree). Scoring for the negative word item is reversed. The translation process of the AWS questionnaire was identical to that of the MBI-GS questionnaire. Vietnamese version of AWS also obtained permission from Mind Garden to use and translate.
Data collection
We calculated the intended sample size using a formula for sample size estimation in a cross-sectional study, with an alpha value of .05 and an estimated p value of .50. Calculated sample size was 385 nurses, which was a safe sample size for this study. To avoid nonresponse, 500 administered questionnaires were delivered to clinical nurses at three hospitals using random sampling. In Vietnam, a general hospital, a children's hospital, and an obstetrics hospital are run by the local government for citizens, and those three hospitals are located in each province.
Based on the number of nurses at each hospital, 260 questionnaires were sent to the general hospital, 90 to the children's hospital, and 150 to the obstetrics hospital. Overall, 443 questionnaires were returned (general hospital, n = 234; children's hospital, n = 80; obstetrics hospital, n = 129) with a response rate of 88.6% (general hospital, 90.0%; children's hospital, 88.8%; obstetrics hospital, 86.0%). However, among 443 responses, there were 13 invalid responses with the same answer given for the entire scale or where there were missing data for some questions. Therefore, we obtained a final sample size of 430 nurses (general hospital, n = 230; children's hospital, n = 78; obstetrics hospital, n = 122) for statistical analysis.
The nurses received a clear explanation of the study before filling out the questionnaires. Researchers gave the questionnaires to nurses during their morning daily meeting and made an appointment to retrieve them. Data collection was implemented from January 2016 to March 2016.
Data analysis
To verify the factorial validity of the translated MBI-GS and AWS, we used explanatory factor analysis (maximum likelihood and promax rotation). We then assessed the reliability of the MBI-GS and AWS subscales by Cronbach α to indicate internal consistency. Then, we conducted a one-way analysis of variance to analyze the differences in burnout scores among variable groups, using multiple regression analysis to predict burnout-related factors and applying the revised exhaustion+1 burnout classification [
] by Chi-square test to assess burnout state. Finally, we performed path analysis by using comparative fit index (CFI), goodness of fit index (GFI), root mean square error of approximation (RMSEA), and Akaike's information criterion (AIC) to create a causal model and test the model fit indices of the Vietnamese version based on the original hypothesized model of Leiter and Maslach [
Table 1 shows participants' characteristics. There were more female nurses (88.1%) than male (11.9%). Average age of participants was 31.64 ± 6.72. Most nurses were already married (78.8%) and had children aged under 11 years (75.3%). As for job factors, average years of nursing work was 8.6 ± 6.48, and working year group from 1 to 5 years had the highest percentage of 41.9%. Conversely, the group with more than 15 years of working was 13.9%. The most common on-duty work schedule was two days per week (63.5%), and most nurses had completed a two-year nursing diploma (65.3%).
Table 1Subscale Scores of Vietnamese MBI-GS by Characteristics (N = 430).
Variables
n (%)
Exhaustion
Cynicism
Professional efficacy
Mean ± SD
F or t
p
Mean ± SD
F or t
p
Mean ± SD
F or t
p
Sex
Female
379 (88.1)
3.05 ± 1.05
0.63
.429
2.67 ± 0.97
0.49
.482
3.98 ± 0.74
0.62
.432
Male
51 (11.9)
2.92 ± 1.07
2.57 ± 1.04
4.07 ± 0.82
Age group (yr)
20–29
206 (47.9)
3.33 ± 1.01
16.65
<.001
2.86 ± 0.97
8.79
<.001
3.80 ± 0.64
13.90
<.001
30–39
164 (38.1)
2.78 ± 1.07
2.45 ± 0.98
4.14 ± 0.82
Above 39
60 (14.0)
2.72 ± 0.88
2.54 ± 0.88
4.24 ± 0.75
Marriage status
Married
339 (78.8)
2.92 ± 1.04
18.47
<.001
2.58 ± 0.97
9.94
.002
4.07 ± 0.75
18.89
<.001
Unmarried
91 (21.2)
3.45 ± 1.02
2.95 ± 0.97
3.69 ± 0.69
Children
With
315 (73.5)
2.91 ± 1.05
16.89
<.001
2.58 ± 0.98
7.77
.006
4.07 ± 0.76
15.11
<.001
Without
115 (26.7)
3.37 ± 0.99
2.88 ± 0.96
3.76 ± 0.67
Age of children (yr)
Above 11
78 (24.7)
2.76 ± 0.90
9.53
<.001
2.57 ± 0.90
3.88
.021
4.25 ± 0.74
10.73
<.001
Below 11
237 (75.3)
2.96 ± 1.09
2.58 ± 1.00
4.02 ± 0.76
Year of nursing work
1–5
180 (41.9)
3.37 ± 1.00
11.94
<.001
2.87 ± 0.93
6.07
<.001
3.77 ± 0.63
12.06
<.001
6–10
126 (29.3)
2.87 ± 1.10
2.61 ± 1.04
4.03 ± 0.76
11–15
64 (14.9)
2.74 ± 1.01
2.32 ± 0.97
4.25 ± 0.82
Above 15
60 (13.9)
2.67 ± 0.88
2.50 ± 0.89
4.03 ± 0.77
Workplace
Surgical
124 (28.8)
2.96 ± 1.13
0.67
.574
2.47 ± 1.12
2.84
.038
4.16 ± 0.72
6.02
.001
Medical
106 (24.7)
3.11 ± 1.11
2.65 ± 1.04
3.99 ± 0.80
Pediatric
78 (18.1)
3.12 ± 0.95
2.86 ± 0.73
4.06 ± 0.61
Obstetric
122 (28.4)
2.98 ± 1.00
2.72 ± 0.88
3.77 ± 0.77
Position
Staff nurse
398 (92.6)
3.11 ± 1.01
34.18
<.001
2.75 ± 0.92
56.67
<.001
3.91 ± 0.70
64.93
<.001
Head nurse
32 (7.4)
2.02 ± 1.04
1.48 ± 0.92
4.95 ± 0.70
On-duty work schedule
0 day
29 (6.7)
2.01 ± 1.01
28.21
<.001
1.52 ± 1.04
27.39
<.001
5.04 ± 0.62
28.59
<.001
1 day
57 (13.3)
2.68 ± 1.01
2.3 ± 0.89
3.90 ± 0.81
2 days
273 (63.5)
3.01 ± 0.95
2.71 ± 0.90
3.99 ± 0.69
More than 2 days
71 (16.5)
3.80 ± 0.85
3.21 ± 0.85
3.65 ± 0.55
Qualification
Four years BScN degree
79 (18.4)
2.41 ± 0.98
18.43
<.001
2.00 ± 1.03
23.99
<.001
4.50 ± 0.79
25.27
<.001
College Nursing Diploma
70 (16.3)
3.08 ± 0.94
2.77 ± 0.87
3.92 ± 0.69
Two-year Nursing Diploma
281 (65.3)
3.19 ± 1.04
2.82 ± 0.92
3.86 ± 0.69
Total
430 (100.0)
3.03 ± 1.05
2.66 ± 0.98
3.99 ± 0.75
Note. ANOVA=Analysis of variance test; MBI-GS=Maslach Burnout Inventory-General Survey; SD = standard deviation.
Psychometric properties of the Vietnamese MBI-GS and AWS
Table 2 shows factor loading of the Vietnamese MBI-GS. Initially, four factors with an eigenvalue more than one were presented. However, in the original MBI-GS version, exploratory factor analysis showed three factors. Therefore, we chose three factors for rotation. After extraction, factor 1 was identified as exhaustion, factor 2 as professional efficacy, and factor 3 as cynicism. The Cronbach α of exhaustion, professional efficacy, and cynicism reached values of .89, .80, and .77, respectively. For exhaustion and professional efficacy, loading of all items indicated a sufficient value greater than .30. However, for cynicism, three items (i.e., 8, 9, and 13) did not load significantly (less than .30) [
]. According to factor loading, these items should be located within the exhaustion subscale. Therefore, based on these results, we did not score items 8, 9, and 13 in the present study.
Table 2Exploratory Factor Analysis of Vietnamese MBI-GS (N = 430).
Items
Factor
1
2
3
Exhaustion (α = .89)
MBI-GS 2
.99
.04
−.15
MBI-GS 1
.99
.10
−.15
MBI-GS 3
.76
−.01
.08
MBI-GS 4
.75
.02
.15
MBI-GS 6
.52
−.05
.21
Professional efficacy (α = .80)
MBI-GS 10
−.06
.80
.24
MBI-GS 11
−.08
.75
.07
MBI-GS 12
−.01
.70
.01
MBI-GS 7
.10
.59
−.12
MBI-GS 16
.01
.55
−.06
MBI-GS 5
.21
.53
−.11
Cynicism (α = .77)
MBI-GS 15
−.01
.01
.88
MBI-GS 14
.06
.01
.74
MBI-GS 9
.43
−.17
.29
MBI-GS 8
.35
−.22
.28
MBI-GS 13
.31
.15
.20
Factor correlation matrix
1
−.52
.60
2
−.45
Note. Extraction method: maximum likelihood; Rotation method: promax with Kaiser normalization.
Table 3 shows exploratory factor analysis of the Vietnamese AWS. Initially, by natural loading, eight factors were extracted with eigenvalue more than one. As in the original AWS version, six factors were constructed. Therefore, we chose six factors for rotation. We did not score subscale items 5 (workload), 6 (control), 14 and 15 (community), and 21 (fairness) because they did not satisfy factorability criteria (at least .30). We calculated internal consistency of each subscale using Cronbach α. Alpha values were acceptable when the following five items were not scored: .74 for workload (without item 5), .62 for control (without item 6), .68 for community (without items 14 and 15), and .77 for fairness (without item 21). Alpha values of rewards and value were .71 and .81, respectively.
Table 3Exploratory Factor Analysis of Vietnamese AWS (N = 430).
In Table 1, the mean score on exhaustion was 3.03 ± 1.05, cynicism has an overall mean score of 2.66 ± 0.98, and professional efficacy has an overall mean score of 3.99 ± 0.75. There were significant differences in each factor except gender. Nurses who worked for more than two days had the highest scores for exhaustion and cynicism and the lowest for professional efficacy.
Table 4 presents predictors for burnout. “On-duty work schedule” was a significant predictor in three subscales. The best predictors of professional efficacy were marriage status, years of nursing work, workplace, and position. The best predictors of cynicism were workplace, position, and qualifications.
]. In general, most nurses (62.8%) were healthy, whereas 17.2% were classified as exhausted, 15.8% with burnout, 3.5% as depressed, and 0.7% with severe burnout. We found no relationship between gender, workplace, and burnout level. Among the age group of nurses older than 39, 80.0% were in good health. The burnout rate of unmarried nurses without children was twice that of married nurses with children. As for job factors, the least healthy was the group with 1–5 years of nursing work (50.0%). A higher rate of staff nurses than head nurses was represented in most burnout categories. Nurses with more than two days on duty had the highest rate of burnout and exhaustion.
Table 5Burnout Level by Characteristics (N = 430).
A path analysis of the hypothesized model produced the following results: Chi-square (χ2 = 204.37, p < .001), CFI = .82, GFI = .92, RMSEA = .16, and AIC = 260.38. These results did not meet criteria for model fit. We then performed a modified model with the control setup as the foundation. After analysis, these results (χ2 = 58.47, p < .001; CFI = .96; GFI = .97; RMSEA = .08; AIC = 120.46) met criteria for model fit. All paths were significant with 14 degrees of freedom. Regression coefficients are shown in Figure 2. The modified model presents different paths to compare with the original model, not only workload but also rewards and fairness directly linked to exhaustion. Control and community were directly linked to professional efficacy. There was also a direct path from control to five other work domains. From the hypothesized model, we identified three factors (rewards, community, and fairness) that were relevant to value, although only fairness showed a direct path to value in the modified model. In addition, value led to three domains of burnout in both causal and hypothesized models.
We chose to translate and test the validity and reliability of the MBI-GS and AWS as no research on measurement scales to assess job stressor and burnout has yet been carried out despite the rising levels of job stress in Vietnam [
]. The MBI-GS and AWS are well used in the domain of occupational health research. The translated versions of MBI-GS exist such as the Japanese version [
], even in Asian countries. The Vietnamese version we created in the present study would be useful to develop occupational health research in Vietnam.
The construct validity evaluation supported a three-factor structure of the MBI-GS. But our analysis suggested that items 8, 9, and 13 of the scale should have been located in the exhaustion rather than cynicism subscale. The same issue has been mentioned in the Malaysian version, and authors suggested it to be mainly the result of differences in cultural knowledge and experience [
] have supported the original version. The factor structure of the AWS was supported in the present study, but factor loadings of five items (5, 6, 14, 15, and 21) were insufficient. For item 5 of workload scale, “I leave my work behind when I go home at the end of the workday” conflicts with Vietnamese culture, which is unfamiliar with the concept of leaving one's job behind. The Japanese version of AWS [
] has the same issue regarding item 5. For item 21 of fairness scale, “There are effective appeal procedures available when I question the fairness of a decision” refers to procedures that actually do not exist at the three hospitals in the present study. The Spanish version supported original version without any eliminated item [
] has excluded two items in which one item from fairness sub-scale and other item belongs to value sub-scale because of increasing internal consistency for satisfactory and possible explanation was that item content did not differ among the participants. Item 15 of community domain “I am a member of a supportive work group” might confuse Vietnamese nurses as “supportive work group” is not a fully understood concept in the Vietnamese health-care system. Finally, item 6 “I have control over how I do my work” and item 14 “People trust one another to fulfill their roles” require more attention and examination. In general, the Vietnamese versions of the MBI-GS and AWS can be used because both scales have the same factor structure as the original ones. But further examination is needed regarding issues aforementioned to be resolved. Meanwhile, we recommend scoring both scales based on results from the analysis researcher performance.
Burnout state of Vietnamese clinical nurses
As a whole, we found that Vietnamese clinical nurses were healthy (62.8%), but this finding was slightly higher than that reported in Japanese mental health nurses (53%) and much lower than that in Finnish mental health nurses (93.5%) [
]. The burnout rate in the present study was also higher than that of Japanese neurosurgeons but lower than that of Japanese mental health nurses. In contrast, the number of nurses suffering from exhaustion was quite high (17.2%) in the present study compared with that of nurses from Japan (10.6%) and Finland (3.7%) [
]. Such a high exhaustion rate represents a potential problem if nothing is done to prevent burnout in these nurses.
The highlight of our research was finding that “on-duty work schedule” was the most significant predictor in three subscales of burnout. More on-duty days (more than two days) per week led to severe exhaustion. The term “on-duty work schedule” in Vietnam means that clinical nurses keep working on wards for 24 hours according to health-care system regulations (8 hours per day and 16 hours all night) followed by a day’s off [
]. In reality, at the three hospitals in this study, only two nurses are assigned to provide all-night care to an ever-changing number of patients in the department (excluding special units such as intensive care unit or emergency). When patient numbers are high and nursing staff are inadequate, work overload can certainly lead to exhaustion. Logically, more exhaustion easily leads to reduced professional efficacy. A strong relationship between stress and being on duty work was found by medical staff in a job stress research [
]. A greater degree of stress and burnout was detected among nurses on shift duty. Nurses with smaller hospital type and no shift work were also associated with lower exhaustion and depersonalization together with higher personal accomplishment [
]. Therefore, it is recommended to arrange appropriate number of nurse staff in each department or to adjust work shift schedules to prevent this growing problem.
Causal model of burnout
The primary purpose of this research was to identify contributory factors to worklife to predict burnout. This study is the first one that targets clinical nurses in Vietnam. In general, we found both similar and different directions of effect as compared with the Leiter and Maslach's model [
], whereas some studies significantly supported the original model described by Leiter and Maslach such as their structural model found most similar pattern in significance and direction of effects [
]. All of these research studies have come from Europe and America.
In identical fashion of this study, control has a strong pathway to other aspects of work environment and plays a central role in burnout prediction. However, the original model posits that control has an indirect impact on professional efficacy, whereas we found that control had a direct effect on professional efficacy. It makes sense that lack of control might lead to low productivity or reversal. This result is also mentioned in a study where employees have ability to give influence decision that affects their work that they are more likely to do an effective job [
]. The issue of control reflects the opportunity for decision-making and independence at work, and lack of participation in decision-making has been most predictive of reduced professional efficacy [
]. However, besides workload, our findings showed that rewards and fairness also led to exhaustion. The strong relationship between these factors suggests that unequal treatment and feeling unrecognized or unappreciated at work can cause feelings of exhaustion. Regarding fairness, our results support this premise considering the lack of effective appeals procedures available in Vietnam for nurses who question the fairness of a decision (item 21 of the AWS). Furthermore, inadequate nurse staff can make it difficult to fairly allocate resources in a hospital. Consequently, these issues can be emotionally draining on nurses. In another study, workplace fairness was a strong predictor of emotional exhaustion among male and female police in Nigeria [
]. As for rewards, instead of being linked to value as in the original model, we proposed a relationship between rewards and exhaustion. It is understandable that lack of recognition and appreciation for contributions at work can lead to feelings of exhaustion. Recent publication has introduced the concept of effort–reward imbalance, which has been positively related to emotional exhaustion [
Lasalvia et al has argued that rewards and fairness are directly linked to job efficacy and that poor rewards and unequal treatment were most predictive of professional efficacy. On the other hand, community was directly linked to job effectiveness in the present study [
] also argued that work groups with poorer connections are at higher risk of burnout. Furthermore, coworker support has been more closely related to accomplishment or efficacy, whereas supervisor support was said to be more consistent with exhaustion [
The original model of Leiter and Maslach's theory is well known and mostly supported by population of countries in the European Union and North America. In other countries, studies regarding relationship between burnout and AWS model have not been published yet, besides our study. In summary, their theory would also be working in describing Vietnamese nurses' burnout. Both burnout and causes of burnout among nurses can be presented in their model as we mentioned previously. However, our study also contributed some valuable knowledge that their original model might need to be revised in terms of Asian sample including Vietnam. Further examinations would give us resolution.
Conclusion
Based on our findings, the reliability and validity of the Vietnamese MBI-GS and AWS are acceptable for use in future investigations. Taking into consideration the state of burnout in Vietnam, approximately 20% of clinical nurses were in burnout including severe burnout. It is nearly the same rate as nurses with exhausted feeling; those nurses could be easily in danger of burnout if no prevention was done. More days' on-duty work strongly predicted burnout. The causal model proposed that control played an important role as the exogenous factor for burnout prediction, whereas exhaustion was directly caused not only by workload and value but also by rewards and fairness. Professional efficacy was strongly involved in community as well as control and value. Cynicism was caused only by value.
Our results suggest various factors leading to burnout. According to these results, many solutions or interventions can be applied for burnout prevention, such as consideration of more nurses and appropriate work schedules. Solutions to limit exhaustion might also include minimizing favoritism by managers and ensuring fair distribution of opportunities. In addition, rewarding excellent performance with bonuses or promotions might effectively reduce exhaustion among nurses. Finally, work group support might greatly help increase profession efficacy. Further studies are necessary to research on other participants for the overall picture of burnout in Vietnam.
Conflicts of interest
There is no conflict of interest regarding this study.
Acknowledgments
This study was self-supported. The authors are grateful to and sincerely thank the management boards of the hospitals, clinical nurses at selected hospitals, and nursing faculty of Hai Phong University of Medicine and Pharmacy, Vietnam. The authors also thank the Kanazawa University Medical Ethics Committee, Japan, for approving their research.
References
Schuler R.S.
Definition and conceptualization of stress in organizations.
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