*Department of Anesthesiology, **Department of Psychology, Faculty of Medicine, Thammasat University, Pathum Thani 10120, Thailand.
ABSTRACT
INTRODUCTION
Burnout is a work-related syndrome characterized by emotional exhaustion, depersonalization, and a sense of reduced personal accomplishment.1 It responds to chronic job-related stressors and is exclusively associated with the work environment. Burnout has been associated with strained relationships among team members2, leading to adverse effects on health and overall quality of life. Physicians have reported increasing levels of stress and burnout3, which can have detrimental effects on the quality of patient care and potentially contribute to medical errors.4,5
Anesthesiologists exhibit a relatively high prevalence of
burnout. The prevalence of burnout among anesthesiology varies across studies, ranging from 13.8% for burnout syndrome to as high as a 59% risk of burnout.6,7 Of particular concern is the high risk of burnout during residency training. A survey conducted among anesthesiology residents in the United States found that 41% were at high risk of burnout.8 The precise reasons of anesthesia residents’ burnout remain unclear. However, it is plausible that they lack experience and need to acquire knowledge, cognitive abilities, and technical skills, as well as adapt to efficient perioperative teamwork, which contributes to this heightened risk.
Corresponding author: Neranchala Soonthornkes E-mail: neransoon@gmail.com
Received 9 August 2023 Revised 5 September 2023 Accepted 12 September 2023 ORCID ID:http://orcid.org/0000-0002-7884-695X https://doi.org/10.33192/smj.v75i10.264582
All material is licensed under terms of the Creative Commons Attribution 4.0 International (CC-BY-NC-ND 4.0) license unless otherwise stated.
MATERIALS AND METHODS
Following the approval from the Ethics Committee of Thammasat University (COA No.023/2566), the data collection period took place from January 22, 2023, to February 12, 2023.
Inclusion criteria were Thai anesthesiology residents who were in the Thai anesthesia training program during the study period. Anesthesia residents who declined to answer the questionnaire were excluded.
The Questionnaires consisted of four sections: a consent form, personal data, potential risk factors of burnout, and the Thai version of the Copenhagen Burnout Inventory Student Survey (Thai CBI-SS). The questionnaire was developed using the online Google Forms application. A research assistant reached out to the representatives of 14 institutions throughout Thailand that host anesthesia resident training programs to share the online questionnaire with all anesthesia residents. Participants could accept or decline answering it. All responses were collected anonymously. To ensure maximum participation, the research assistant sent reminders once a week for three consecutive weeks.
Participants were asked to furnish details about their demographic characteristics (including gender, age, marital status, years of resident training, underlying disease, income, smoking habits, alcohol consumption, and exercise habits). The potential risk factors of burnout were reviewed in relevant literature.7, 8-10 These factors included working hours, shift hours, adequacy of consultation, sleep duration, workplace resources, job support at work and at home, satisfaction with resident training, an idea to discontinue training, as well as demographic characteristics data. The final section was the Thai version of the Copenhagen Burnout Inventory Student Survey (Thai CBI-SS). The Thai CBI-SS consisted of a comprehensive set of twenty-five questions categorized into four domains: personal burnout (six items), study-related burnout (seven items), colleague-related burnout (six items), and teacher-related burnout (six items). Each item was rated on a five-point Likert scale, ranging from ‘100 (always)’ to ‘0 (never/almost never)’. Scores between 50 and 74
were categorized as ‘moderate’ burnout, scores between 75 and 99 were classified as ‘high’ burnout and a score of 100 indicated severe burnout.
The Copenhagen Burnout Inventory (CBI), developed by Kristensen TS11, has demonstrated good reliability and validity as a measure of burnout. Winwood and Winefield12 confirmed that the CBI effectively conceptualizes burnout, and exhibits strong reliability and validity. Moreover, the Copenhagen Burnout Inventory-Student Survey (CBI-SS)13 has been acknowledged as a reliable and valid instrument for evaluating burnout among medical students. Correlational analyses between CBI-SS and the Maslach Burnout Inventory-Student Survey (MBI-SS), a widely-used standardized tool, have revealed moderate to strong correlations. The CBI-SS has undergone linguistic translation and cultural adaptation into several languages, including Thai. Research has validated the Thai version of the Copenhagen Burnout Inventory-Student Survey (CBI-SS) and demonstrated its reliability as an effective tool for evaluating burnout syndrome among preclinical medical students in Thailand.14
Although anesthesia residents are part of a postgraduate training program, in the Thai cultural educational system, Thai residents interact with the staff in a teacher/student relationship. The decision was made to utilize the CBI- SS instead of the CBI because the CBI-ss has a teacher- related burnout question domain.
The sample size was calculated based on a previous report of the prevalence of burnout (41%), with an acceptable margin of error (0.05). The calculated sample size was 372. The decision was made to recruit all Thai anesthesia residents at the time of the study, which totaled 385 considering the survey dropout rate. The data were analyzed using StataCorp. Version 17. College Station, TX: StataCorp LLC; 2021. Descriptive statistics summarized the characteristics. Categorical variables were depicted in the form of frequencies and percentages. Continuous variables were described using either means with standard deviations or medians with interquartile ranges. For comparing continuous variables, t-tests or Wilcoxon rank-sum tests were employed. Categorical variables were compared using either chi-square tests or Fisher exact tests. Logistic regression analysis identified predictors of burnout. Univariate associations were initially examined, and factors with a p-value < 0.1 were included in the multivariable logistic regression model to identify independent risk factors. Results were reported as odds ratios with 95% confidence intervals. A p-value
< 0.05 indicated statistical significance.
RESULTS
Out of the 385 Thai anesthesia residents from 14 institutions who were invited to participate in the online questionnaire, a total of 250 individuals responded, resulting in a response rate of 64.94%. Among the respondents, 248 participants completed all the questions, while 2 residents declined to answer the questionnaire.
The majority of respondents, 200 were female (80.65%). The median age was 29 years old, with a range of 27 to 30 years. Among the respondents, there were 77 first-year residents (31.05%), 77 second-year residents (31.05%), and 94 third-year residents (37.90%) who completed the questionnaire.
Regarding underlying disease, 15 individuals (6.05%), reported underlying diseases including allergic rhinitis, polycystic ovary syndrome, gastritis, obstructive sleep apnea, migraine, and Glucose-6-Phosphate Dehydrogenase deficiency. Five residents (2.02%) had a major depressive disorder. Further details of participant characteristics are shown in Table 1.
The prevalence of burnout among Thai anesthesia residents who responded to the questionnaire was 35.48% (95% CI 0.30 to 0.42), with 33.06% experiencing moderate burnout and 2.42% experiencing high burnout. No participants reported severe burnout. These findings indicate that burnout is a significant concern among Thai anesthesia residents.
The univariate analysis was conducted to screen factors associated with burnout and found ten factors associated with burnout. The results are presented in Table 2. Following the screening procedure, a multivariable logistic regression analysis was conducted to validate the findings, identifying four significant factors which are sleep duration, year of resident training, dissatisfaction, and having an idea of discontinuation of resident training associated with burnout. The result is shown in Table 3. Sleeping less than 7-8 hours was identified as a significant risk factor for burnout, particularly among residents who slept less than 5 hours (odds ratio 6.89, 95% CI 1.90 to 24.92, P = 0.003). Additionally, residents who slept for 5-6 hours also had a higher risk of burnout
(odds ratio 3.68, 95% CI 1.40 to 9.68, P = 0.008).
Dissatisfaction with resident training and having an idea of stopping resident training were found to be significant risk factors for burnout (odds ratio 8.38, 95%
CI 3.65 to 19.25, P = 0.00, and odds ratio 3.11, 95% CI
1.57 to 6.18, P = 0.001, respectively).
Compared to first-year residents, second-year residents had a significantly lower risk of burnout (odds ratio 0.32, 95% CI 0.134 to 0.76, P = 0.009). However, no significant difference was observed between third-year residents and first-year residents (odds ratio 0.54, P = 0.17, 95% CI 0.25 to 1.17).
DISCUSSION
The prevalence of burnout among Thai anesthesiology residents is high at 35.48%, although lower than reported in the United States by Oliveira et al. (41%)8 and Huaping Sun (51%).9 Various factors may contribute to this difference, including individual variations, cultural influences, and differences in training programs between countries. Additionally, the study’s sample size may have influenced the prevalence rate, as only 66.67% (248 of 372) of the target sample size was recruited. However, this online response rate of 64.94% is higher than the average reported in recent meta-analyses of online surveys which was around 44.1%.15
The multivariable logistic regression analyses identified factors including sleeping time, year of training, satisfaction with the training program, and the idea of discontinuation of training that were significantly associated with burnout among Thai anesthesiology residents. The results align with previous research10,16 that has found an association between insufficient sleep and increased risk of burnout. In this study, sleeping less than 7 hours increased the burnout risk. Insufficient sleep can have various physical, cognitive, and emotional effects that increase stress levels and make individuals more susceptible to burnout. While this study focused on sleep duration, Hannah K. Allen17 has found an association between the poor quality of sleep which is characterized by difficulties falling asleep or frequent awakenings, and burnout. Additionally, Chatlaong T found an association between sleep quality and emotional exhaustion, which is a component of burnout among resident trainees.18 However, our questionnaire evaluated only the duration, not the quality, of sleep. It would be valuable for future studies to consider evaluating both sleep duration and quality to provide more understanding of the relationship between sleep and burnout.
According to the research conducted by Huaping Sun and De Oliveira GS8,9, it was discovered that an increase in the age of physician trainees corresponded to a reduced risk of experiencing burnout. The second- year residents had a significantly lower risk of burnout compared to first-year residents, while there was no
TABLE 1. Descriptive statistics of respondent characteristics. TABLE 1. Descriptive statistics of respondent characteristics.
Characteristic | N 248 |
Job support at home | |
No support | 198 (79.84) |
Not enough | 43 (17.34) |
Enough | 7 (2.82) |
Job support at work | |
No support | 75 (30.24) |
Not enough | 153 (61.69) |
Enough | 20 (8.07) |
Resource for working | |
Not enough | 118 (47.58) |
Enough but not easy to use | 92 (37.10) |
Enough and easy to use | 38 (15.32) |
Consultation | |
No consultation | 122 (49.19) |
Not enough | 95 (38.31) |
Enough | 31 (12.5) |
(Continue)
Sex
Male 48 (19.35)
Year of resident training 3rd year
2nd year
1st year
94 (37.90)
77 (31.05)
77 (31.05)
Female 200 (80.65)
Marital status Single Married
In relationship
139 (56.05)
17 (6.85)
92 (37.10)
Age 29 (27-30)
Have children 5 (2.02)
Have underlying disease 15 (6.05)
Have Psychological disease 5 (2.02)
Smoking 2 (0.81)
Exercise Routine Sometimes
No
16 (6.45)
125 (50.4)
107 (43.15)
Alcohol drinking 128 (51.61)
Official working hours per month
>160 119 (47.98)
128-160 115 (46.37)
Shifts hours per month
>160
128-160
88-120
40-80
<40
13 (5.24)
54 (21.77)
73 (29.44)
97 (39.11)
11 (4.44)
80-120 14 (5.65)
Income
Enough to save 8 (3.23)
Enough to spend 75 (30.24)
Sleeping time (hours) 7-8
5-6
<5
31 (12.50)
169 (68.15)
48 (19.35)
Not enough 165 (66.53)
Dissatisfaction with resident training 62 (25)
Having thought about discontinuing training* 114 (45.97)
Data are presented as number (%) or median (range). *One piece of data is missing from the answers of 248 participants.
significant difference among third-year residents. A possible explanation for this finding is that second-year residents have had more time to adapt to their work, studies, and interactions with colleagues and the work environment. They may have gained more experience and developed better coping strategies, which could contribute to a lower risk of burnout. On the other hand, first-year residents are faced with the challenges of acquiring new knowledge and navigating stressful situations which could increase their risk of burnout. Interestingly, our study did not find a significant difference in burnout risk between first-year and third-year residents. This could be attributed to the fact that third-year residents carry higher responsibilities, face greater expectations, and need to maintain high levels of concentration as they prepare for the board-certified examination. These factors may offset the additional experience they have gained.
Residents who expressed dissatisfaction with the training program and considered discontinuing their training had a significantly higher risk of burnout. The association between burnout and job satisfaction has been observed in previous studies, such as Govardhan LM’s study, which found an inverse correlation between burnout
TABLE 2. Univariate analysis for factors associated with burnout.
Variable | Risk of burnout | P value | Univariate OR | 95%CI |
Gender Female Male | 68 (34.00) 20 (41.67) | 0.32 | 0.72 | 0.38-1.37 |
Age | 0.626 | 1.04 | 0.90-1.20 | |
Year of resident training 3rd year 2nd year 1st year | 31 (32.98) 18 (23.38) 39 (50.65) | 0.020 0.001 | 0.48 0.30 | 0.26-0.89 0.15-0.59 |
Marital status Married In relationship Single | 6 (35.29) 23 (25.00) 59 (42.44) | 0.573 0.007 | 0.74 0.45 | 0.26-2.11 0.25-0.81 |
Have children Yes No | 2 (40.00) 86 (35.39) | 0.831 | 1.22 | 0.20-7.42 |
Underlying disease Yes No | 8 (53.33) 80 (34.33) | 0.144 | 2.19 | 0.77-6.24 |
Psychological disease Yes No | 2 (40.00) 86 (35.39) | 0.831 | 1.22 | 0.20-7.42 |
Smoking No Yes | 88 (35.77) 0 (0.00) | 0.540 | ||
Alcohol drinking Yes No | 44 (34.38) 44 (36.67) | 0.706 | 0.9 | 0.54-1.52 |
Exercise Sometimes Regular No | 49 (39.20) 7 (43.75) 32 (29.90) | 0.140 0.272 | 1.51 1.82 | 0.87-2.61 0.62-5.32 |
Income Not enough Enough for spending Enough for saving | 5 (62.50) 36 (48.00) 47 (28.48) | 0.056 0.004 | 4.18 2.32 | 0.96-18.21 1.32-4.08 |
Official working hours per month (hour) >160 | 58 (48.74) | 0.017 | 12.36 | 1.57-97.51 |
128-160 | 29 (25.22) | 0.163 | 4.38 | 0.55-34.99 |
80-120 | 1 (7.14) |
TABLE 2. Univariate analysis for factors associated with burnout. (Continue)
Variable | Risk of burnout | Univariate | ||
P value | OR | 95%CI | ||
Shift hours per month | ||||
>160 | 4 (30.77) | 0.851 | 1.19 | 0.20-6.99 |
128-160 | 14 (25.93) | 0.926 | 0.93 | 0.22-4.02 |
88-120 | 19 (26.03) | 0.930 | 0.94 | 0.23-3.91 |
40-80 | 48 (49.48) | 0.174 | 2.61 | |
<40 | 3 (27.27) | |||
Sleeping time (hour) | ||||
<5 | 20 (64.52) | <0.001 | 6.91 | 2.51-19.03 |
5-6 | 58 (34.32) | 0.079 | 1.99 | 0.92-4.27 |
7-8 | 10 (20.83) | |||
Resource for working | ||||
Not enough | 21 (55.26) | 0.003 | 3.18 | 1.50-6.77 |
Enough but not easy to use | 34 (36.96) | 0.167 | 1.51 | 0.84-2.71 |
Enough and easy to use | 33 (27.97) | |||
Consultation | ||||
No consultation | 20 (64.52) | <0.001 | 4.71 | 2.04-10.85 |
Not enough | 34 (35.79) | 0.213 | 1.44 | 0.81-2.57 |
Enough | 34 (27.87) | |||
Satisfaction with resident training | ||||
Dissatisfy | 46 (74.19) | <0.001 | 9.86 | 5.07-19.16 |
Satisfy | 42 (22.58) | |||
Having thought about discontinuing training | ||||
Yes | 66 (57.89) | <0.001 | 6.94 | 3.85-12.51 |
No | 22 (16.54) | |||
Job support at home | ||||
No support | 3 (42.86) | 0.769 | 1.26 | 0.27-5.77 |
Not enough | 11 (25.58) | 0.146 | 0.58 | 0.27-1.21 |
Enough | 74 (37.37) | |||
Job support at work | ||||
No support | 14 (70.00) | <0.001 | 12.25 | 3.92-38.24 |
Not enough | 62 (40.52) | <0.001 | 3.58 | 1.78-7.18 |
Enough | 12 (16.00) |
CI = confidence interval, OR = odds ratio, significant at P ≤0.0, risk of burnout are presented as number (%)
and job satisfaction among obstetrics and gynecology residents.19 Factors contributing to job satisfaction are influenced by various details. Unfortunately, our questionnaire did not explore the specific reasons behind residents’ dissatisfaction or desire to discontinue training. Further research is needed to investigate these causes for improvement in training programs.
Regarding other factors related to burnout, some previous studies have reported factors to be associated with an increased risk of burnout, such as younger age20- 22, female gender8,20, marital status21, having children22, high alcohol consumption8, lack of job support7,21, and high workload.7-9,22 Conversely, having sufficient work resources has been found to decrease the risk of burnout9.
TABLE 3. Multivariable logistic regression analysis for factors associated with burnout.
Associated factors Multivariable analysis OD 95%CI P value | |
Year of resident training 1st year 2nd year 0.32 3rd year 0.54 | 0.134-0.76 0.009 0.25-1.17 0.17 |
Sleeping time (hour) <5 6.88 5-6 3.68 7-8 | 1.90-24.93 0.003 1.40-9.68 0.008 |
Satisfaction with resident training Dissatisfy 8.38 Satisfy | 3.65-19.25 0.00 |
Thought of discontinuing training | |
Yes 3.112 | 1.57-6.18 0.001 |
No | |
CI = confidence interval, OR = odds ratio, significant at P ≤0.05 | |
However, our investigation yielded no significant association | measures to support residents. |
between these factors and burnout, which is consistent | In conclusion, the prevalence of burnout among |
with findings from several other studies that reported | Thai anesthesiology residents was high. Sleeping less than |
no association between burnout and female gender21,22 | 7 hours, experiencing dissatisfaction, and contemplating |
or marital status.8 Regarding workload, we did not find | discontinuation of training were identified associated |
a significantly increased risk of burnout, despite the | with a higher risk of burnout, while being a second-year |
association reported in many studies. One possible | resident lowered the risk. |
explanation could be the way the question was asked, which separated official work hours and shift hours per | ACKNOWLEDGMENTS |
month, rather than capturing all the working hours per | The authors gratefully acknowledge Asst. Prof. |
week. This might have made it challenging to accurately | Yodying Dangprapai, Ph.D. (Faculty of Medicine Siriraj |
estimate the exact workload per week and could have | Hospital, Mahidol University) for The Thai version of the |
influenced the results. | CBI-SS. We thank Assist. Prof Alisa Seangleulur (Faculty |
The present study has several limitations. Firstly, the | of Medicine, Thammasat Hospital) for the advice in data |
study relied on self-reported data, which introduces the | analysis, Ms. Sam Ormond, (international instructor, |
possibility of social desirability bias. Secondly, the sample | Clinical Research Center, Faculty of Medicine, Thammasat |
size was lower than the calculated sample size. This could | University) for a native-speaking editor review, and |
have affected the generalizability of the findings. The | importantly, residents who agreed to participate in this |
smaller sample size could also have limited the statistical | study. |
power to detect significant associations. Thirdly, the unclear wording of questions regarding working hours may adversely impact the result. Lastly, the study did not explore certain potential risk factors in depth, such as the quality of sleep or the specific reasons for dissatisfaction with the training program. Future research could be conducted to explore the factors contributing to burnout. This research would help in developing suitable support
The authors declare no conflict of interest
This study was supported by a grant from the Faculty of Medicine Thammasat Hospital, Thammasat University, Pathum Thani, Thailand.
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