ABSTRACT
Background
COVID-19 pandemic-related changes have increased mental health problems of essential workers. However, little is known about its impact on direct support professionals (DSPs) working at facilities for people with disabilities. This study examined the resilience and related factors, including psychological distress, burnout, depression, among DSPs working during the COVID-19 pandemic, including those in facility with cluster occurrences in Japan.
Methods
We conducted an online survey from May 18 to June 9, 2021, targeting staff at Social Welfare Corporation Shirane Gakuen, a cluster facility. An additional survey was conducted on May 24, 2021, through Yahoo! Crowd Sourcing, targeting individuals who worked at disability support facilities categorized as non-cluster facilities. We obtained 108 and 95 responses, respectively, including 76 and 16 DSPs. The following instruments were used: Maslach Burnout Inventory-General Survey, Bidimensional Resilience Scale, Kessler 6-item Psychological Distress Scale, and Patient Health Questionnaire-9. Multiple regression analysis was conducted to examine associations between resilience and related factors.
Results
The prevalence of severe psychological distress, burnout, and depression among DSPs within the COVID-19 cluster facility was 11.8%, 69.7%, and 29.0%, respectively. In the non-cluster facilities, the prevalence was 37.5%, 68.8%, and 62.5%, respectively. A positive association between acquired resilience factors and burnout was observed (coefficient = 2.35, 95% CI 0.38 to 4.31). However, no significant association was observed between acquired resilience factors and the interaction term of COVID-19 cluster occurrences at the workplace and burnout (coefficient = –2.03, 95% CI –6.97 to 2.90).
Conclusion
This study demonstrated that the acquired resilience factors of Japanese DSPs was linked with increased burnout during the COVID-19 pandemic, regardless of the COVID-19 cluster occurrences. As burnout intensifies, DSPs may cultivate acquired resilience, and programs enhancing these factors may help alleviate their burnout and promote well-being.
Keywords: Burnout, Psychological, internet use, pandemic, Patient Health Questionnaire, people with disability
The spread of COVID-19 and the subsequent pandemic had a negative impact on people’s mental health. In observational studies on the pandemic, associations between psychological distress and populations such as the general public,1 students,2 and health care workers3 have been reported. Undoubtedly, continuation of psychological distress associated with the pandemic negatively impacts mental health. Furthermore, it is important to find measures related to psychological burdens such as burnout in such crises in the future.
Direct support professionals (DSPs) work with people with intellectual, physical, and mental disabilities and with those having difficulty living independently in their daily lives. DSPs provide consultation and advice, vocational guidance, employment support, and work guidance to clients who stay at or visit disability welfare facilities to support them in living comfortably and independently. According to the 2020 national census, approximately 530,000 people are engaged in this field in Japan.4 Understanding the psychological burdens of DSPs who work closely with clients will contribute to ensure sustainability of the work style of DSPs and the associated independence of people with disabilities.
The association between DSP work environment and psychological burdens during the COVID-19 pandemic is essential in preparing for future outbreaks of infectious diseases. In addition, this association for DSPs working at disability support facilities has not been investigated sufficiently after the outbreak of the COVID-19 pandemic.
Studies have used the Kessler 6-item Psychological Distress Scale (K6) to assess psychological distress,5, 6 and the Patient Health Questionnaire-9 (PHQ-9) to measure depression.7 In a previous study, susceptibility to burnout was related to a high K6 score for registered dietitians who are essential workers8 and to a high PHQ-9 score for psychiatry residents.9 Although, burnout has been included in the International Classification of Diseases 11th Revision (ICD-11),10 studies on burnout risk of DSPs are limited. However, previous studies on similar essential workers during the COVID-19 pandemic reported a high burnout risk.11 Additionally, a report targeting nurses before the COVID-19 pandemic indicated that burnout predicted 11%, 15%, and 13% of the variance in anxiety, stress, and depression, respectively.12
Resilience has been highlighted as one of the key strengths in enabling recovery from stressful events, such as psychological burdens. Resilience consists of innate and acquired resilience factors.13, 14 Previous study on physicians have reported a negative association between resilience and burnout.15 Similar association have been reported for nurses.16 However, studies on the association of resilience with K6 and PHQ-9 scores, especially in essential workers, are scarce. One study targeting parents of children with cancer reported higher distress using K6 for those with low resilience.17 Furthermore, a study on the general population during the COVID-19 pandemic showed an association between low resilience and high PHQ-9 score.18
DSPs often have close physical contact with clients, and previous studies have reported that female healthcare workers with high exposure to COVID-19 tend to have higher scores of stress, depression, and anxiety symptoms.19 During the COVID-19 pandemic, individuals working in facility with cluster occurrences, may experience higher levels of psychological distress, burnout, and depression. Resilience may have a negative association with these issues. However, the resilience of DSP characteristics during COVID-19 pandemic is not well understood. By elucidating the associations of resilience with psychological distress, burnout and depression in DSPs, it will be possible to implement interventions and improvements aimed at enhancing resilience.
While resilience is generally regarded as a protective factor against stress and burnout, a recent finding suggests that resilience may also emerge as a consequence of experiencing psychological burdens, such as burnout.20 This bidirectional possibility highlights the need to further investigate the causal direction between resilience and psychological burdens. In the light of this perspective, the present study considers resilience as a development variable and investigates its association with psychological burdens. This approach may offer meaningful insights into the mechanisms underlying resilience among DSPs during the COVID-19 pandemic. Our study examined the association between resilience and its related factors, including psychological distress, burnout, and depression, among DSPs working at disability support facilities in Japan during the COVID-19 pandemic, some of which experienced cluster occurrences.
MATERIALS AND METHODS
Participants
The data was obtained from an online survey from the staff of the Social Welfare Corporation Shirane Gakuen COVID-19 cluster facility using Google Forms. In order to obtain data on related factors in other social welfare facilities during the COVID-19 pandemic, a total of 100 participants registered with Yahoo! Crowd Sourcing (Y Crowd) who reported working at a disability support facility were recruited through an online survey panel. Y Crowd is a crowd sourcing service launched by Yahoo Japan Corporation, Inc. (Tokyo, Japan) in 2013. The 111 participants of the COVID-19 cluster facility completed the online survey from May 18 to June 9, 2021, and the 100 participants from Y Crowd completed it on May 24, 2021. Responses from those with inconsistent answers to a question regarding the presence of COVID-19 clusters in Shirane Gakuen (three participants), and responses from facilities where COVID-19 clusters occurred in Y Crowd were excluded (five participants). Finally, regarding the DSP data, we analyzed data from 108 participants, including 76 DSPs who worked at the COVID-19 cluster facility and 16 DSPs from the Y crowd, totaling 92 DSPs.
In the COVID-19 cluster outbreak facility, one staff member and two clients contracted the COVID-19 infection in late August 2020, and 40 more people were infected on September 4, 2020, bringing the total number of infected people to 43. On September 8, 2020, new infections were confirmed in a female staff member in her 50s and a teenage client (total number = 49). On September 9, 2020, two more staff members in their 50s and a female staff member in her 20s were newly infected (total number = 51). On September 13, 2020, a male client and his family got newly infected (total number = 62). On September 17, 2020, clients and staff members were newly infected (total number = 68). In addition, the COVID-19 cluster outbreak facility received intervention from the health center and Yokohama City on September 1, 2020 to strengthen infection control.
Maslach Burnout Inventory-General Survey (MBI-GS)
The Japanese version of the Maslach Burnout Inventory–General Survey (MBI-GS) comprising 16 questions on a 7-point Likert scale (Mind Garden, Inc. Menlo Park, CA) was used to assess burnout. The response options range from 0 (never) to 6 (daily). The validity of the scale has been confirmed and permission was obtained for its use in this study.21 Burnout was defined based on Kijima et al.’s previous study on Japanese people: a score of ≥ 16 on the MBI-GS emotional exhaustion (MBI-EX) subscale and/or ≥ 11 on cynicism (MBI-CY) subscale.22 In addition, higher scores on the professional efficacy (MBI-PE) subscale indicate higher professional efficacy, and the scores were divided into two groups: ≥ 24 points and ≤ 23 points.
Kessler 6-item Psychological Distress Scale (K6)
The Kessler Psychological Distress Scale (K6) is a 4-point Likert scale with six questions, with answers ranging from 0 (none) to 4 (always). The higher the total score, the higher the psychological distress. The reliability and validity of K6 have been confirmed in previous studies; the Japanese version was used in this study.5, 6 The cut-off points for psychological distress were identified as low (0–4 points), moderate (5–12 points), and severe (13–24 points) based on previous studies.23
Patient Health Questionnaire-9 (PHQ-9)
The Japanese version of the Patient Health Questionnaire-9 (PHQ-9) comprising nine questions on a 4-point Likert scale was used to assess depression. The responses are composed of rarely (0) to almost every day (3). The validity of this scale has been confirmed and permission was obtained for its use in this study.7 Based on Muramatsu et al.’s study, the cut-off value for depression was defined as a score of ≥ 10.
Bidimensional Resilience Scale (BRS)
The Bidimensional Resilience Scale (BRS) consists of innate and acquired resilience factors. Its reliability and validity have been confirmed.13, 14 The questionnaire consists of 21 items, and responses are based on a 5-point scale ranging from not applicable at all (1) to strongly applicable (5). Higher scores indicate greater resilience. This scale can be used freely by citing the source.
Demographic variables
We collected data on the following demographic variables: gender, age, education history, occupation, residence status, prefecture of residence, marital status, medical history, presence or absence of COVID-19 cluster occurrence at the workplace, smoking status, drinking status, walking time per day, presence or absence of regular annual vaccinations, annual income, and years of experience in the workplace. From May 18 to June 9, 2021, 10 areas in Japan—Hokkaido, Tokyo Metropolis, Aichi, Kyoto, Osaka, Hyogo, Okayama, Hiroshima, Fukuoka, and Okinawa—were identified as areas under the state of emergency declaration.
Statistical analysis
Continuous and categorical variables were expressed as medians and percentages (%), respectively. Statistical analysis was performed using Stata software version 15 (Stata Corp, College Station, TX), and the significance level was set at P < 0.05. Differences between DSPs at the COVID-19 cluster outbreak facility and those from non-cluster facilities were tested using the Mann-Whitney U test for continuous variables and χ2 test for categorical variables. Since the distribution of innate resilience factors was not normal (as assessed by Skewness/Kurtosis test), the variable was dichotomized at the median value (40) and used as a categorical variable: “low”(≤ 40) and “high”(> 40).The crude (unadjusted) association between acquired resilience factors and related variables were examined using linear regression across all DSPs, while multiple regression analysis was performed to examine the association of acquired resilience factors with psychological distress, depression and burnout. The confounding factors were adjusted for age (continuous variable), sex (men, women), annual income (less than 4 million yen, 4 million yen or more), K6 (low and moderate, severe), depression (no depression, depression), and burnout (no, yes), COVID-19 cluster occurrence at workplace (no, yes), and innate resilience factors (low, high). In the sub-analysis, we examined the interaction of COVID-19 cluster occurrence in a model that included interaction term of COVID-19 cluster occurrence at the workplace with burnout.
Ethical considerations
This study was conducted in accordance with the Declaration of Helsinki, and its protocol was approved by the ethics committee of Kagoshima University on 2 March 2021 (approval number 200301). All participants were provided with written information about the research and consent was obtained from those who agreed to participate in the study.
RESULTS
The data for this study were obtained from 76 DSPs working at the Shirane Gakuen COVID-19 cluster outbreak facility and 16 DSPs registered with Y Crowd from facilities without cluster outbreaks. The DSPs from Shirane Gakuen were categorized as the COVID-19 cluster facility, while the 16 DSPs registered with Y Crowd were categorized as the non-cluster facilities. The characteristics of all DSPs and the 76 DSPs in the COVID-19 cluster facility, as well as the 16 DSPs in the non-cluster facilities, are summarized in Table 1.
Table 1. Characteristics of DSPs working at facilities for people with disabilities.
| COVID-19 | ||||||
| Overall (n = 92) |
Cluster facility (n = 76) |
Non-cluster facilities (n =16) |
||||
| N (%) | ||||||
| Sex, women† | 50 | (54.4) | 40 | (52.6) | 10 | (62.5) |
| Years of education, < 16 years† | 41 | (44.6) | 33 | (43.4) | 8 | (50.0) |
| Living alone† | 22 | (23.9) | 16 | (21.1) | 6 | (37.5) |
| Living in emergency declaration area†** | 13 | (14.1) | 2 | (2.6) | 11 | (68.8) |
| Marital status, single† | 39 | (42.4) | 34 | (44.7) | 5 | (31.3) |
| Past medical history† | 21 | (22.8) | 16 | (21.1) | 5 | (31.3) |
| Current smoking† | 22 | (23.9) | 21 | (27.6) | 1 | (6.25) |
| Current drinking† | 56 | (60.9) | 46 | (60.5) | 10 | (62.5) |
| Walking time, less than 30 minutes/day† | 18 | (19.6) | 15 | (19.7) | 3 | (18.8) |
| Regular annual vaccination†* | 68 | (73.9) | 60 | (79.0) | 8 | (50.0) |
| High income, 4 million yen or more† | 45 | (48.9) | 39 | (51.3) | 6 | (37.5) |
| Burnout† | 64 | (69.6) | 53 | (69.7) | 11 | (68.8) |
| K6†* | ||||||
| Low | 25 | (27.2) | 24 | (31.6) | 1 | (6.25) |
| Moderate | 52 | (56.5) | 43 | (56.6) | 9 | (56.3) |
| Severe | 15 | (16.3) | 9 | (11.8) | 6 | (37.5) |
| PHQ-9* | ||||||
| Depression | 32 | (34.8) | 22 | (29.0) | 10 | (62.5) |
| Median (IQR) | ||||||
| Age, years ‡** | 34 | 28–43 | 33.5 | 28–40.5 | 39.5 | 32–46 |
| Years of experience in the workplace‡ | 6 | 4–9 | 6.5 | 4–9.5 | 5.5 | 3–8.5 |
| BRS | ||||||
| Innate resilience factors‡ | 40 | 35.5–43.5 | 40 | 35.5–43 | 38.5 | 35–44.5 |
| Acquired resilience factors‡ | 32 | 29–36 | 32 | 30–36 | 29 | 25–35 |
*P < 0.05, **P < 0.01. †χ2 test. ‡Mann-Whitney U test.
Overall, 50 DSPs (54.4%) were women, and the median age of DSPs was 34years (IQR: 28–43). Burnout was observed in 64 DSPs (69.6%), a moderate on K6 scale was found in 52 DSPs (56.5%), and depression was present in 32 DSPs (34.8%). The median score for innate resilience factors was 40 (IQR: 35.5–43.5), while the median score for acquired resilience factors was 32 (IQR: 29–36). The comparison of the characteristics between the COVID-19 cluster facility and non-cluster facilities is also shown in table 1. Of the 76 DSPs at the COVID-19 cluster facility, 52.6% were women. Regarding the characteristics of this facility, the median age was relatively young at 33.5 years, 79% received regular annual vaccinations, 56.6% as moderate on the K6 scale; these percentages were higher than those of the non-cluster facilities. However, proportions of living in an emergency declaration area (2.6%), 29% as depression on the PHQ-9 scale were lower in the COVID-19 cluster facility.
The crude (unadjusted) association between acquired resilience factors and related factors in DSPs can be seen in Table 2. Acquired resilience factors had a significant positive association with innate resilience factors (coefficient = 4.13, 95% CI: 2.92 to 5.35), and a significant negative association with both annual income (–2.36, –4.17 to –0.54) and depression (–2.02, –3.94 to –0.09).
Table 2. Association between acquired resilience factors and related factors using regression correlation coefficient in DSPs.
| Acquired resilience factors (n = 92) | ||
| Crude, coefficient | 95% CI | |
| Sex, women | 0.49 | (–1.39, 2.38) |
| Years of education, < 16 years | –0.74 | (–2.63, 1.14) |
| Living alone | –1.61 | (–3.79, 0.57) |
| Living in emergency declaration area | –0.90 | (–3.60, 1.79) |
| Marital status, single | 0.52 | (–1.38, 2.42) |
| Past medical history | –2.01 | (–4.21, 0.19) |
| Current smoking | 1.86 | (–0.31, 4.02) |
| Current drinking | 0.24 | (–1.68, 2.17) |
| Walking time, less than 30 minutes/day | 1.06 | (–1.30, 3.42) |
| Regular annual vaccination | –0.02 | (–2.16, 2.12) |
| Annual income, 4 million yen or more* | –2.36 | (–4.17, –0.54) |
| Burnout | 1.43 | (–0.59, 3.45) |
| K6 | ||
| Severe | –0.93 | (–3.47–1.60) |
| PHQ-9 | ||
| Depression* | –2.02 | (–3.94, –0.09) |
| BRS | ||
| Innate resilience factors, high** | 4.13 | (2.92, 5.35) |
| Age, years | 0.02 | (–0.08, 0.11) |
| Years of experience in the workplace | 0.05 | (–0.18, 0.27) |
*P < 0.05, **P < 0.01. Crude: unadjusted. CI, confidence interval.
Multiple regression analysis demonstrated a significant negative association between acquired resilience factors and annual income (coefficient = –2.29, 95% CI: –4.28 to –0.30) (Table 3). Additionally, acquired resilience factors was significantly positively associated with burnout (coefficient = 2.35, 95% CI: 0.38 to 4.31) as well as with innate resilience factors (2.89, 1.15 to 4.63). In a similar analysis that included the interaction term for COVID-19 cluster occurrence at the workplace with burnout, acquired resilience factors had significant negative association with annual income (coefficient = –2.16, 95% CI: –4.18 to –0.15), and a significant positive association with innate resilience factors (2.80, 1.04 to 4.55) (Table 4). However, no significant association was observed with this interaction term (coefficient = –2.03, 95% CI: –6.97 to 2.90).
Table 3. Association between acquired resilience factors and related factors in DSPs: a multiple regression analysis.
| Acquired resilience factors (n = 92) | ||
| Coefficient | 95% CI | |
| Age, years | 0.06 | (–0.03, 0.15) |
| Sex, women | –0.35 | (–2.26, 1.56) |
| Annual income, 4 million yen or more* | –2.29 | (–4.28 -0.30) |
| K6, severe | 1.26 | (–1.63, 4.15) |
| Depression | –1.67 | (–3.98, 0.63) |
| Burnout* | 2.35 | (0.38, 4.31) |
| COVID-19 cluster occurrences at workplace | 2.34 | (–0.06, 4.74) |
| Innate resilience factors** | 2.89 | (1.15, 4.63) |
*P < 0.05, **P < 0.01. Multiple regression analysis adjusted for age, sex, annual income, K6, depression, burnout, COVID-19 cluster occurrences at workplace, and innate resilience factors. CI, confidence interval.
Table 4. Association between acquired resilience factors and related factors, including the interaction with COVID-19 cluster occurrences at the workplace in DSPs: a multiple regression analysis.
| Acquired resilience factors (n = 92) | ||
| Coefficient | 95% CI | |
| Age, years | 0.06 | (–0.04, 0.15) |
| Sex, women | –0.24 | (–2.17, 1.70) |
| Annual income, 4 million yen or more* | –2.16 | (–4.18, –0.15) |
| K6, severe | 1.09 | (–1.84, 4.02) |
| Depression | –1.55 | (–3.88, 0.77) |
| Burnout | 4.00 | (–0.46, 8.46) |
| COVID-19 cluster occurrence at workplace | 3.74 | (–0.41, 7.89) |
| Innate resilience factors** | 2.80 | (1.04, 4.55) |
| Interaction with COVID-19 cluster occurrences at workplace | ||
| Burnout | –2.03 | (–6.97, 2.90) |
*P < 0.05, **P < 0.01. Multiple regression analysis adjusted for age, sex, annual income, K6, depression, burnout, COVID-19 cluster occurrences at workplace, innate resilience factors, and the interaction between COVID-19 cluster occurrences at the workplace and burnout. CI, confidence interval.
DISCUSSION
This study used data from DSPs during the COVID-19 pandemic, indicating that higher acquired resilience factors in DSPs were associated with higher levels of burnout. However, acquired resilience factors did not show a significant association with the interaction term between COVID-19 cluster occurrence at the workplace and burnout. These findings suggest that the development of acquired resilience factors to cope with burnout, along with interventions to enhance acquired resilience factors in DSPs, may contribute to improved mental health, even in challenging contexts such as the COVID-19 pandemic.
Evaluation of association between resilience and depression during the COVID-19 pandemic has been limited, particularly for DSPs. However, among essential workers, a negative association between PHQ-9 scores and resilience has been reported in nurses.24 The current study found no statistically significant negative association between acquired resilience and depression. Although a crude regression model indicated a significant inverse correlation, this association was attenuated and no longer significant after adjusting for confounding variables. A meta-analysis of resilience training programs targeting as essential workers has reported an association between increased resilience and reduced depression.25 A similar trend of negative association between acquired resilience and depression was observed in the present study; however, the association did not reach statistical significance, possibly due to the small sample size.
There are limited reports on the association between the occurrence of COVID-19 clusters and resilience in DSPs. Systematic reviews have reported resilience as a preventive factor against negative outcomes in healthcare workers,26 and another systematic review reported that burnout among healthcare workers during the COVID-19 pandemic ranged from 14.7–76%.27 In the present study, the proportion of burnout was 69.7% in the COVID-19 cluster facility, 68.8% in the non-cluster facilities among DSPs, indicating that the DSPs had a relatively high rate of burnout. Additionally, since there was no significant difference in the proportion of burnout between the COVID-19 cluster and non-cluster facilities, the occurrence of COVID-19 cluster in the workplace and its associated impact on work may not be very high.
In the study DSPs, burnout was positively associated with acquired resilience factors. Previous studies conducted on essential workers, such as nurses during the COVID-19 pandemic, have reported a positive association between burnout and acquired resilience factors, although not statistically significant.20, 28 Acquired resilience factors is a form of resilience that can be developed postnatally, and the positive association may be observed because resilience acquisition becomes necessary in situations leading to burnout. Furthermore, the interaction term between burnout and the occurrence of COVID-19 clusters was not significantly associated with acquired resilience factors. This may be attributable to the consistently high levels of burnout across the study population, irrespective of COVID-19 cluster occurrence, which could have masked any potential associations.
In this study, acquired resilience factors among DSPs showed a trend of being negatively associated with depression and positively associated with burnout. Previous reviews on burnout have identified four levels of burnout severity, ranging from mild to extreme, with chronic depression being a characteristic of extreme burnout.29 Given these classifications, it is possible that acquired resilience factors may help mitigate the progression toward more severe forms of burnout, including conditions such as chronic depression. On the other hand, these observed trends may reflect a potential risk for an increased depression rates among DSPs during the COVID-19 pandemic.
In DSPs, although the point estimate of the adjusted coefficient suggested a positive association between acquired resilience factors and psychological distress, the association was not statistically significant. Similar to the association between acquired resilience factors and burnout, it is possible that higher levels of psychological distress lead to increased acquired resilience factors. In the future, it will be necessary to confirm whether the positive association in this cross-sectional study between acquired resilience factors and psychological distress becomes negative in longitudinal studies.
In this study, annual income was significantly negatively associated with acquired resilience factors. Although very few previous studies have reported a link between income and acquired resilience, it is plausible that low income may be perceived as a form of adversity, thereby increasing the necessity to develop acquired resilience factors; however, the possibility of reverse causation cannot be ruled out. In addition, innate resilience factors was found to be significantly and positively associated with acquired resilience factors. This positive relationship between the two constructs was previously reported during scale development,13 and our findings support this earlier result. Using innate resilience factors, as indicators may be a useful approach for screening DSPs, in order to assess the balance of acquired resilience development and the extent to which individuals are situated in contexts that allow them to utilize their innate resilience factors.
This study has some limitations. The sample size was limited, with a notable imbalance between the cluster facility (n = 76) and the non-cluster facilities (n = 16). This disparity may have contributed to instability in effect estimation, such as increased standard errors, and may have elevated the risk of β error due to insufficient statistical power. In addition, the small number of participants rases concerns not only about the potential for α error but also about the appropriateness of applying multiple regression analysis. These sample size constraints may limit the precision of parameter estimates and the stability of the statistical models, requiring cautions interpretation of the study findings.
Furthermore, since this was a cross-sectional study, the possibility of reverse causation cannot be ruled out. To address this, we conducted preliminary research to assess consistency with prior studies. Given the limited amount of literature on DSPs, we also considered the perspectives of former healthcare and welfare essential workers. Although adjustments were made for known confounding factors, the influence of unmeasured confounders cannot be excluded. Additionally, only DSPs who consented to participate were included in this study, which may have resulted in lower observed levels of psychological distress, burnout, and depression compare to the broader DSP population. Consequently, the findings may slightly underestimate the actual prevalence of these conditions.
In conclusion, our study found that acquired resilience factors among DSPs during COVID-19 pandemic was positively associated with burnout. However, the interaction term between COVID-19 cluster occurrence at the workplace and burnout was not significantly associated with acquired resilience factors. These results suggest that acquired resilience factors during the COVID-19 pandemic may have developed as a means of coping with intensified burnout, regardless of the presence or absence of COVID-19 clusters. Workplace managers may be required to implement interventions aimed at enhancing the acquired resilience factors of DSPs and to systematically evaluate the effectiveness of these measures.
Acknowledgments
Acknowledgments: We would like to thank Michihiro Sakata for their contribution to the research by providing oversight and ensuring smooth progress.
This study was not supported by any specific grant from any public, commercial, or not-for-profit sectors.
Footnotes
The authors declare no conflict of interest.
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