Abstract
Background
This study’s aim was to reveal the burnout, depression, anxiety, stress, and hopelessness levels of HCWs in the pandemic period.
Methods
The cross-sectional study was carried out with 1015 HCWs, between September 1 and October 1, 2021, in Turkey. Maslach Burnout Inventory, Beck Hopelessness Scale, and Depression Anxiety Stress Scale-21 (DASS-21) were used.
Results
Of the HCWs, 56.7% had moderate/high emotional exhaustion (EE), 35.8% had moderate/high depersonalization (D), 58.0% had low personal accomplishment (PA), 34.9% had high depression, 31.9% had high anxiety, 15.4% had high stress, and 33.3% had moderate/severe hopelessness. Risk factors for EE were working in a public hospital, increased workload intensity, decreased income, and difficulty in procuring personal protective equipment (PPE). Risk factors for D were having a male gender, being under the age of 40, being a physician, working in a public hospital, and difficulty in procuring PPE. Risk factors for depression were being under the age of 40, having contact with COVID-19 patients, and decreased income. Risk factors for anxiety were having a female gender, being under the age of 40, working in a public hospital, having a chronic disease, having contact with COVID-19 patients, and having individuals at high risk for COVID-19 at home. Risk factors for hopelessness were having a female gender, being a physician, and increased income.
Conclusion
The negative effects of the pandemic, which has been going on for over a year and a half, on the mental health of HCWs were high.
Keywords: burnout, depression, hopelessness, healthcare workers, COVID-19
-
Highlights
What Do We Already Know About This Topic?
HCWs are at high risk of physical and mental problems due to their contact with COVID-19 patients. Many studies examining the psychological impact of COVID-19 on HCWs and the general population have reported high levels of problems in these dimensions.
This Research’s Contribution to the Field
Our study revealed increasing mental problems in HCWs in a period when we leave behind about one and half year of the pandemic. Gender, being under the age of 40, being a physician, working in a public hospital, having a chronic disease, having contact with COVID-19 patients, increased workload intensity, having individuals at high risk for COVID-19 at home, income, and difficulty in PPE were the factors affecting the mental state of HCWs negatively during the pandemic process.
This Research’s Implications Towards Theory, Practice, or Policy
The increase in cases with the emergence of new variants with each passing day continues to prolong the pandemic process. This means increased workload and worse mental state for HCWs. It may be recommended to follow up the mental status of HCWs and even to establish special follow-up centers for this purpose.
Introduction
The mental health impact of a major disaster has a wider and longer impact on people than physical injuries. 1 In particular, healthcare workers (HCWs) have been affected by many different infectious diseases and are considered to be among the occupational groups with the highest potential to be affected by future epidemics. 2 According to World Health Organization (WHO), HCWs are at high risk of physical and mental problems due to their contact with COVID-19 patients. 3
Many studies examining the psychological impact of COVID-19 on HCWs and the general population have reported high levels of problems in these dimensions. 4 The pooled prevalence was reported to be 33% for anxiety and 28% for depression in these studies. 4 In addition, studies conducted in China, Italy, Turkey, Spain, and Iran reported a higher prevalence among HCWs and the general population than these values. 4 Also a systematic review and meta-analysis determined the prevalence of burnout as 28%, the prevalence of anxiety as 30%, and the prevalence of depression as 24% in HCWs. 5 On the other hand, in a study comparing the hopelessness levels of HCWs and the general population during the pandemic process, it was reported that hopelessness levels were higher in HCWs than in non-HCWs. 6
Excessive stress and emotional load, long working hours, the risk of infecting themselves or their relatives, being separated from the family, lack of adequate protective equipment in the work environment, and lack of effective treatments can adversely affect the mental health of HCWs.7-9
It is thought that HCWs are among the occupational groups that are most affected psychologically in the COVID-19 pandemic, and it is important to take protective measures as soon as possible to prevent this situation. In the literature, no study was found in which the burnout, depression, anxiety, stress, and hopelessness levels of HCWs were evaluated simultaneously. Thus, this study aimed to reveal these mental health levels of HCWs and the affecting factors at a time when one and a half years of the pandemic was left behind.
Methods
Study Design, Period, and Sample Size
The cross-sectional study was carried out between September 1 and October 1, 2021, in Turkey. The minimum sample size was calculated as 600 using the formula n = [DEFF*Np(1−p)]/[(d2/Z21-α/2*(N−1) + p*(1−p)] (N = 1.033.767, margin of error d = 4%, confidence interval = 95%, P = 50%) in the OpenEpi (Version 3) program. The study was carried out to cover the whole of Turkey. The number of HCWs to be included in the sample from each region was determined, and in this way, HCWs were included in the sample. Since state hospitals in Turkey serve as pandemic hospitals where COVID-19 patients are treated, the health institution was evaluated in 3 tiers (primary healthcare, public hospital, and university hospital).
Data collection tools were delivered online with Google Forms through the WhatsApp program. A total of 1064 HCWs out of nearly 5000 HCWs (the estimation of response rate was %21.3) in WhatsApp groups agreed to fill out the form, but people with psychiatric illness and who reported receiving treatment for this reason (n=49) were excluded. Ultimately, the study was conducted with 1015 HCWs.
Data Collection Tools
A Socio-demographic Form, Maslach Burnout Inventory (MBI), Depression Anxiety Stress Scale-21 (DASS-21), and Beck Hopelessness Scale (BHS) were used to obtain the study data.
The Socio-demographic Form had 15 questions about age, place of the residence, gender, marital status, number of children, occupation, total working time, health institution, and the status of HCWs in the pandemic process.
Maslach Burnout Inventory was used to determine the burnout levels of the participants. It consists of 22 items and has sub-dimensions such as emotional exhaustion (EE), depersonalization (D), and personal accomplishment (PA). Scores were evaluated as ≤20 low, 21–27 moderate, and ≥28 high for EE; ≤8 low, 9–12 moderate, and ≥13 high for D; ≤23 low, 24–27 moderate, and ≥28 high for PA. 10 The Turkish validity of the scale was done by Ergin. 10
Depression Anxiety Stress Scale-21 is a four-point Likert-type scale in which depression, stress, and anxiety sub-dimensions are evaluated (seven questions for each). Normal scores are less than 9 for depression, less than 7 for anxiety, and less than 14 for stress. 11 The Turkish validity of the scale was done by Yılmaz et al. 12
Beck Hopelessness Scale was used to examine the hopelessness levels of the participants. It contains 20 questions and questions are scored as zero or one. 13 One point is given for each “no” answer in questions 1, 3, 5, 6, 8, 10, 13, 15, and 19 of the scale, while one point is given for each “yes” answer in the other questions. According to the scores they get, individuals are considered as 0–3 Minimal, 4–8 Mild, 9–14 Moderate, >15 severe hopelessness level. The reliability coefficient was found between .72 and .78. 13 The Turkish validity of the scale was done by Durak et al. 13
Ethics Statement
Informed consent was obtained from the individuals who agreed to participate in the study. Ethical approval was obtained from the Non-Invasive Ethics Committee of Sivas Cumhuriyet University (Decision no: 2020–12/19).
Statistical Analysis
The data obtained from the study was examined with SPSS 22.0. Descriptive statistics (median, interquartile range, percentage) and the Cronbach’s alpha values were calculated. The Cronbach’s alpha values were found as .912 for EE, .807 for D, .764 for PA, .924 for depression, .863 for anxiety, .901 for stress, and .857 for BHS. A limit of 11 years was used when categorizing participants’ total working time, as the median value for working years were 11. The normality of the data was analyzed by Kolmogorov-Simirnov test [The test statistics results were .060 for EE score (P < .001), .109 for D score (P < .001), .061 for PA score (P < .001), .104 for depression score (P < .001), .127 for anxiety score (P < .001), .100 for stress score (P < .001), and .095 for hopelessness score (P < .001)]. Mann–Whitney U test, Kruskal–Wallis test (post hoc Mann–Whitney U test) and chi-square test were used. Binary logistic regression analysis was performed (MBI subscale scores were accepted as two categories as low and moderate/high, BHS score was accepted as two categories as minimal/mild and moderate/severe). To determine the relationship between mental health levels and sociodemographic and pandemic process characteristics [contact with COVID-19 patients, workload intensity, presence of individuals at high risk for COVID-19 at home, change in income, and difficulty in procuring personal protective equipment (PPE)] of the participants, characteristics with statistically significant differences in Table 3 were included in the models separately for each mental state. For EE, age, occupation, presence of children, total working time, health institution, contact with COVID-19 patients, workload intensity, change in income, and procuring PPE were included in the model. For D, gender, age, occupation, presence of children, total working time, health institution, place of the residence, contact with COVID-19 patients, workload intensity, individuals at high risk for COVID-19 at home, change in income, and procuring PPE were included in the model. For PA, age, marital status, occupation, presence of children, total working time, and individuals at high risk for COVID-19 at home were included in the model. For depression, age, marital status, presence of children, total working time, contact with COVID-19 patients, change in income, and procuring PPE were included in the model. For anxiety, gender, age, marital status, occupation, presence of children, total working time, health institution, presence of chronic disease, contact with COVID-19 patients, workload intensity, and individuals at high risk for COVID-19 at home were included in the model. For stress, age, presence of children, and total working time were included in the model. For hopelessness, gender, occupation, and change in income were included in the model. Hosmer-Lemeshow test results (P>.05) revealed that the model-data fit was good enough. P < .05 was considered statistically significant.
Table 3.
Distribution of participants' socio-demographic and pandemic process characteristics according to Maslach Burnout Inventory and Depression, Anxiety, Stress Scale-21 subscales, and Beck Hopelessness Scale scores (n=1015).
| Emotional Exhaustion | Depersonalization | Personal Accomplishment | Depression | Anxiety | Stress | Hopelessness | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Characteristic | M (IQR) | Test, p | M (IQR) | Test, p | M (IQR) | Test, p | M (IQR) | Test, p | M (IQR) | Test, p | M (IQR) | Test, p | M (IQR) | Test, p |
| Total | 22 (13) | 6 (8) | 22 (8) | 6 (9) | 4 (5) | 7 (7) | 7 (6) | |||||||
| Gender | ||||||||||||||
| Female | 22 (13) | U=121 859.0, .157 | 5 (7) | U=159 250.5, <.001*** | 23 (7) | U=119 300.0, .050 | 6 (8) | U=123 436.0, .281 | 5 (6) | U=104 099.0, <.001*** | 7 (6) | U=123 667.0.0.304 | 7 (6) | U=110 825.0, <.001*** |
| Male | 22 (14) | 7 (8) | 22 (8) | 6 (9) | 3 (6) | 7 (7) | 6 (7) | |||||||
| Age (years) | ||||||||||||||
| <40 | 23 (12) | U=84 523.0, <.001*** | 7 (7) | U=79 625.5, <.001*** | 22 (8) | U=123 611.5, <.001*** | 7 (9) | U=80 332.0, <.001*** | 5 (6) | U=87 787.0, <.001*** | 7 (8) | U=90 241.0, .003** | 7 (6) | U=95 426.5, .091 |
| ≥40 | 20 (14.3) | 5 (7) | 24 (8) | 5 (7) | 3 (5) | 7 (6) | 6 (6) | |||||||
| Marital status | ||||||||||||||
| Single + Widow | 23 (11) | U=96 884.5, .200 | 7 (7) | U=94 690.0, .070 | 21 (8) | U=111 118.0, .032* | 7 (8.5) | U=86 953.0, <.001*** | 5 (6) | U=92 625.5, .021* | 7 (7) | U=95 696.0, .116 | 7 (6) | U=100 527.0.0.684 |
| Married | 22 (14) | 6 (8) | 23 (7) | 6 (8) | 4 (6) | 7 (7) | 7 (7) | |||||||
| Occupation | ||||||||||||||
| Physician 1 | 23 (12) | x 2 =13.969, .001** | 8 (7) | x 2 =119.073, <.001*** | 21 (7) | x 2 =34.135, <.001*** | 7 (8) | x 2 =4.166, .125 | 4 (6) | x 2 =16.832, <.001*** | 7 (7) | x 2 =6.035, .050 | 7 (7) | x 2 =7.653, .022* |
| Nurse 2 | 20 (14) | 4 (4.8) | 24 (6.8) | 6 (7) | 5 (6) | 7 (6.8) | 7 (5) | |||||||
| Other HCWs 3 | 22 (16) | 5 (7.3) | 23 (8) | 6 (10) | 5 (6) | 7 (8) | 6 (7) | |||||||
| Post hoc | 1>2 | 1>2,1>3,3>2 | 2>1,2>3 | 2>1,3>1 | 2>3 | |||||||||
| Number of children | ||||||||||||||
| None | 24 (13) | U=101 794.5, <.001*** | 7 (7) | U=103 560.5, <.001*** | 21 (7) | U=139 980.0, <.001*** | 7 (8) | U=98 132.0, <.001*** | 5 (6) | U=105 987.0, <.001*** | 8 (7) | U=109 193.0, .006** | 7 (6) | U=120 520.5, .785 |
| At least one | 21 (15) | 6 (8) | 23 (7) | 6 (8) | 4 (6) | 7 (7) | 7 (6) | |||||||
| Total working time (years) | ||||||||||||||
| <11 | 23 (12) | U=107 668.0, <.001*** | 7 (7) | U=102 493.0, <.001*** | 21 (7) | U=151 697.5, <.001*** | 7 (8) | U=104 531.0, <.001*** | 5 (6) | U=113 023.5, .003** | 8 (7) | U=111 670.5, .001** | 7 (6) | U=123 861.0, .557 |
| ≥11 | 21 (15) | 5 (8) | 24 (8) | 6 (7.5) | 4 (6) | 7 (6) | 6 (6) | |||||||
| Health institution | ||||||||||||||
| Primary healthcare 1 | 21 (14) | x 2 =16.754, <.001*** | 6 (9) | x 2 =17.571, <.001*** | 23 (8) | x 2 =5.429, .066 | 6 (9) | x 2 =3.025, .220 | 4 (5) | x 2 =18.448, <.001*** | 7 (7) | x 2 =3.784, .151 | 7 (6) | x 2 =1.473, .479 |
| Public hospital 2 | 24 (12) | 7 (8) | 23 (6) | 7 (8) | 5 (7) | 8 (8) | 6.5 (7.3) | |||||||
| University hospital 3 | 22 (12.3) | 7 (7) | 21 (7.3) | 6 (9) | 3 (5.3) | 7 (6.3) | 7 (7) | |||||||
| Post hoc | 2>1,2>3 | 2>1,3>1 | 2>3,2>1 | |||||||||||
| Place of the residence | ||||||||||||||
| Metropolitan 1 | 23 (14) | x 2 =3.853, .146 | 8 (8) | x 2 =21.129, <.001*** | 22 (8) | x 2 =4.407, .110 | 6 (9) | x 2 =2.793, .247 | 4 (6) | x 2 =4.380, .112 | 7 (7) | x 2 =4.663, .097 | 6 (7) | x 2 =.090, .956 |
| City 2 | 22 (12) | 6 (7) | 23 (7) | 7 (8) | 4 (6) | 7 (7) | 7 (7) | |||||||
| County 3 | 21 (15) | 5 (7) | 22.5 (8) | 6 (8) | 4 (5) | 7 (8) | 7 (6) | |||||||
| Post hoc | 1>3,1>2 | |||||||||||||
| Presence of chronic disease | ||||||||||||||
| No | 22 (13) | U=71 413.0, .437 | 6 (8) | U=74 800.0, .857 | 22 (8) | U=77 854.5, .297 | 6 (9) | U=69 270.0, .166 | 4 (6) | U=66 158.5, .023* | 7 (7) | U=70 401.0, .287 | 7 (6) | U=77 588.5, .332 |
| Yes | 23 (14) | 6 (8) | 22 (8) | 7 (9) | 5 (6) | 8 (8) | 6 (6) | |||||||
| Contact with COVID-19 patients | ||||||||||||||
| No | 20 (15) | U=61 164.5, .016* | 5 (9) | U=61 134.5, .015* | 22 (8) | U=67 970.5, .668 | 6 (7) | U=61 459.0, .020* | 3 (5) | U=59 471.5, .004** | 7 (7) | U=67 105.0, .495 | 7 (6) | U=73 111.0, .282 |
| Yes | 22 (12) | 6 (8) | 22 (7.8) | 6 (8) | 4 (6) | 7 (7) | 7 (6.8) | |||||||
| Workload intensity in the pandemic | ||||||||||||||
| Not changed 1 | 18 (14.8) | x 2 =16.965, <.001*** | 6 (7.8) | x 2 =10.832, .004** | 21.5 (6.8) | x 2 =.775, .679 | 6 (8) | x 2 =2.343, .310 | 3 (6) | x 2 =14.385, .001** | 7 (6) | x 2 =2.042, .360 | 6 (6) | x 2 =1.387, .500 |
| Increased 2 | 23 (13) | 6 (8) | 22 (8) | 6 (9) | 4 (6) | 7 (7) | 7 (6) | |||||||
| Decreased3 | 20 (15) | 7 (8) | 22 (7) | 6 (7) | 3 (5) | 7 (6) | 6 (6) | |||||||
| Post hoc | 2>1 | 3>1, 3>2 | 2>3,2>1 | |||||||||||
| Presence of individuals at high risk for COVID-19 at home | ||||||||||||||
| No | 21 (14) | U=118 627.0, .060 | 6 (8.5) | U=136 605.0, .046* | 22 (8) | U=115 584.0, .011* | 6 (8) | U=126 175.0, .800 | 4 (6) | U=114 647.0, .006** | 7 (7) | U=119 111.5, .076 | 7 (6) | U=134 145.0,0.142 |
| Yes | 22.5 (13) | 6 (7) | 23 (7) | 6.5 (9) | 5 (6) | 7 (8) | 6 (7) | |||||||
| Change in income during the pandemic | ||||||||||||||
| Not changed 1 | 21 (14) | x2=10.799, .005** | 6 (8) | x2=8.093, .017* | 22 (7) | x2=3.746, .154 | 6 (9) | x2=6.842, .033* | 4 (6) | x2=2.035, .361 | 7 (7) | x2=4.839, .089 | 7 (6) | x2=9.976, .007** |
| Increased2 | 20 (11) | 6 (8) | 22 (8) | 6 (7) | 5 (6) | 7 (7) | 7 (6) | |||||||
| Decreased 3 | 24 (14) | 7 (8) | 22 (8) | 7 (9) | 5 (6) | 8 (7) | 6 (7) | |||||||
| Post hoc | 3>1 | 3>1 | 3>1 | 2>3 | ||||||||||
| Difficulty in procuring personal protective equipment | ||||||||||||||
| No | 21 (14) | U=74 138.5, <.001*** | 6 (7) | U=73 699.0, <.001*** | 23 (7.3) | U=105 934.5, .064 | 6 (9) | U=89 448.0, .028* | 4 (6) | U=92 578.0, .152 | 7 (7) | U=91 345.0, .083 | 7 (6) | U=104 525.5, .132 |
| Yes | 25 (12.5) | 9 (8) | 22 (7) | 7 (8) | 5 (6) | 8 (7.5) | 6 (7) | |||||||
n Number of participants, M Median, IQR Interquartile range, HCW Healthcare worker, x 2 Kruskal–Wallis H test, U Mann–Whitney U test, *P<.05, **P<.01, ***P<.001.
Results
Socio-Demographic and Pandemic Process Characteristics of the Participants
Of the 1015 HCWs participating in our study, 533 (52.5%) were male, 737 (72.6%) were under 40 years old, 738 (72.7%) were married, and 569 (56.1%) were physicians (Table 1). 626 (61.7%) had at least one child, 574 (56.6%) worked less than 11 years, 571 (56.3%) worked in primary healthcare, 448 (44.1%) lived in the city, 838 of them (82.6%) did not have any chronic disease. 83.9% had contact with COVID-19 patients. The workload intensity of 82% of HCWs had increased and income status of 49.6% of them had not change in the pandemic. 74.3% had no difficulty in procuring PPE (Table 1).
Table 1.
Socio-demographic and pandemic process characteristics of the participants (n = 1015).
| Socio-Demographic Characteristics | n (%) | |
| Gender | Female | 482 (47.5) |
| Male | 533 (52.5) | |
| Age (years) | <40 | 737 (72.6) |
| ≥40 | 278 (27.4) | |
| Marital status | Single + Widow | 277 (27.3) |
| Married | 738 (72.7) | |
| Occupation | Physician | 569 (56.1) |
| Nurse | 252 (24.8) | |
| Other HCWs | 194 (19.1) | |
| Health officer | 63 (32.5) | |
| Emergency medical technician | 49 (25.3) | |
| Paramedic | 35 (18.0) | |
| Lab technician | 29 (14.9) | |
| Physiotherapist | 18 (9.3) | |
| Region | Marmara | 171 (16.8) |
| Centre Anatolia | 170 (16.7) | |
| Aegean | 157 (15.5) | |
| Mediterranean | 149 (14.7) | |
| Black Sea | 141 (13.9) | |
| Eastern Anatolia | 128 (12.6) | |
| South eastern Anatolia | 99 (9.8) | |
| Number of children | None | 389 (38.3) |
| At least one | 626 (61.7) | |
| Total working time (years) | <11 | 574 (56.6) |
| ≥11 | 441 (43.4) | |
| Health institution | Primary healthcare | 571 (56.3) |
| Public hospital | 238 (23.4) | |
| University hospital | 206 (20.3) | |
| Place of the residence | Metropolitan | 335 (33.0) |
| City | 448 (44.1) | |
| County | 232 (22.9) | |
| Presence of chronic disease | No | 838 (82.6) |
| Yes | 177 (17.4) | |
| Pandemic process characteristics | n (%) | |
| Contact with COVID-19 patients | No | 163 (16.1) |
| Yes | 852 (83.9) | |
| Workload intensity in the pandemic | Not changed | 84 (8.3) |
| Increased | 832 (82.0) | |
| Decreased | 99 (9.8) | |
| Presence of individuals at high risk for COVID-19 at home | No | 561 (55.3) |
| Yes | 454 (44.7) | |
| Change in income during the pandemic | Not changed | 503 (49.6) |
| Increased | 217 (21.4) | |
| Decreased | 295 (29.1) | |
| Difficulty in procuring personal protective equipment | No | 754 (74.3) |
| Yes | 261 (25.7) |
n Number of participants
Prevalence of Burnout, Depression, Anxiety, Stress, and Hopelessness Among HCWs
Table 2 shows the prevalence of burnout, depression, anxiety, stress, and hopelessness among HCWs. Of the HCWs, 56.7% had moderate/high EE, 35.8% had moderate/high D, 58.0% had low PA, 34.9% had high depression, 31.9% had high anxiety, 15.4% had high stress and 33.3% had moderate/severe hopelessness. The proportion of high EE was higher in physicians (30.4%) than in nurses (23.4%) and other HCWs (29.4%) (P = .007). The proportion of high D was higher in physicians (21.1%) than in nurses (5.2%) and other HCWs (12.4%) (P < .001). The proportion of low PA was higher in physicians (63.6%) than in nurses (47.2%) and other HCWs (55.7%) (P < .001). The proportion of high depression was lower in nurses (27.0%) than in physicians (37.1%) and other HCWs (38.7%) (P = .009). The proportion of high anxiety was lower in physicians (27.9%) than in nurses (38.1%) and other HCWs (35.6%) (P = .008). The proportion of high stress was higher in other HCWs (20.1%) than in physicians (16.0%) and nurses (10.3%) (P = .015). The proportion of moderate/severe hopelessness was lower in other HCWs (25.8%) than in physicians (34.3%) and nurses (36.9%) (P = .036) (Table 2).
Table 2.
Prevalence of burnout, depression, anxiety, stress, and hopelessness among health care workers.
| Physicians (n= 569) | Nurses (n=252) | Other Healthcare Workers (n=194) | Total (n=1015) | ||
|---|---|---|---|---|---|
| Emotional Exhaustion, n (%) | Low | 220 (38.7) | 131 (52.0) | 89 (45.9) | 440 (43.3) |
| Moderate | 176 (30.9) | 62 (24.6) | 48 (24.7) | 286 (28.2) | |
| High | 173 (30.4) | 59 (23.4) | 57 (29.4) | 289 (28.5) | |
| x2, p | 14.056, .007** | ||||
| Depersonalization, n (%) | Low | 308 (54.1) | 209 (82.9) | 135 (69.6) | 652 (64.2) |
| Moderate | 141 (24.8) | 30 (11.9) | 35 (18.0) | 206 (20.3) | |
| High | 120 (21.1) | 13 (5.2) | 24 (12.4) | 157 (15.5) | |
| x2, p | 68.640, <.001*** | ||||
| Personal Accomplishment, n (%) | Low | 362 (63.6) | 119 (47.2) | 108 (55.7) | 589 (58.0) |
| Moderate | 130 (22.8) | 77 (30.6) | 49 (25.3) | 256 (25.2) | |
| High | 77 (13.5) | 56 (22.2) | 37 (19.1) | 170 (16.7) | |
| x2, p | 21.085, <.001*** | ||||
| Depression, n (%) | Normal | 358 (62.9) | 184 (73.0) | 119 (61.3) | 661 (65.1) |
| High | 211 (37.1) | 68 (27.0) | 75 (38.7) | 354 (34.9) | |
| x2, p | 9.353, .009** | ||||
| Anxiety, n (%) | Normal | 410 (72.1) | 156 (61.9) | 125 (64.4) | 691 (68.1) |
| High | 159 (27.9) | 96 (38.1) | 69 (35.6) | 324 (31.9) | |
| x2, p | 9.749, .008** | ||||
| Stress, n (%) | Normal | 478 (84.0) | 226 (89.7) | 155 (79.9) | 859 (84.6) |
| High | 91 (16.0) | 26 (10.3) | 39 (20.1) | 156 (15.4) | |
| x2, p | 8.457, .015* | ||||
| Hopelessness, n (%) | Minimal/mild | 374 (65.7) | 159 (63.1) | 144 (74.2) | 677 (66.7) |
| Moderate/severe | 195 (34.3) | 93 (36.9) | 50 (25.8) | 338 (33.3) | |
| x2, p | 6.664, .036* | ||||
n Number of participants, x 2 Chi-square test, *P < .05, **P < .01, ***P < .001.
Distribution of Participants' Socio-Demographic And Pandemic Process Characteristics According to MBI Subscales Scores
The EE score was higher in those younger than 40 years of age (P < .001), physicians (P = .001) (compared to nurses), who have no children (P < .001), who have worked less than 11 years (P < .001), who work in a public hospital (P < .001) (compared to those who work in primary healthcare and university hospital), who had contact with COVID-19 patients (P = .016), whose workload increased (P < .001) and income decreased (P = .005) during the pandemic process, and who had difficulty in PPE (P < .001) (Table 3). The D score was higher in male (P < .001), those under the age of 40 (P < .001), physicians (compared to nurses and other health workers) and other HCWs (compared to nurses) (P < .001), who have no children (P < .001), who have worked less than 11 years (P < .001), who work in public and university hospitals (P < .001), who work in metropolitan (P < .001), who had contact with COVID-19 patients (P = .015), whose workload (P = .004) and income decreased (P = .017) during the pandemic process, who have no individuals at high risk for COVID-19 at home (P = .046) and who had difficulty in PPE (P < .001). The PA score was higher in those aged 40 and over (P < .001), married (P = .032), nurses (P < .001), who have at least one child (P < .001), who have worked for 11 years or more (P < .001), and who have individuals at high risk for COVID-19 at home (P = .011) (Table 3).
Distribution Of Participants' Socio-Demographic and Pandemic Process Characteristics According to DASS-21 Subscales Scores
The depression score was higher in those under the age of 40 (P < .001), single/widow (P < .001), who have no children (P < .001), who have worked less than 11 years (P < .001), who had contact with COVID-19 patients (P = .020), whose income decreased during the pandemic process (P = .033), and who had difficulty in PPE (P = .028) (Table 3). The anxiety score was higher in female (P < .001), those under the age of 40 (P < .001), single/widow, nurses and other HCWs (compared to physicians) (P < .001), who have no children (P < .001), who have worked less than 11 years (P = .003), who work in a public hospital (P < .001), who have any chronic disease (P = .023), who had contact with COVID-19 patients (P = .004), whose workload increased during the pandemic process (P = .001), and who have individuals at high risk for COVID-19 at home (P = .006). The stress score was higher in those under the age of 40 (P = .003), who have no children (P = .006), and who have worked less than 11 years (P = .001) (Table 3).
Distribution of Participants' Socio-Demographic and Pandemic Process Characteristics According to BHS Scores
The hopelessness score was higher in female (P < .001), in nurses (compared to other HCWs) (P=.022), and in those whose income increased during the pandemic process (P = .007) (Table 3).
Relationship Between Mental Health Levels and Sociodemographic and Pandemic Process Characteristics of the Participants
Working in a public hospital increased the EE 2 times (P = .001), increased workload intensity increased the EE 2.1 times (P = .002), decreased income increased the EE 1.4 times (P=.030), and difficulty in procuring PPE increased the EE 2.2 times (P < .001) (Table 4). Having a male gender increased the D 1.5 times (P = .021), being a physician increased the D 1.7 times (P = .009), working in a public hospital increased the D 1.6 times (P = .030), and difficulty in procuring PPE increased the D 1.8 times (P < .001). Factors associated with low D were being in the age of 40 and over (P = .001) and being a nurse (P = .029). Having worked for 11 years or more increased the PA 1.6 times (P = .010). Having contact with COVID-19 (+) patients increased the depression 1.6 times (P=.018) and decreased income increased the depression 1.5 times (P = .014). Being in the age of 40 and over was associated with low depression (P < .001). Working in a public hospital increased the anxiety 2 times (P = .002), having a chronic disease increased the anxiety 2.1 times (P < .001), having contact with COVID-19 (+) patients increased the anxiety 1.8 times (P = .007), and having individuals at high risk for COVID-19 at home increased the anxiety 1.4 times (P = .037). Factors associated with low anxiety were being a male gender (P=.001) and being in the age of 40 and over (P = .016). Being a physician increased the hopelessness 1.5 times (P=.024), and increased income increased the hopelessness 1.4 times (P = .032). Being a male gender is associated with low hopelessness (P = .048) (Table 4).
Table 4.
The relationship between mental health levels and sociodemographic and pandemic process characteristics of the participants (n= 1015).
| Emotional Exhaustion¹ | Depersonalization¹ | Personal Accomplishment¹ | Depression² | Anxiety² | Stress² | Hopelessness³ | |
|---|---|---|---|---|---|---|---|
| ORa (95% CI), p | ORa (95% CI), p | ORa (95% CI), p | ORa (95% CI), p | ORa (95% CI), p | ORa (95% CI), p | ORa (95% CI), p | |
| Gender (Ref C= female) | |||||||
| Male | Not included | 1.49 (1.06–2.09), .021* | Not included | Not included | .55 (.39–.77), .001** | Not included | .73 (.53–.99), .048* |
| Age (Ref C= <40) | |||||||
| ≥40 | .71 (.48–1.04), .078 | .49 (.32–.76), .001** | 1.21 (.84–1.76), .297 | .47 (.31–.71), <.001*** | .59 (.39–.91), .018* | .57 (.33–1.01), .050 | Not included |
| Marital status (Ref C= Single + Widow) | |||||||
| Married | Not included | Not included | .94 (.63–1.40), .763 | .91 (.61–1.34), .621 | 1.08 (.72–1.64), .727 | Not included | Not included |
| Occupation (Ref C= Other healthcare worker) | |||||||
| Physician | 1.35 (.93–1.95), .111 | 1.68 (1.14–2.49), .009** | .74 (.52–1.04), .078 | Not included | .70 (.47–1.03), .091 | Not included | 1.54 (1.06-2.24), .024* |
| Nurse | .74 (.50–1.11), .143 | .57 (.35–.95), .029* | 1.40 (.95–2.05), .090 | Not included | .73 (.47–1.14), .155 | Not included | 1.44 (.93–2.22), .105 |
| Presence of children (Ref C= None) | |||||||
| At least one | .77 (.56–1.06), .105 | .79 (.57–1.10), .155 | 1.19 (.80–1.77), .389 | .67 (.45–1.01), .052 | .78 (.51–1.18), .253 | .77 (.52–1.15), .205 | Not included |
| Total working time (years) (Ref C= <11) | |||||||
| ≥11 | .86 (.59–1.24), .410 | .93 (.63–1.37), .698 | 1.60 (1.12–2.29), .010* | 1.04 (.71–1.51), .836 | .94 (.64–1.39), .752 | .92 (.57–1.49), .746 | Not included |
| Health institution (Ref C= University hospital) | |||||||
| Primary healthcare | .90 (.62–1.29), .564 | 1.23 (.84–1.80), .285 | Not included | Not included | 1.13 (.76–1.69), .524 | Not included | Not included |
| Public hospital | 2.00 (1.31–3.03), .001** | 1.61 (1.05–2.47), .030* | Not included | Not included | 1.98 (1.28–3.06), .002** | Not included | Not included |
| Place of the residence (Ref C= County) | |||||||
| Metropolitan | Not included | 1.41 (.93–2.13), .102 | Not included | Not included | Not included | Not included | Not included |
| City | Not included | 1.01 (.69–1.47), .975 | Not included | Not included | Not included | Not included | Not included |
| Presence of chronic disease (Ref C= No) | |||||||
| Yes | Not included | Not included | Not included | Not included | 2.06 (1.43–2.96), <.001*** | Not included | Not included |
| Contact with COVID-19 (+) patients (Ref C= No) | |||||||
| Yes | 1.14 (.80–1.65), .467 | .93 (.63–1.38), .728 | Not included | 1.61 (1.09–2.38), .018* | 1.79 (1.17–2.72), .007** | Not included | Not included |
| Workload intensity in the pandemic (Ref C= Not changed) | |||||||
| Increased | 2.14 (1.31–3.51), .002** | 1.63 (.96–2.79), .073 | Not included | Not included | 1.06 (.62–1.81), .805 | Not included | Not included |
| Decreased | .98 (.53–1.82), .944 | 1.56 (.82–3.00), .179 | Not included | Not included | .85 (.42–1.73), .631 | Not included | Not included |
| Presence of individuals at high risk for COVID-19 at home (Ref C= No) | |||||||
| Yes | Not included | .88 (.66–1.17), .373 | 1.29 (.99–1.67), .055 | Not included | 1.35 (1.02–1.80), .033* | Not included | Not included |
| Change in income during the pandemic (Ref C= Not changed) | |||||||
| Increased | .97 (.69–1.38), .882 | .87 (.60–1.28), .484 | Not included | .86 (.60–1.23), .412 | Not included | Not included | 1.44 (1.03–2.01), .032* |
| Decreased | 1.42 (1.04–1.96), .030* | 1.27 (.92–1.77), .150 | Not included | 1.48 (1.08–2.01), .014* | Not included | Not included | .92 (.67–1.27), .616 |
| Difficulty in procuring personal protective equipment (Ref C= No) | |||||||
| Yes | 2.24 (1.61–3.12), <.001*** | 1.84 (1.34–2.53), <.001*** | Not included | 1.35 (.99–1.83), .056 | Not included | Not included | Not included |
n Number of participants, OR a Adjusted odds ratio, CI Confidence interval, Ref C Reference category, Reference categories; ¹=Low, ²=Normal, ³=Minimal/mild
*P<.05, **P<.01, ***P<.001.
Discussion
HCWs are considered to be among the groups with the highest potential to be affected by epidemics. 2 In a study with HCWs during the peak of the pandemic in Italy, it was observed that 37.0% of the HCWs showed high score of EE and 24.7% of them showed high levels of D, and 15.3% of them reported low levels of PA. 14 In another study, burnout levels in HCWs were reported as 41% for EE, 15.2% for D, and 8.4% for low PA. 15 The burnout levels detected in our study were higher than those in these studies. This situation may be related to the prolonged pandemic process. The prolongation of the process may have driven health workers to more burnout.
A limited number of studies have been found in the literature that present the prevalence of burnout among HCWs by occupational groups. In a study in Taiwan, it was determined that 45% of the nurses and 31% of the physicians suffered from burnout. 16 Roslan et al reported that burnout levels were between 26.6% and 63.5% for physicians, 14.9% to 55.3% for nurses. 17 Although different rates were determined between burnout levels according to occupational groups in our study and other studies, it was observed that burnout was high in HCWs during the pandemic process.
In a study, the prevalence of moderate/severe anxiety was found as 11.9% in physicians, 14.9% in nurses, 18.4% in public health professionals, and 8.9%–14.5% in other HCWs during the pandemic process. 18 In the same study, the prevalence of moderate/severe depression was found as 12.8% in all HCWs, 12.9% in physicians, 12.0% in nurses, 20.8% in public health professionals, and 11.2%−12.2% in other HCWs. 18 A systematic review and meta-analysis found the prevalence of depression and anxiety in HCWs to be 23.2% for anxiety and 22.8% for depression. 19 The reason for the higher rates of depression and anxiety that we found in our study may be that we conducted the study in the second year of the pandemic (prolongation of the pandemic process). However, the fact that anxiety prevalence was found to be higher in nurses than physicians, and depression prevalence in physicians than nurses support our study. On the other hand, there are studies reporting higher rates of depression and anxiety than we found in our study. In two previous studies, the prevalence of anxiety and depression was 44.7%, 50.7%, 20 and 44.6%, 50.4%, 21 respectively. This may be since these studies were conducted among HCWs working in the hospital.
In another systematic review, while moderate/high levels of anxiety (26.5%–44.6%) and depression (8.1%–25%) were found in HCWs, paradoxically, stress levels were found to be below expectations (3.8%–68%). 22 In a study in Italy, the prevalence of hopelessness was 16.7% for physicians and 29.2% for nurses. 23 The reason for the higher levels of hopelessness in our study may be that we carried out this study in the later stages of the pandemic, as a matter of fact, the prolongation of the process leads to more hopelessness especially in HCWs who are very worn out in this process.
In their study, Hacimusalar et al 6 found that nurses’ hopelessness levels were higher than physicians and also found that state-anxiety levels in nurses were higher than physicians similar to our study. But, in another study among HCWs working in pandemic hospitals, it was reported that the mean scores of burnout of the nurses were higher than the mean scores of physicians and other HCWs. 24 In another research, burnout levels of nurses were found to be higher than other HCWs. 14
Factors affecting the mental state of HCWs were listed as follows in many studies. Pappa et al found that female HCWs and nurses had higher rates of emotional symptoms. 19 In the study by Lai et al, it was pointed out that being a female and working in the front line are risk factors for stress symptoms. 21 On the other hand, young age, female gender, working in a high-risk environment, and less work experience were reported as factors that cause anxiety, depression and burnout in HCWs. 5 In the study carried out by Yıldırım et al, the researchers detected that those who were female, divorced/widowed, and those with less knowledge about COVID-19 had higher levels of depression and anxiety, and they also detected that those who were young had only higher level of anxiety. 25 Another study found higher levels of hopelessness in HCWs compared to non-HCWs, in nurses, in those with increasing weekly working hours, in female, in those living with a high-risk individual at home, in those who had difficulty in caring for children, and in those whose income decreased. 6 In the another study, the risk factors were as follows: working in a high-risk department, diagnosed family member, inadequate hand hygiene before and after contact with patients, inappropriate use of PPE, and long daily contact hours. 26 As it can be seen in the results of these studies, like our study, younger age, female gender, working in a high-risk environment, workload intensity, and difficulty in procuring or inappropriate use of PPE appear as factors that negatively affect the mental state of HCWs. Unlike our study, being nurse come to the fore affecting the mental state of HCWs negatively. In the current study, being a physician and having a male gender were risk factors especially for burnout (D). Additionally, being a physician was found to be a risk factor for hopelessness. Being a nurse was also a factor that increased the level of hopelessness, although it was not statistically significant. Thus, in our study, it was also observed that burnout, depression, and hopelessness levels were higher in physicians, anxiety and hopelessness levels were higher in nurses, and depression, anxiety, and stress levels were higher in other HCWs.
As we determined in our study, female gender also has been shown as a risk factor that increases hopelessness in previous studies.6,27 On the other hand, increased income was found as a risk factor for hopelessness in our study. We can explain this unexpected finding as follows: additional payments were made periodically to HCWs working actively during the pandemic in Turkey. However, the additional payments made were not at a level to satisfy the HCWs. This situation may have led to increased hopelessness.
The limitations of our study can be listed as follows: using only MBI, DASS-21, and BHS, respectively, to determine the levels of burnout, depression, anxiety, stress, and hopelessness of HCWs; the mental state evaluations of the participants were not supported by clinical examinations, not knowing these mental states of the participants before the pandemic process, a limited number of participants who may limit the generalization of the findings, low study response rate, and not having any information about the characteristics of those who did not respond.
Conclusions
In this study, the prevalence of burnout, depression, anxiety, and hopelessness was found to be much higher in HCWs than in studies on similar subjects in the literature (except for studies conducted only with HCWs working in hospitals). Younger age, gender, working in a public hospital, workload intensity, chronic disease, individuals at high risk for COVID-19 at home, contact with COVID-19 patients, income, and difficulty in procuring PPE were the factors that affect the mental state of HCWs. Unlike other studies in the literature, being a physician and having a male gender were risk factors especially for burnout (D). Additionally, being a physician was found to be a risk factor for hopelessness. This study revealed increasing mental problems in HCWs in a period when we leave behind about one and half year of the pandemic. The increase in cases with the emergence of new variants with each passing day continues to prolong the pandemic process. This means increased workload and worse mental state for HCWs. It may be recommended to follow up the mental status of HCWs and even to establish special follow-up centers for this purpose. More studies are needed to better understand the experiences and needs of HCWs and to further explore their expectations during the pandemic.
Footnotes
Declaration of Conflicting Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The authors received no financial support for the research, authorship, and/or publication of this article.
Ethical Approval: Ethical approval was obtained from the Non-Invasive Ethics Committee of Sivas Cumhuriyet University (Decision no: 2020-12/19).
ORCID iD
İrem AKOVA, MD https://orcid.org/0000-0002-2672-8863
References
- 1.Allsopp K, Brewin CR, Barrett A, et al. Responding to mental health needs after terror attacks. BMJ. 2019;366:l4828. doi: 10.1136/bmj.l4828. Published online August 13 [DOI] [PubMed] [Google Scholar]
- 2.Cetintepe SP, ilhan MN. Covid-19 salgınında sağlık çalışanlarında risk azaltılması. Journal of Biotechnology and Strategic Health Research. 2020;1:50-54. doi: 10.34084/bshr.712539 [DOI] [Google Scholar]
- 3.Koh D, Lim M-K, Chia S-E. SARS: health care work can be hazardous to health. Occup Med. 2003;53(4):241-243. doi: 10.1093/occmed/kqg090 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Luo M, Guo L, Yu M, Jiang W, Wang H. The psychological and mental impact of coronavirus disease 2019 (COVID-19) on medical staff and general public - A systematic review and meta-analysis. Psychiatr Res. 2020;291:113190. doi: 10.1016/j.psychres.2020.113190. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Serrano-Ripoll MJ, Meneses-Echavez JF, Ricci-Cabello I, et al. Impact of viral epidemic outbreaks on mental health of healthcare workers: a rapid systematic review and meta-analysis. J Affect Disord. 2020;277:347-357. doi: 10.1016/j.jad.2020.08.034. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Hacimusalar Y, Kahve AC, Yasar AB, Aydin MS. Anxiety and hopelessness levels in COVID-19 pandemic: A comparative study of healthcare professionals and other community sample in Turkey. J Psychiatr Res. 2020;129:181-188. doi: 10.1016/j.jpsychires.2020.07.024. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Moazzami B, Razavi-Khorasani N, Dooghaie Moghadam A, Farokhi E, Rezaei N. COVID-19 and telemedicine: Immediate action required for maintaining healthcare providers well-being. J Clin Virol. 2020;126:104345. doi: 10.1016/j.jcv.2020.104345. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Vieta E, Pérez V, Arango C. Psychiatry in the aftermath of COVID-19. Rev Psiquiatía Salud Ment. 2020;13(2):105-110. doi: 10.1016/j.rpsm.2020.04.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Chen Q, Liang M, Li Y, et al. Mental health care for medical staff in China during the COVID-19 outbreak. Lancet Psychiatr. 2020;7(4):e15-e16. doi: 10.1016/S2215-0366(20)30078-X. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Ergin C. Adoption of maslach burnout inventory in doctors and nurses. In: National Psychology Congress Scientific Studies; 1992. [Google Scholar]
- 11.Lovibond SH, Lovibond PF. Manual for the Depression Anxiety Stress Scales. 2nd ed. Psychology Foundation of Australia; 1995. [Google Scholar]
- 12.Yılmaz Ö, Boz H, Arslan A. The validity and reliability of depression stress and anxiety scale (dass21) turkish short form. Res Financ Econ Soc Stud. 2017;2(2):78-91. [Google Scholar]
- 13.Durak A, Palabıyıkoğlu R. Beck Umutsuzluk Ölçeği Geçerlilik Çalışması. Kriz Derg. 2006;2(2):311-319. [Google Scholar]
- 14.Barello S, Palamenghi L, Graffigna G. Burnout and somatic symptoms among frontline healthcare professionals at the peak of the Italian COVID-19 pandemic. Psychiatr Res. 2020;290:113129. doi: 10.1016/j.psychres.2020.113129. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Luceño-Moreno L, Talavera-Velasco B, García-Albuerne Y, Martín-García J. Symptoms of Posttraumatic Stress, Anxiety, Depression, Levels of Resilience and Burnout in Spanish Health Personnel during the COVID-19 Pandemic. Int J Environ Res Publ Health. 2020;17(15):5514. doi: 10.3390/ijerph17155514. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Sung C-W, Chen C-H, Fan C-Y, et al. Burnout in Medical Staffs during a Coronavirus Disease (COVID-19) Pandemic. Published online; 2020:SSRN3594567. [Google Scholar]
- 17.Roslan NS, Yusoff MSB, Asrenee AR, Morgan K. Burnout prevalence and its associated factors among malaysian healthcare workers during COVID-19 pandemic: an embedded mixed-method study. Healthcare. 2021;9(1):90. doi: 10.3390/healthcare9010090. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Que J, Shi L, Deng J, et al. Psychological impact of the COVID-19 pandemic on healthcare workers: a cross-sectional study in China. General Psychiatry. 2020;33(3):e100259. doi: 10.1136/gpsych-2020-100259. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Pappa S, Ntella V, Giannakas T, Giannakoulis VG, Papoutsi E, Katsaounou P. Prevalence of depression, anxiety, and insomnia among healthcare workers during the COVID-19 pandemic: A systematic review and meta-analysis. Brain Behav Immun. 2020;88:901-907. doi: 10.1016/j.bbi.2020.05.026. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Liu S, Yang L, Zhang C, et al. Online mental health services in China during the COVID-19 outbreak. Lancet Psychiatr. 2020;7(4):e17-e18. doi: 10.1016/S2215-0366(20)30077-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Lai J, Ma S, Wang Y, et al. Factors associated with mental health outcomes among health care workers exposed to coronavirus disease 2019. JAMA Netw Open. 2020;3(3):e203976. doi: 10.1001/jamanetworkopen.2020.3976. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.García-Iglesias JJ, Gómez-Salgado J, Martín-Pereira J, et al. [Impact of SARS-CoV-2 (Covid-19) on the mental health of healthcare professionals: a systematic review.]. Rev Esp Salud Publica. 2020;94:23. http://www.ncbi.nlm.nih.gov/pubmed/32699204 [PMC free article] [PubMed] [Google Scholar]
- 23.Franza F, Basta R, Pellegrino F, Solomita B, Fasano V. The role of fatigue of compassion, burnout and hopelessness in healthcare: experience in the time of COVID-19 outbreak. Psychiatr Danub. 2020;32(suppl 1):10-14. http://www.ncbi.nlm.nih.gov/pubmed/32890354. [PubMed] [Google Scholar]
- 24.Çelmeçe N, Menekay M. The effect of stress, anxiety and burnout levels of healthcare professionals caring for COVID-19 patients on their quality of life. Front Psychol. 2020;11:597624. doi: 10.3389/fpsyg.2020.597624. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Yildirim TT, Atas O, Asafov A, Yildirim K, Balibey H. Psychological status of healthcare workers during the Covid-19 pandemic. Journal of the College of Physicians and Surgeons Pakistan. 2020;30(6):26-31. doi: 10.29271/jcpsp.2020.Supp1.S26. [DOI] [PubMed] [Google Scholar]
- 26.Shaukat N, Ali DM, Razzak J. Physical and mental health impacts of COVID-19 on healthcare workers: a scoping review. Int J Emerg Med. 2020;13(1):40. doi: 10.1186/s12245-020-00299-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Serafini G, Lamis DA, Aguglia A, et al. Hopelessness and its correlates with clinical outcomes in an outpatient setting. J Affect Disord. 2020;263:472-479. doi: 10.1016/j.jad.2019.11.144. [DOI] [PubMed] [Google Scholar]