Observational Study Open Access
Copyright ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Psychiatry. Jun 19, 2022; 12(6): 843-859
Published online Jun 19, 2022. doi: 10.5498/wjp.v12.i6.843
Dimensions of emotional distress among Brazilian workers in a COVID-19 reference hospital: A factor analytical study
Marcos O Carvalho-Alves, Vitor A Petrilli-Mazon, Andre R Brunoni, Andre Malbergier, Pedro Fukuti, Guilherme V Polanczyk, Euripedes C Miguel, Felipe Corchs, Yuan-Pang Wang
Marcos O Carvalho-Alves, Vitor A Petrilli-Mazon, Andre R Brunoni, Andre Malbergier, Pedro Fukuti, Guilherme V Polanczyk, Yuan-Pang Wang, Department of Psychiatry, School of Medicine, University of Sao Paulo, Sao Paulo 05403-010, Brazil
Marcos O Carvalho-Alves, Felipe Corchs, Program in Neuroscience and Behavior, Department of Experimental Psychology, Institute of Psychology, University of Sao Paulo, Sao Paulo 01060-970, Brazil
Euripedes C Miguel, Department and Institute of Psychiatry, University of Sao Paulo, Sao Paulo 05403-010, Brazil
Felipe Corchs, Department of Psychiatry, University of Sao Paulo, Sao Paulo 05403-010, Brazil
ORCID number: Marcos O Carvalho-Alves (0000-0002-4136-6311); Vitor A Petrilli-Mazon (0000-0002-5874-7401); Andre R Brunoni (0000-0002-6310-3571); Andre Malbergier (0000-0001-6093-8381); Pedro Fukuti (0000-0002-7690-0895); Guilherme V Polanczyk (0000-0003-2311-3289); Euripedes C Miguel (0000-0002-9393-3103); Felipe Corchs (0000-0002-9935-5658); Yuan-Pang Wang (0000-0001-7076-8312).
Author contributions: Carvalho-Alves MO, Brunoni AR, Malbergier A, Fukuti P, Polanczyk GV, Miguel EC and Corchs F contributed to the study design, data acquisition and interpretation, and revised the manuscript; Wang YP conceived the statistical analysis; Carvalho-Alves MO, Petrilli-Mazon VA, Corchs F and Wang YP conducted the statistical analysis and wrote the preliminary draft; all authors reviewed and approved the manuscript in its final version.
Institutional review board statement: This study was approved by the National Research Ethics Commission of the Ministry of Health, Brazil.
Informed consent statement: All study participants gave their informed consent before study inclusion.
Conflict-of-interest statement: There are no conflicts of interest in this work.
Data sharing statement: No additional data are available.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Yuan-Pang Wang, MD, MSc, PhD, Research Scientist, Department of Psychiatry, School of Medicine, University of Sao Paulo, Rua Dr. Ovídio Pires de Campos, 785, Cerqueira César, Sao Paulo 05403-010, Brazil. gnap_inbox@hotmail.com
Received: December 21, 2021
Peer-review started: December 21, 2021
First decision: March 13, 2022
Revised: April 22, 2022
Accepted: May 13, 2022
Article in press: May 13, 2022
Published online: June 19, 2022


The coronavirus disease 2019 (COVID-19) pandemic is an unprecedented challenge for public health and has caused the loss of millions of lives worldwide. Hospital workers play a key role in averting the collapse of the health system, but the mental health of many has deteriorated during the pandemic. Few studies have been devoted to identifying the needs of workers on frontline duty.


To investigate dimensions of common emotional symptoms and associated predictors among Brazilian workers in a COVID-19 reference hospital.


This is an observational study of the mental health of professionals in a COVID-19 hospital in the city of São Paulo. We invited all hospital employees to respond to an online survey between July and August 2020, during the first peak of the pandemic. Data of 1000 participants who completed the survey were analyzed (83.9% were women and 34.3% were aged 30 to 40). Hospital workers self-reported the presence of symptoms of depression, anxiety, trauma-related stress, and burnout through the Patient Health Questionnaire-9, the Generalized Anxiety Disorder-7, the Impact of Event Scale-Revised and the Mini-Z Burnout Assessment respectively. Responses were assembled and subjected to exploratory factor analysis to reveal workers’ core emotional distress. Multiple linear regression models were subsequently carried out to estimate the likelihood of dimensions of distress using questions on personal motivation, threatening events, and institutional support.


Around one in three participants in our sample scored above the threshold of depression, anxiety, post-traumatic stress disorder, and burnout. The factor analysis revealed a three-factor structure that explained 58% of the total data variance. Core distressing emotional domains were avoidance and re-experience, depression-anxiety, and sleep changes. Regression analysis revealed that institutional support was a significant protective factor for each of these dimensions (β range = -0.41 to -0.20, P < 0.001). However, participants’ personal motivation to work in healthcare service was not associated with these emotional domains. Moreover, the likelihood of presenting the avoidance and re-experience dimension was associated with having a family member or close friend be hospitalized or die due to COVID-19 and having faced an ethical conflict.


Distressing emotional domains among hospital workers were avoidance and re-experience, depression and anxiety, and sleep changes. Improving working conditions through institutional support could protect hospital workers' mental health during devastating public health crises.

Key Words: COVID-19, Pandemics, Health personnel, Mental health, Psychological distress, Occupational medicine

Core Tip: Although the literature contains many reports on the deteriorating mental health of hospital workers during pandemics, few investigations have focused on the core mental health needs of this specific population. Hence, we subjected the common emotional symptoms of hospital workers to exploratory factor analysis. The main emotional dimensions were avoidance and re-experience, depression-anxiety, and sleep changes. Institutional support was found to be the most relevant protective factor for these emotional dimensions. This investigation could contribute to a better understanding of work-related distress from a dimensional perspective and has indicated comprehensive coping strategies in healthcare settings during a public health emergency.


The coronavirus disease 2019 (COVID-19) is an infectious disease that emerged in Wuhan, China in late 2019, and rapidly spread worldwide. A dramatic loss of human life, economic disruption, unemployment, and food insecurity, all caused by the pandemic, have imposed a monumental challenge on communities. Millions of people are at risk of being infected by a life-threatening virus and falling into extreme poverty due to economic and social hardships. During the lockdown, to mitigate the spread of the epidemic, hospital workers (HWs) have played a key role in the fight against disease outbreaks, but without the privilege of confinement. Besides saving lives in exhaustive duties, HWs face an insurmountable burden of increased risk of infection, fear of infecting family members, increased workload, inadequate support, and discrimination[1]. The healthcare workforce is a particularly vulnerable population because the majority lack work protection and access to quality personal protection equipment (PPE). Thus, HWs are exposed to an overwhelmingly stressful environment, which contributes to the deterioration of their mental health, with subsequent development of multiple emotional symptoms.

Frontline HWs directly involved with patient care present greater vulnerability to developing disabling emotional symptoms, as shown in previous epidemics of Ebola virus disease and severe acute respiratory syndrome[2,3]. Among different psychological reactions, symptoms of anxiety were the first to emerge in the early stages of epidemics. Although anxiety sometimes wanes over the course of the observation, symptoms of depression and distress may persist or intensify[4]. A recent cross-sectional study involving 1257 Chinese COVID-19 healthcare workers (HCWs) has indicated that 71.5% of the sample experienced symptoms of distress, 44.6% anxiety, 50.4% depression, and 34% insomnia[5]. Globally, a comprehensive review confirmed the high frequency of depression (24.3%), anxiety (25.8%), and stress (45%) among frontline HCWs caring for COVID-19 patients[6]. Likewise, a high frequency of symptoms of burnout was also reported in over one-third of Italian healthcare professionals[7]. Regarding the Brazilian context, a study composed of Brazilian HCWs from different regions also found high rates of anxiety (43.3%), depression (40.2%), trauma (36%), and insomnia (61.5%)[8]. Nevertheless, these rates present large fluctuations because data collection relies on individual and contextual aspects of vulnerability, such as socio-demographic characteristics, social support, time of data collection, institutional infrastructure, and public responses, among other factors. Thus, these quantitative rates are limited indicators for clarifying the psychological impact of the COVID-19 and the possibilities of coping with it.

Bearing in mind the plethora of observational studies describing the poor mental health of HWs during the pandemic, few studies have determined the symptomatic clustering of occupational distress during the pandemic. Because most of the emotional symptoms of HWs appear at the same time, we took advantage of a data reduction method of factor analysis to examine the structure of self-reported symptoms in this population. We estimated both individual and contextual factors that were potentially associated with dimensions of distress. The next logical step is to understand how to prevent or reduce emotional distress in healthcare settings. Hypothetically, we posited that core dimensions of emotional distress among HWs would manifest as a sound structure, and that protective or risk factors for this distress would indicate meaningful coping strategies.

The primary objective of the present study was to determine the structure of the mental health of HWs during the COVID-19 pandemic, as related to anxiety, depression, event-related stress, and burnout. Secondarily, we aimed to determine correlated factors of HWs’ mental health. These findings could contribute to a greater understanding of the human capacity to face extreme working conditions during global sanitary crises. The implications of potential factors that could improve preventive and supportive strategies in pandemic contexts are discussed.

Study design

Data of the current observational study were cross-sectionally collected between July 1 and August 28, 2020, using an online survey on the REDCap platform (https://www.project-redcap.org/). This is the baseline data of an ongoing longitudinal study on HWs’ mental health.

At the time of data collection for this study, Brazil was one of the pandemic epicenters of the world, with high rates of new cases and deaths per day. The country had 2662485 confirmed cases, and 92475 deaths as of July 31, 2020[9]. Most of these cases were reported in the state of São Paulo, the most populous in Brazil. São Paulo had 542304 confirmed cases and 22997 deaths in the same period[9]. The Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (HCFMUSP) became the main reference healthcare facility for COVID-19 care in the state. Wards of the HCFMUSP main building were entirely reconfigured into a full capacity of 900 beds for the exclusive care of COVID-19 patients. Between July and August of 2020 - around the decline of the first wave of the pandemic - the well-being of hospital professionals in this large care center was on the verge of collapse.


The inclusion criterion was that participants had to be working at the hospital, in person or from home, at the time of data collection. Medical doctors, nurses, nursing assistants, dentists, speech therapists, psychologists, occupational therapists, dieticians, physical therapists, social workers, pharmacists, clinical laboratory technicians, radiological technologists, and administrative professionals were included as HWs. Professionals from all hospital sites were invited, including the emergency room, inpatient wards, intensive care units, outpatient care, operating room, pharmacy, and laboratory. There were few exclusions as current workers were all adults and able to respond to an online questionnaire. Potential participants did not present linguistic problems, but limited access to the internet from a computer or mobile phone could have been an obstacle to participation.

At the time of the baseline survey, 22056 employees were working in the hospital complex. The online invitation was sent to all HWs through the institutional e-mail, in addition to social media advertising and wall posters in the hospital. Moreover, participants were also encouraged to forward the online survey to eligible colleagues. Respondents could complete the survey, which took approximately 15 min to answer in its entirety, at any time. Using non-probabilistic sampling, data were gathered from 1377 respondents, but only 1000 provided complete data for inclusion in the analysis.

Measurement tools

The following instruments were used: Socio-demographic questionnaire: This instrument consisted of questions about age, gender, marital status, educational level, occupational status, living with children or elderly adults, and time of direct contact with COVID-19 patients (hours per week, as an ordinal scale). Questions related to changes in daily routine and individual’s ability to cope with distress were also included.

The Impact of Event Scale-Revised (IES-R) was used to screen and rate the severity of distress symptoms in the previous seven days, based on the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) criteria of post-traumatic stress disorder (PTSD)[10]. The IES-R is a 22-item self-report scale, with a five-point ordinal scale from “not at all” (score 0) to “extremely” (score 4) for each item[10]. Total scores ranged from 0 to 88. Scores of 9, 26, and 44 are used as the cut-off points for mild, moderate, and severe post-traumatic symptoms, respectively[10]. The cut-off point ≥ 26 was adopted in this study, based on previous literature for a probable case of PTSD[5]. Cronbach's alpha coefficient of the IES-R was α = 0.96, indicating an adequate internal consistency.

The Patient Health Questionnaire-9 (PHQ-9) was used to screen and rate the severity of depressive symptoms in the previous two weeks, based on the DSM-IV[11]. The PHQ-9 is a nine-item self-report scale, with a four-point ordinal scale from “not at all” (score 0) to “nearly every day” (score 3) for each item[11]. The total score ranged from 0 to 27. Scores of 5, 10, 15, and 20 are used as the cut-off points for mild, moderate, moderately severe, and severe depression, respectively[11]. The cut-off point ≥ 10 has a sensitivity of 88% and a specificity of 88%, in comparison to the diagnosis of major depressive disorder[12]. Cronbach’s alpha coefficient of the PHQ-9 was α = 0.90, indicating good internal consistency.

The Generalized Anxiety Disorder-7 (GAD-7) was used to screen and rate the severity of anxiety symptoms in the previous two weeks, based on DSM-IV criteria[13]. The GAD-7 is a seven-item self-report scale, with a four-point ordinal scale from “not at all” (score 0) to “nearly every day” (score 3) for each item[13]. The total score ranged from 0 to 21. Scores of 5, 10, and 15 are used as the cut-off points for mild, moderate, and severe anxiety, respectively[13]. The cut-off point ≥ 10 has a sensitivity of 89% and a specificity of 82%, in comparison to the diagnosis of generalized anxiety disorder[14]. Cronbach's alpha coefficient of the GAD-7 was α = 0.92, indicating appropriate internal consistency.

The validated single-item Mini-Z Burnout Assessment was used to evaluate the experience of burnout[15,16]. This question instructs respondents to define burnout for themselves: “Overall, based on your definition of burnout, how would you rate your level of burnout?”. Responses are scored on a five-category ordinal scale and the threshold of burnout was indicated by a rating ≥ 3. Score 3 was applied to respondents who chose "I am definitely burning out and have one or more symptoms of burnout, such as physical and emotional exhaustion”; score 4 for those who chose “The symptoms of burnout that I’m experiencing won’t go away. I think about frustration at work a lot”; and score 5 for those who chose “I feel completely burned out and often wonder if I can go on. I am at the point where I may need some changes or may need to seek some sort of help.”. This single-item scale was validated against the exhaustion subscale of the Maslach Burnout Inventory, with a correlation of 0.64 (P < 0.001)[15], and previous studies have used it to evaluate burnout during the current pandemic[17].

Psychoactive substance use: Straightforward questions about increased consumption of alcohol and tobacco were included to assess changes in substance use patterns. Answers were recorded as dichotomous yes/no answers.

Threatening events: HWs were asked about the following three self-reported items, with dichotomous yes/no answers, to assess COVID-19 related threatening events: having had a confirmed COVID-19 diagnosis, having had a close family member or friend hospitalized or dying due to COVID-19, and having experienced an ethical conflict during COVID-19 patient care. Ethical issues covered a broad array of extreme contexts such as lack of PPE, disagreement with clinical decisions, overwork, mandatory work despite belonging to a risk group for COVID-19, use of public transportation, etc.

Personal motivation: To evaluate contextual variables in the occupational setting, questions about personal motivation and stressors were formulated based on previous literature[2,4,18]. All answers were scored on a five-point Likert scale: “I feel my family, friends or colleagues recognize me for the work I am doing during the COVID-19 pandemic”; “I feel like I'm gaining new knowledge while I am working on the COVID-19 pandemic”; “I feel that my work on the COVID-19 pandemic helps people”; “I am willing to accept the risks because I want to help infected people”; “I feel like I'm developing myself by working on the COVID-19 pandemic”; “I feel motivated to work on the COVID-19 pandemic”; and “I feel part of a movement in my community to take care of infected people”.

For statistical analysis, the cumulative score of each of the seven items was calculated, generating a total score labeled “personal motivation”. Cronbach's alpha coefficient of personal motivation questions was α = 0.85, indicating appropriate internal consistency.

Institutional support: Based on previous studies[2,4,18], support related to the organizational environment was evaluated using questions on a five-point Likert scale: “I have access to adequate PPE in situations where this is required”; “I have access to equipment and resources needed to provide adequate care to patients”; “I feel that I received adequate training to carry out my work in the COVID-19 pandemic”; “I feel supported by my bosses and by the institution”; “I feel supported by my work team”; “I feel that the patient care protocols are clear in the institution, and I know what must be done”; “If I get infected, I will receive care at my institution”; “If I get infected, my family will receive support”; and “I feel that I have enough rest to continue my job as long as it is necessary”.

For statistical analysis, the cumulative score for each of these items was calculated, leading to a total score labeled as “institutional support”. These items yielded a Cronbach's alpha coefficient of 0.85, indicating adequate internal consistency.

Statistical analysis

All analyses were performed using R software, version 4.0.4 (https://www.r-project.org/), and corresponding packages detailed below. The significance level was set at α = 0.05 for 2-tailed tests. Because data were not normally distributed according to the Shapiro-Wilk test (P < 0.05), a descriptive analysis of participant socio-demographic characteristics was presented as percentage (%) for categorical variables and medians with interquartile ranges for continuous variables.

To investigate the factorial structure of mental health in our sample, an exploratory factor analysis (EFA) was performed. All 39 items of the PHQ-9, the GAD-7, the IES-R, and the single-item Mini-Z Burnout Assessment were assembled in a single dataset to determine the underlying latent constructs of separate items. Before factor extraction, the factorability of the data was checked using the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett’s test of sphericity. The data were considered adequate for performing EFA because Bartlett's test of sphericity χ2 value was 31085.35 (P < 0.001) and the KMO value was 0.98. The between-item correlation was examined in a matrix containing all 39 individual items of the scales. Factors were extracted using the principal axis factoring method. We used criteria such as Kaiser’s eigenvalues > 1[19,20], Cattell’s scree plot inspection[19,20], and clinical interpretability of the resulting factor structure to determine the number of factors to be extracted[19,20]. To aid in factor interpretation, the initial solution was subjected to oblique (Oblimin) rotation, assuming that extracted factors would be correlated to reflect the structure of HWs’ emotional distress[19,20]. Items presenting factor loadings above 0.40 were retained in each factor, due to their substantial contribution to data variance. The EFA was run with psych and GTArotation packages in R.

A collinearity analysis was subsequently conducted using the polycor package to rule out the correlation between independent variables. All analyzed variables had a Variance Inflation Factor below 3 (for more details, see Supplementary Table 1), suggesting that multicollinearity was not a problem in our data. Two multiple linear regression models were carried out to identify potential predictors for each of the retained factors, using the beta.lm function. Factor scores of each of the retained factors were used as dependent variables. After checking for independence, homoscedasticity, normality, and linearity, two regression models were run for each retained factor. First, a crude model included predictor questions on threatening events, personal motivation, and institutional support. Second, the final adjusted model was controlled for sociodemographic variables such as gender, age, marital status, educational level, and occupation. Results were reported as β, 95% confidence interval, and P value. Model fit was estimated in terms of R2.

The statistical methods were reviewed by Wang YP from the Department of Psychiatry, School of Medicine, University of Sao Paulo.


The online survey was anonymous and participant confidentiality was assured. Due to social isolation, data were collected by means of an online survey which included an informed consent form explaining the study design, its purpose, and the responsible researcher of the study. This study was approved by the Institutional Board of Research Ethics, protocol # 30710620.2.0000.0068.

Demographic and mental health characteristics

Considering Table 1, out of 1000 participants who completed the survey, 83.9% were women, 34.3% were aged 30 years old to 40 years old, 57.4% were married or living with a partner, and 72.9% had an educational level of university graduate or higher. In terms of occupational characteristics, 74.1% were HCWs directly involved in patient care and the remaining 25.9% had no direct contact with patients infected with COVID-19 (office workers and clinical clerks). Regarding participant occupations, 14% were medical doctors, 34.8% were nurses or nursing assistants, and 25.3% were other healthcare professionals. Although participants were recruited using a non-probabilistic strategy, the distribution of socio-demographic characteristics was similar to the total sample of employees in the institution, in terms of gender, age, and occupation. Regarding threatening events directly related to COVID-19 care, 79.6% had direct contact with COVID-19 patients and 32.8% reported having had COVID-19 themselves. An additional 38.6% reported having had a close family member or friend hospitalized or dying due to COVID-19. Approximately one in five participants reported having had to deal with an ethical conflict related to COVID-19 patient care. Regarding the previous history of mental disorders, 28% reported previous psychiatric or psychological treatment and 13.8% reported psychological or psychiatric treatment after the onset of the pandemic.

Table 1 Sociodemographic and clinical characteristics of participants (n = 1000).
n (%)
Age bracket
18-30211 (21.1)
30-40343 (34.3)
40-50252 (25.2)
> 50194 (19.4)
Female839 (83.9)
Male159 (15.9)
Other2 (0.2)
Marital status
Unmarried426 (42.6)
Married574 (57.4)
Educational level
< University graduate271 (27.1)
≥ University graduate729 (72.9)
Living with elderly (> 60 yr)
Yes233 (23.3)
No767 (76.7)
Living with children
Yes450 (45.0)
No550 (55.0)
Medical doctor140 (14.0)
Nurse and nursing assistants348 (34.8)
Other healthcare professionals1253 (25.3)
Administrative workers2259 (25.9)
Work sector
Emergency room60 (6.0)
Inpatient ward176 (17.6)
Intensive care unit157 (15.7)
Outpatient care128 (12.8)
Operating room44 (4.4)
Pharmacy36 (3.6)
Laboratory84 (8.4)
Other sectors163 (16.3)
Direct contact with COVID-19 patient (h/wk)
0204 (20.4)
1-20311 (31.1)
21-40285 (28.5)
> 40200 (20.0)
Had COVID-19 (self-reported)
Yes328 (32.8)
No672 (67.2)
Close family or friend hospitalized or who died due to COVID-19
Yes386 (38.6)
No614 (61.4)
Changes in daily routine due to pandemic
Financial failure387 (38.7)
Lack of public safety199 (19.9)
Lack of public transport297 (29.7)
Lack of medical care292 (29.2)
Distancing from family and friends620 (62.0)
Previous psychiatric or psychological treatment
Yes280 (28.0)
No720 (72.0)
Previous self-reported diagnoses
Anxiety91 (9.1)
Depression78 (7.8)
PTSD6 (0.6)
Previous psychotherapy treatment199 (19.9)
Previous pharmacological treatment177 (17.7)
Psychological or psychiatric treatment after pandemic beginning138 (13.8)
Protective health actions
Physical activities274 (27.4)
Meditative practices182 (18.2)
Leisure activities/hobbies320 (32.0)
Religious practices310 (31.0)
I'm not doing anything in this sense354 (35.4)
Ethical conflict119 (11.9)

Table 2 shows the range and frequency of the scores of the rating scales IES-R, PHQ-9, GAD-7, and Mini-Z Burnout Assessment. The score was categorized into severity levels, according to the established cut-offs. With reference to clinically significant levels of assessed psychiatric categories, 46.8% of participants scored above the level for a probable case of PTSD, 37.9% for depression, 32.5% for anxiety, and 34.9% for burnout. An additional 7.6% of participants reported increased tobacco consumption and 17.1% reported increased alcohol consumption.

Table 2 Frequency of categories of distress symptoms (n = 1000).
Scale and severity categories
n (%)
The Patient Health Questionnaire-97 (4-13)1
Minimal (< 5)312 (31.2)
Mild (5-9)309 (30.9)
Moderate (10-14)177 (17.7)
Moderately severe (15-19)116 (11.6)
Severe (≥ 20)86 (8.6)
The Generalized Anxiety Disorder-76 (3-12)1
Minimal (< 5)347 (34.7)
Mild (5-9)328 (32.8)
Moderate (10-14)154 (15.4)
Severe (≥ 15)171 (17.1)
The Impact of Event Scale-Revised24 (11-42)1
Minimal (< 9)197 (19.7)
Mild (9-25)335 (33.5)
Moderate (26-43)225 (22.5)
Severe (≥ 44)243 (24.3)
Mini-Z Burnout Assessment (≥ 3)2349 (34.9)
Increased tobacco consumption76 (7.6)
Increased alcohol consumption171 (17.1)
Factor analysis

Table 3 shows the rotated pattern matrix of the EFA solution. The initial solution for the 39 items yielded two factors meeting Kaiser’s eigenvalue > 1 criterion. Nevertheless, the scree test suggested a three- or four-factor solution, according to Cattell’s criterion. Taking into account these two criteria and the clinical interpretability of the resulting factorial structure, a three-factor solution was chosen as the optimal model, in view of the balance between parsimony and comprehensiveness.

Table 3 Pattern matrix of rotated Oblimin solution as extracted through principal axis factoring.
Avoidance and re-experience
Sleep changes
I tried to remove it from my memoryIES-R-170.81-
I found myself acting or feeling as though I was back at that timeIES-R-140.790.03-0.010.66
I was aware that I still had a lot of feelings about it, but I didn't deal with themIES-R-120.79-
I tried not to think about itIES-R-110.78-
Pictures about it popped into my mindIES-R-90.760.100.010.70
I was jumpy and easily startledIES-R-100.750.120.020.71
I tried not to talk about itIES-R-220.75-0.06-0.030.48
My feelings about it were kind of numbIES-R-130.72-0.02-0.020.48
I thought about it when I didn't mean toIES-R-60.700.140.090.73
I had waves of strong feelings about itIES-R-160.680.120.120.71
I stayed away from reminders about itIES-R-80.68-
I felt watchful or on-guardIES-R-210.650.190.010.62
I felt as if it hadn't happened or wasn't realIES-R-70.620.05-0.020.42
I avoided letting myself get upset when I thought about it or was reminded of itIES-R-50.620.050.070.48
Other things kept making me think about itIES-R-30.610.120.220.70
Reminders of it caused me to have physical reactions, such as sweating, trouble breathing, nausea, or a pounding heartIES-R-190.600.200.030.59
Any reminder brought back feelings about itIES-R-10.590.190.070.59
I had dreams about itIES-R-200.490.120.100.41
I had trouble concentratingIES-R-180.450.320.120.61
I felt irritable and angryIES-R-40.450.340.090.60
Feeling down, depressed, or hopelessPHQ-9-20.010.84-0.050.68
Feeling tired or having little energyPHQ-9-4-0.150.780.150.60
Little interest or pleasure in doing thingsPHQ-9-1-0.070.750.090.58
Feeling nervous, anxious, or on edgeGAD-7-10.060.740.020.63
Feeling bad about yourself - or that you are a failure or have let yourself or your family downPHQ-9-60.050.74-0.090.52
Not being able to stop or control worryingGAD-7-20.160.72-0.010.69
Becoming easily annoyed or irritableGAD-7-60.110.72-0.030.61
Trouble concentrating on things, such as reading the newspaper or watching televisionPHQ-9-70.040.680.070.55
Worrying too much about different thingsGAD-7-30.130.680.030.62
Trouble relaxingGAD-7-40.010.680.220.69
Overall, based on your definition of burnout, how would you rate your level of burnoutMini-Z-0.050.670.050.45
Moving or speaking so slowly that other people could have noticedPHQ-9-80.180.62-0.080.49
Poor appetite or overeatingPHQ-9-50.040.560.130.45
Feeling afraid as if something awful might happenGAD-7-70.310.56-0.120.53
Or the opposite - being so fidgety or restless that you have been moving around a lot more than usualPHQ-9-90.020.49-0.080.21
Being so restless that it's hard to sit stillGAD-7-50.270.46-0.060.41
I had trouble falling asleepIES-R-150.21-0.030.810.85
I had trouble staying asleepIES-R-
Trouble falling or staying asleep, or sleeping too muchPHQ-9-3-0.160.460.530.60
Eigenvalue; Explained variance (%)10.76; 28.009.22; 24.002.50; 6.00
Total explained variance (%)28.0052.0058.00

After oblique rotation, salient factor loadings (≥ 0.40) for 38 items were observed in a single factor. Cross-loading occurred with the item "trouble falling or staying asleep, or sleeping too much?" (PHQ-9 #3), which contributed to both factors 2 and 3. All three factors accounted cumulatively for 58% of the total data variance. The correlation between factor 1 and factor 2 was 0.64, 0.56 between factor 1 and factor 3, and 0.55 between factor 2 and factor 3. Cronbach's alpha coefficient for factors 1, 2, and 3 was α = 0.96, 0.94, and 0.87, respectively, indicating adequate internal consistency of each extracted factor.

The first dominant factor explained 28% of the total variance and included 20 items from the IES-R but excluded #15 - "I had trouble falling asleep" - and #2 - "I had trouble staying asleep". The second factor explained 24% of the total variance and included all the items from the GAD-7, 8 items from the PHQ-9 (except #3 - "Trouble falling or staying asleep, or sleeping too much?"), and the Mini-Z Burnout Assessment. The third factor explained an additional 6% of the total variance and included two items from the IES-R (#15 and #2) and one item from the PHQ-9 (#3).

We consistently examined how each item loaded in each factor to label each one of the latent factors. The first factor was composed of all the IES-R items, except two items related to sleep ("I had trouble falling asleep" and "I had trouble staying asleep"). The following three items presented the highest loadings: "I tried to remove it from my memory", "I found myself acting or feeling as though I was back at that time" and "I was aware that I still had a lot of feelings about it, but I didn't deal with them". These items are related to major PTSD-associated symptom clusters, namely avoidance, and re-experiencing.

The second factor was composed of all the items from the GAD-7, almost all the items from the PHQ-9 (except one item related to sleep, "trouble falling or staying asleep, or sleeping too much?"), and the Mini-Z Burnout Assessment. The following three items presented the highest loadings: "Feeling down, depressed, or hopeless?", "Feeling tired or having little energy?" and "Little interest or pleasure in doing things?". These items mostly relate to major symptoms associated with depression. Hence, the second factor was labeled Depression-anxiety. Lastly, the third factor was composed of three items associated with sleep, two of which loaded 0.7 or higher: "I had trouble falling asleep" and "I had trouble staying asleep". The third factor was labeled Sleep changes.

Predictors of the mental health dimensions

Table 4 shows crude and adjusted multiple linear regression models which were built to evaluate potential predictors for each of the emotional dimensions retained from the EFA. First, models were carried out using the following independent variables: direct contact with a COVID-19 patient, previous psychiatric and psychological treatment, had COVID-19, close family or friend hospitalized or died due to COVID-19, ethical conflict, personal motivation, and institutional support. The fitness of all three crude models was statistically significant (P < 0.001). Likewise, the adjusted R2 for each of the models was 0.14, 0.25, and 0.08 respectively. Second, three final models were adjusted for age, gender, marital status, educational level, and occupation, yielding an adjusted R2 of 0.18, 0.29, and 0.09, respectively. All adjusted models were statistically significant (P < 0.001) by F test, considering the Bonferroni test for multiple models.

Table 4 Multiple linear regressions between predictable variables and each of the emotional dimensions of hospital workers (n = 1000).

Avoidance and re-experience
Sleep changes

β (95%CI)
β (95%CI)1
β (95%CI)
β (95%CI)1
β (95%CI)
β (95%CI)1
Direct contact with COVID-19 patient (h/wk)0.05 (-0.01 to 0.11)0.02 (-0.04 to 0.09)0.08 (0.02 to 0.13)b0.02 (-0.04 to 0.09)0.03 (-0.03 to 0.08)-0.02 (-0.08 to 0.05)
Previous psychiatric or psychological treatment (self-reported)0.33 (0.2 to 0.46)c0.33 (0.21 to 0.46)c0.38 (0.27 to 0.5)c0.38 (0.26 to 0.49)c0.26 (0.13 to 0.38)c0.25 (0.12 to 0.38)c
Had COVID-19 (self-reported)0.14 (0.02 to 0.26)a0.09 (-0.03 to 0.21)-0.03 (-0.14 to 0.08)-0.07 (-0.18 to 0.04)0.09 (-0.03 to 0.21)0.05 (-0.07 to 0.17)
Close family or friend hospitalized or who died due to COVID-190.14 (0.03 to 0.26)a0.13 (0.02 to 0.25)a0.06 (-0.06 to 0.16)0.06 (-0.04 to 0.17)0.14 (0.02 to 0.26)a0.13 (0.01 to 0.24)a
Ethical conflict0.21 (0.03 to 0.39)a0.26 (0.08 to 0.44)b0.08 (-0.09 to 0.25)0.12 (-0.04 to 0.29)0.02 (-0.16 to 0.2)0.03 (-0.15 to 0.21)
Personal motivation-0.03 (-0.11 to 0.04)-0.02 (-0.09 to 0.06)-0.03 (-0.1 to 0.04)-0.02 (-0.09 to 0.05)-0.01 (-0.09 to 0.06)0.01 (-0.07 to 0.08)
Institutional support-0.26 (-0.34 to -0.18)c-0.26 (-0.33 to -0.18)c-0.41 (-0.49 to -0.33)c-0.41 (-0.48 to -0.34)c-0.2 (-0.28 to -0.12)c-0.2 (-0.28 to -0.13)c

The analysis revealed that institutional support presented a negative association with all dimensions of emotional distress (β = -0.26, P < 0.001; β = -0.41, P < 0.001; β = -0.22, P < 0.001). Personal motivation was not a significantly correlated variable with dimensions of emotional distress. Moreover, the final sociodemographic adjusted models indicated that participants with previous psychiatric or psychological treatments presented a significant likelihood of manifesting the three mental health dimensions (β = 0.33, 0.38, and 0.25, P < 0.001, respectively). Chi-squared tests were carried out to evaluate the association between this variable and scores of each used scale, showing a P value < 0.001 for all tests, which points out that pre-pandemic psychopathology was associated with higher rates of mental health outcomes (data not shown, available upon request).

In terms of events related to COVID-19 care, the dimension of avoidance and re-experience also presented a significant association with those HWs who had a close family member or friend who was hospitalized or died due to COVID-19 (β = 0.13, P < 0.05) and had experienced an ethical conflict during COVID-19 patient care (β = 0.26, P < 0.01). Furthermore, direct contact with COVID-19 patients was positively associated with the depression-anxiety dimension (β = 0.08; P < 0.01) and having had COVID-19 (β = 0.14; P < 0.05) was associated with avoidance and re-experience in the non-adjusted crude model.

Regarding sociodemographic predictors as data not shown, high educational level was negatively associated with all factors (P < 0.01; P < 0.01; P < 0.05). Age presented a negative association with factors 1 and 2 (P < 0.01; P < 0.001). Being a nurse was a significant factor associated with the first dimension (P < 0.01) when compared with being a medical doctor.


The mental health status of 1000 workers in a large COVID-19 reference hospital was assessed through self-reporting scales as applied online during the 2020 pandemic peak, a hectic period for HWs when exhausted professionals needed to continue to fight to stop the frightening level of deaths caused by COVID-19. Unsurprisingly, the results indicated a high frequency of depression, anxiety, stress, and burnout, as well as increased consumption of tobacco and alcohol, which were in line with reported rates found in previous studies[21-23]. Over one in four participants reported previous psychological or psychiatric treatment and an additional 14% of participants reported that they started treatment after the beginning of the pandemic. This incremental figure of HWs in need of care confirmed the vulnerability of this population to emotional distress during a demanding global health crisis. We showed a three-factor structure as a well-fit model for data variance of multiple co-occurring symptoms among HWs. Avoidance and re-experience, depression-anxiety, and sleep changes represent core dimensions of their prevalent emotional symptoms. Moreover, our findings suggested that the support of the organizational environment was the preventive intervention most associated with workers' emotional reactions. Also, professionals who had a close relative or friend present severe COVID-19 or had experienced an ethical challenge also presented a significant likelihood of association with the PTSD-like dimension of avoidance and re-experience. These results only include the suffering experience of a sample of HWs; however, their relevance should be examined in light of their fundamental role in the battle against the COVID-19 pandemic. Protecting their mental health by providing sufficient institutional support could make a difference to HWs’ well-being.

Although the method of factor analysis used for examining underlying dimensions of psychopathologies is a well-known technique for data reduction in psychiatry, we are aware of only one factorial study on the mental health of HWs during the pandemic. Chatterjee et al[24] conducted a factor analysis of distress among 140 Hindu HCWs and observed a four-factor structure: sleeplessness, anxiety, irritability, and hopelessness. In line with our findings, they also found that symptoms of sleep, anxiety, and depression play an important role in HWs’ distress. Unlike them, however, we found that stress-related responses were the most relevant dimension for data variance and sleep changes had the lowest impact. While our second factor included symptoms of depression and anxiety, symptoms such as hopelessness prevailed over irritability or anxiety. The difference could be explained by the fact that they selected a smaller sample size of participants, collected data in different stages of the pandemic course, and used specific instruments to assess only insomnia and perceived stress. Direct comparison between factorial models and their generalizability is not feasible.

Our dominant factor was the stress-related Avoidance and re-experience dimension, which was correlated with the depression-anxiety and sleep changes dimensions. This finding might support the argument that the current pandemic is considered a stressor event capable of triggering PTSD-like responses as well as worsening other related mental health problems such as depression and anxiety[25]. Furthermore, depressive, anxiety and burnout symptoms could be more associated with chronic stressors not directly related to COVID-19 care. Regarding externalizing behaviors, our questionnaire indicated that HWs increased their consumption of psychoactive substances, namely 7.6% tobacco and 17.1% alcohol. However, we did not include these variables in the analysis because of their weak contribution to the factorial model (communality < 0.10, data not shown). Hypothetically, HWs might be using more psychoactive substances to alleviate their distress[26]. This increased consumption is one of the aspects that might be bi-directionally related to their sleep problems[27], disturbing their sleep, or being a way of dealing with distress caused by inefficient rest. However, a more consistent investigation is warranted.

This study was conducted during the peak of the first wave of COVID-19 in Brazil, which was associated with the highest level of hospitalization of infected patients and produced an overwhelmingly stressful environment for HWs[28]. Our frequency of symptoms of traumatic events (46.8%) was similar to the rate of 49.4% found during the contagion peak in Italy[29]. The current data revealed that while working as a nurse was associated with the likelihood of presenting the avoidance and re-experience dimension, being a HW of older age and higher education level were both protective factors. These findings are in accordance with a recent systematic review that revealed that nurses facing pandemic crises experienced more stress when compared to doctors and that having more experience in healthcare work was a protective factor[30]. We also found that having had a close family member or friend hospitalized or die due to COVID-19 and having experienced an ethical conflict related to COVID-19 patient care had a significant positive association with this dimension. In this regard, recent studies have indicated that the loss of colleagues and dealing with ethical challenges in a time of acute resource shortages were associated with an increased risk of mental disorders[31-33]. Our results suggested that stress-related symptoms like avoidance and intrusive traumatic thoughts are a part of hospital professionals' emotional response to demanding conditions and adverse settings. Hence, it is recommended that hospital providers and administrators pay special attention to the occurrence of these symptoms among workers.

The second factor was labeled Depression-anxiety and the included items were taken from the PHQ-9, the GAD-7, and the burnout assessment. Previous studies have reported high levels of depression, anxiety, and burnout among HWs dealing with the pandemic[31,34,35]. Our findings were in accordance with these studies, showing that the retained dimension accounted for a substantial 24% of the data variance, with symptoms of hopelessness, anhedonia, and anergia being more represented than complaints of anxiety and burnout. A possible explanation for this dimension may be the important role of chronic stress in the workplace and decreased protective health actions for the development of psychological conditions among those professionals, such as high workload, changes in daily routine, reduced physical activity, scarcity of resources, and lack of rest[36-38]. Corroborating this proposition, our analysis did not find a significant correlation between acute threatening events and this dimension, which is also consistent with the suggestion that the association between direct contact with COVID-19 patients and anxiety and depression is based on weak evidence[23].

The third factor, Sleep changes, included three sleep-related items from the IES-R and the PHQ-9. Although no specific scale for screening sleep disorders was employed in this study, our factor analysis suggested an independent and unobservable sleep pattern, with the difficulty of falling or staying asleep having a high impact. This is consistent with previous studies[30,39,40] that have demonstrated an increased level of sleep problems among such professionals, with a frequency reaching 45%. A study describing the experience of supporting HWs in the current pandemic also reported a high frequency of sleep complaints and suggested specific support be provided for this condition[41]. Several aspects may be associated with these sleep changes, including physical exhaustion, quarantine, sleeping in unfamiliar places, separation from family, concerns about getting infected or infecting close contacts, and long work shifts[42,43]. Therefore, this sleep factor represents a neurovegetative dimension and may be triggered by other aspects apart from trauma-related stress, depression, anxiety, and burnout, which might justify its inclusion in a different emotional distress dimension.

Regarding coping strategies, the strongest finding was that aspects related to the organizational environment had a protective effect on overall emotional dimensions, which is in line with previous literature[44,45]. Amid a paucity of information on specific psychological interventions that could be useful to cope with the current pandemic[46], our findings provide some support to interventions that have already been applied in practice[47-51], such as providing adequate PPE and receiving adequate training, implementing an adequate and clear protocol for dealing with possible ethical conflicts, supporting HWs’ families, and providing enough rest time for workers to continue their job. For the purposes of the present paper, we only evaluated the institutional support during the peak of the first wave in Brazil. However, several studies have demonstrated concerns about chronic COVID-19 sequelae, which could be associated with mental health outcomes among other clinical conditions, requiring specific treatments and continuous aid[52,53]. Moreover, although altruistic acceptance of risk and support from family and friends have been considered protective coping factors in previous studies[30], our results did not confirm this relationship, showing any association between motivational coping strategies and emotional distress. However, this finding corroborates a recent study that did not find an association between adaptive coping strategies and symptoms of anxiety, depression, and stress[54]. A possible explanation for this is that we analyzed these aspects together as a personal motivation predictor, including items feeling recognized, motivated, and altruistic, which may enable a more consistent assessment of the role of all these variables in preventing the worsening of HWs’ well-being.

This study has some limitations. First, although our sample size was large enough, it was not representative of our institutional HWs, with a low response rate of 4.5%, and might be vulnerable to self-selection and response bias. Nevertheless, a good fit factorial model does not require a representative sample, but a large enough size with correlated items[19]. Second, self-reported online questionnaires were used, hence response bias may have occurred, where over or underreporting could not be ruled out. Third, considering the study design, we could not distinguish preexisting mental health symptoms from new-onset symptoms. Many participants self-reported previous history of mental disorders and treatment, but our rates clearly surpass the pre-pandemic level. Finally, because data were cross-sectionally collected in the baseline wave of an ongoing longitudinal study, causal relationships with predictors should not be stated definitively.


This factor analytical study of common psychological symptoms among HWs during the first wave of the current pandemic revealed that avoidance and re-experience, depression-anxiety, and sleep changes were the core reported manifestations. Institutional support was the most relevant protective aspect of the workers' well-being. Mental health professionals, health service administrators, and policy-makers should be mindful of the core dimensions of emotional distress of frontline workers and implement sound safeguarding measures. In the future, interventions should be tailored to improve occupational well-being in health services during subsequent waves of COVID-19 as well as possible forthcoming pandemic crises. Moreover, tracking the longitudinal course of HWs' reactions may help clarify their coping mechanisms for adversity.

Research background

The current pandemic has generated a dramatic challenge to public health, in a set of contextual changes throughout the world, including millions of deaths, the collapse of health systems, economic disruption, and food insecurity. During frontline service, hospital workers (HWs) were exposed to an increased risk of becoming infected, fear of infecting family members, ethical conflicts, overwhelming workload, among other stressors. Facing these stressors may contribute to a decline in their psychological well-being. Supporting this suggestion, high rates of depression, anxiety, stress, burnout, and insomnia have been reported among hospital professionals.

Research motivation

Several observational studies have described rates of common psychological responses of HWs facing the current pandemic. Nevertheless, few studies have examined the structure of multiple co-occurring symptoms through exploratory factor analysis. The data reduction approach is a potential asset to expand our understanding of how to prevent or reduce emotional distress in healthcare settings using a smaller number of variables.

Research objectives

We aimed to show core dimensions of common psychological symptoms as well as their associated predictors among HWs in a coronavirus disease 2019 (COVID-19) reference hospital.

Research methods

This is an observational study, and the data were cross-sectionally collected using an online survey during the first peak of the pandemic in Brazil. Data of 1000 HWs who completed the survey were analyzed (83.9% women and 34.3% aged 30 to 40). Self-reported symptoms of depression, anxiety, trauma-related stress, and burnout were subjected to exploratory factor analysis. Multiple linear regression models were then carried out to estimate predictors for each of the factors retained using questions on personal motivation, threatening events, and institutional support as independent variables.

Research results

HWs presented high rates of depression, anxiety, stress, and burnout during their frontline duty, as well as increased tobacco and alcohol consumption. The following three factors were the main dimensions of HWs’ distress: avoidance and re-experience, depression-anxiety, and sleep changes. Institutional support was the most significant protective factor for each of these dimensions. Furthermore, scores of the avoidance and re-experience dimension were associated with having a family member or a close friend with severe COVID-19 and having dealt with an ethical challenge. Contrary to expectation, participants’ personal motivation to work with COVID-19 patients was not associated with these factors.

Research conclusions

This factor analytic study revealed distressing dimensions of avoidance and re-experience, depression-anxiety, and sleep changes as the core psychological reactions of a sample of Brazilian HWs during the pandemic. It also highlighted the importance of institutional support in preventing a worsening of hospital professionals’ mental health during their pandemic service. These findings have implications for tailoring interventions to maintain HWs’ mental health.

Research perspectives

Data reduction methods, such as exploratory factor analysis, contribute to enlarging our understanding of the core psychological reactions of hospital professionals during a sanitary crisis. Multiple co-occurring symptoms can be clustered in a sound dimensional structure. In the future, institutional strategies based on these unobservable patterns could be planned to improve occupational well-being in health settings, either during subsequent waves of COVID-19 or during other future pandemic crises. Lastly, analyzing the longitudinal trajectory of the HWs’ reactions could help to elucidate coping mechanisms in similar stressful periods.


Provenance and peer review: Invited article; Externally peer reviewed.

Peer-review model: Single blind

Corresponding Author's Membership in Professional Societies: Regional Medical Council of São Paulo State, No. CRM-SP 59.946.

Specialty type: Psychiatry

Country/Territory of origin: Brazil

Peer-review report’s scientific quality classification

Grade A (Excellent): 0

Grade B (Very good): B, B

Grade C (Good): 0

Grade D (Fair): 0

Grade E (Poor): 0

P-Reviewer: El Sayed S, Egypt; Lal A, United States A-Editor: Liu X, China S-Editor: Gao CC L-Editor: A P-Editor: Gao CC

1.  Kang L, Li Y, Hu S, Chen M, Yang C, Yang BX, Wang Y, Hu J, Lai J, Ma X, Chen J, Guan L, Wang G, Ma H, Liu Z. The mental health of medical workers in Wuhan, China dealing with the 2019 novel coronavirus. Lancet Psychiatry. 2020;7:e14.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 1071]  [Cited by in F6Publishing: 769]  [Article Influence: 357.0]  [Reference Citation Analysis (0)]
2.  Chan AO, Huak CY. Psychological impact of the 2003 severe acute respiratory syndrome outbreak on health care workers in a medium size regional general hospital in Singapore. Occup Med (Lond). 2004;54:190-196.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 355]  [Cited by in F6Publishing: 373]  [Article Influence: 18.7]  [Reference Citation Analysis (0)]
3.  Ji D, Ji YJ, Duan XZ, Li WG, Sun ZQ, Song XA, Meng YH, Tang HM, Chu F, Niu XX, Chen GF, Li J, Duan HJ. Prevalence of psychological symptoms among Ebola survivors and healthcare workers during the 2014-2015 Ebola outbreak in Sierra Leone: a cross-sectional study. Oncotarget. 2017;8:12784-12791.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 135]  [Cited by in F6Publishing: 139]  [Article Influence: 22.5]  [Reference Citation Analysis (0)]
4.  Wu P, Fang Y, Guan Z, Fan B, Kong J, Yao Z, Liu X, Fuller CJ, Susser E, Lu J, Hoven CW. The psychological impact of the SARS epidemic on hospital employees in China: exposure, risk perception, and altruistic acceptance of risk. Can J Psychiatry. 2009;54:302-311.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 987]  [Cited by in F6Publishing: 980]  [Article Influence: 70.5]  [Reference Citation Analysis (0)]
5.  Lai J, Ma S, Wang Y, Cai Z, Hu J, Wei N, Wu J, Du H, Chen T, Li R, Tan H, Kang L, Yao L, Huang M, Wang H, Wang G, Liu Z, Hu S. Factors Associated With Mental Health Outcomes Among Health Care Workers Exposed to Coronavirus Disease 2019. JAMA Netw Open. 2020;3:e203976.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 4130]  [Cited by in F6Publishing: 3932]  [Article Influence: 1376.7]  [Reference Citation Analysis (0)]
6.  Salari N, Khazaie H, Hosseinian-Far A, Khaledi-Paveh B, Kazeminia M, Mohammadi M, Shohaimi S, Daneshkhah A, Eskandari S. The prevalence of stress, anxiety and depression within front-line healthcare workers caring for COVID-19 patients: a systematic review and meta-regression. Hum Resour Health. 2020;18:100.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 157]  [Cited by in F6Publishing: 164]  [Article Influence: 52.3]  [Reference Citation Analysis (0)]
7.  Barello S, Palamenghi L, Graffigna G. Burnout and somatic symptoms among frontline healthcare professionals at the peak of the Italian COVID-19 pandemic. Psychiatry Res. 2020;290:113129.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 329]  [Cited by in F6Publishing: 335]  [Article Influence: 109.7]  [Reference Citation Analysis (0)]
8.  Osório FL, Silveira ILM, Pereira-Lima K, Crippa JAS, Hallak JEC, Zuardi AW, Loureiro SR. Risk and Protective Factors for the Mental Health of Brazilian Healthcare Workers in the Frontline of COVID-19 Pandemic. Front Psychiatry. 2021;12:662742.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 12]  [Cited by in F6Publishing: 13]  [Article Influence: 6.0]  [Reference Citation Analysis (0)]
9.  State Health Departments  Web site. 2020. [cited 29 Sep 2021]. Available from: https://www.covid.saude.gov.br.  [PubMed]  [DOI]  [Cited in This Article: ]
10.  Caiuby AV, Lacerda SS, Quintana MI, Torii TS, Andreoli SB. [Cross-cultural adaptation of the Brazilian version of the Impact of Events Scale-Revised (IES-R)]. Cad Saude Publica. 2012;28:597-603.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 24]  [Cited by in F6Publishing: 28]  [Article Influence: 2.2]  [Reference Citation Analysis (1)]
11.  Santos IS, Tavares BF, Munhoz TN, Almeida LS, Silva NT, Tams BD, Patella AM, Matijasevich A. [Sensitivity and specificity of the Patient Health Questionnaire-9 (PHQ-9) among adults from the general population]. Cad Saude Publica. 2013;29:1533-1543.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 6]  [Cited by in F6Publishing: 86]  [Article Influence: 0.7]  [Reference Citation Analysis (0)]
12.  Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16:606-613.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 21545]  [Cited by in F6Publishing: 22235]  [Article Influence: 979.3]  [Reference Citation Analysis (0)]
13.  Bártolo A, Monteiro S, Pereira A. Factor structure and construct validity of the Generalized Anxiety Disorder 7-item (GAD-7) among Portuguese college students. Cad Saude Publica. 2017;33:e00212716.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 41]  [Cited by in F6Publishing: 48]  [Article Influence: 6.8]  [Reference Citation Analysis (0)]
14.  Spitzer RL, Kroenke K, Williams JB, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006;166:1092-1097.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 11947]  [Cited by in F6Publishing: 12724]  [Article Influence: 702.8]  [Reference Citation Analysis (0)]
15.  Rohland BM, Kruse GR, Rohrer JE. Validation of a single-item measure of burnout against the Maslach Burnout Inventory among physicians. Stress Health. 2004;20:75-79.  [PubMed]  [DOI]  [Cited in This Article: ]
16.  Dolan ED, Mohr D, Lempa M, Joos S, Fihn SD, Nelson KM, Helfrich CD. Using a single item to measure burnout in primary care staff: a psychometric evaluation. J Gen Intern Med. 2015;30:582-587.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 253]  [Cited by in F6Publishing: 249]  [Article Influence: 31.6]  [Reference Citation Analysis (0)]
17.  Kachadourian LK, Feder A, Murrough JW, Feingold JH, Kaye-Kauderer H, Charney D, Southwick SM, Peccoralo L, Ripp J, Pietrzak RH. Transdiagnostic Psychiatric Symptoms, Burnout, and Functioning in Frontline Health Care Workers Responding to the COVID-19 Pandemic: A Symptomics Analysis. J Clin Psychiatry. 2021;82.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 10]  [Cited by in F6Publishing: 10]  [Article Influence: 5.0]  [Reference Citation Analysis (0)]
18.  Guinan JJ, McCallum LW, Painter L, Dykes J, Gold J. Stressors and rewards of being an AIDS emotional-support volunteer: a scale for use by care-givers for people with AIDS. AIDS Care. 1991;3:137-150.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 22]  [Cited by in F6Publishing: 22]  [Article Influence: 0.7]  [Reference Citation Analysis (0)]
19.  Hair JF, Black WC, Babin BJ, Anderson RE.   Multivariate Data Analysis. 7th ed. United Kingdom: Pearson, 2013: 90-147.  [PubMed]  [DOI]  [Cited in This Article: ]
20.  Wetzel AP. Factor analysis methods and validity evidence: a review of instrument development across the medical education continuum. Acad Med. 2012;87:1060-1069.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 38]  [Cited by in F6Publishing: 42]  [Article Influence: 3.5]  [Reference Citation Analysis (0)]
21.  Ide K, Asami T, Suda A, Yoshimi A, Fujita J, Nomoto M, Roppongi T, Hino K, Takahashi Y, Watanabe K, Shimada T, Hamasaki T, Endo E, Kaneko T, Suzuki M, Kubota K, Saigusa Y, Kato H, Odawara T, Nakajima H, Takeuchi I, Goto T, Aihara M, Hishimoto A. The psychological effects of COVID-19 on hospital workers at the beginning of the outbreak with a large disease cluster on the Diamond Princess cruise ship. PLoS One. 2021;16:e0245294.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 9]  [Cited by in F6Publishing: 12]  [Article Influence: 4.5]  [Reference Citation Analysis (0)]
22.  Robles R, Rodríguez E, Vega-Ramírez H, Álvarez-Icaza D, Madrigal E, Durand S, Morales-Chainé S, Astudillo C, Real-Ramírez J, Medina-Mora ME, Becerra C, Escamilla R, Alcocer-Castillejos N, Ascencio L, Díaz D, González H, Barrón-Velázquez E, Fresán A, Rodríguez-Bores L, Quijada-Gaytán JM, Zabicky G, Tejadilla-Orozco D, González-Olvera JJ, Reyes-Terán G. Mental health problems among healthcare workers involved with the COVID-19 outbreak. Braz J Psychiatry. 2021;43:494-503.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 20]  [Cited by in F6Publishing: 24]  [Article Influence: 6.7]  [Reference Citation Analysis (0)]
23.  Li Y, Scherer N, Felix L, Kuper H. Prevalence of depression, anxiety and post-traumatic stress disorder in health care workers during the COVID-19 pandemic: A systematic review and meta-analysis. PLoS One. 2021;16:e0246454.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 138]  [Cited by in F6Publishing: 155]  [Article Influence: 69.0]  [Reference Citation Analysis (0)]
24.  Chatterjee SS, Chakrabarty M, Banerjee D, Grover S, Chatterjee SS, Dan U. Stress, Sleep and Psychological Impact in Healthcare Workers During the Early Phase of COVID-19 in India: A Factor Analysis. Front Psychol. 2021;12:611314.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 27]  [Cited by in F6Publishing: 28]  [Article Influence: 13.5]  [Reference Citation Analysis (0)]
25.  Bridgland VME, Moeck EK, Green DM, Swain TL, Nayda DM, Matson LA, Hutchison NP, Takarangi MKT. Why the COVID-19 pandemic is a traumatic stressor. PLoS One. 2021;16:e0240146.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 134]  [Cited by in F6Publishing: 148]  [Article Influence: 67.0]  [Reference Citation Analysis (0)]
26.  Cheetham A, Allen NB, Yücel M, Lubman DI. The role of affective dysregulation in drug addiction. Clin Psychol Rev. 2010;30:621-634.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 186]  [Cited by in F6Publishing: 179]  [Article Influence: 14.3]  [Reference Citation Analysis (0)]
27.  Pieters S, Burk WJ, Van der Vorst H, Dahl RE, Wiers RW, Engels RC. Prospective relationships between sleep problems and substance use, internalizing and externalizing problems. J Youth Adolesc. 2015;44:379-388.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 69]  [Cited by in F6Publishing: 64]  [Article Influence: 7.7]  [Reference Citation Analysis (0)]
28.  Mira JJ, Carrillo I, Guilabert M, Mula A, Martin-Delgado J, Pérez-Jover MV, Vicente MA, Fernández C; SARS-CoV-2 Second Victim Study Group. Acute stress of the healthcare workforce during the COVID-19 pandemic evolution: a cross-sectional study in Spain. BMJ Open. 2020;10:e042555.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 52]  [Cited by in F6Publishing: 51]  [Article Influence: 17.3]  [Reference Citation Analysis (0)]
29.  Rossi R, Socci V, Pacitti F, Di Lorenzo G, Di Marco A, Siracusano A, Rossi A. Mental Health Outcomes Among Frontline and Second-Line Health Care Workers During the Coronavirus Disease 2019 (COVID-19) Pandemic in Italy. JAMA Netw Open. 2020;3:e2010185.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 485]  [Cited by in F6Publishing: 464]  [Article Influence: 161.7]  [Reference Citation Analysis (0)]
30.  Chigwedere OC, Sadath A, Kabir Z, Arensman E. The Impact of Epidemics and Pandemics on the Mental Health of Healthcare Workers: A Systematic Review. Int J Environ Res Public Health. 2021;18.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 29]  [Cited by in F6Publishing: 30]  [Article Influence: 14.5]  [Reference Citation Analysis (0)]
31.  Sahebi A, Nejati-Zarnaqi B, Moayedi S, Yousefi K, Torres M, Golitaleb M. The prevalence of anxiety and depression among healthcare workers during the COVID-19 pandemic: An umbrella review of meta-analyses. Prog Neuropsychopharmacol Biol Psychiatry. 2021;107:110247.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 123]  [Cited by in F6Publishing: 123]  [Article Influence: 61.5]  [Reference Citation Analysis (0)]
32.  Sperling D. Nurses' challenges, concerns and unfair requirements during the COVID-19 outbreak. Nurs Ethics. 2021;28:1096-1110.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 9]  [Cited by in F6Publishing: 10]  [Article Influence: 4.5]  [Reference Citation Analysis (0)]
33.  Gebreheat G, Teame H. Ethical Challenges of Nurses in COVID-19 Pandemic: Integrative Review. J Multidiscip Healthc. 2021;14:1029-1035.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 14]  [Cited by in F6Publishing: 14]  [Article Influence: 7.0]  [Reference Citation Analysis (0)]
34.  Gualano MR, Sinigaglia T, Lo Moro G, Rousset S, Cremona A, Bert F, Siliquini R. The Burden of Burnout among Healthcare Professionals of Intensive Care Units and Emergency Departments during the COVID-19 Pandemic: A Systematic Review. Int J Environ Res Public Health. 2021;18.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 37]  [Cited by in F6Publishing: 46]  [Article Influence: 18.5]  [Reference Citation Analysis (0)]
35.  Faria de Moura Villela E, Rodrigues da Cunha I, Nelson Siewe Fodjo J, Obimpeh M, Colebunders R, Van Hees S. Impact of COVID-19 on Healthcare Workers in Brazil between August and November 2020: A Cross-Sectional Survey. Int J Environ Res Public Health. 2021;18.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 8]  [Cited by in F6Publishing: 10]  [Article Influence: 4.0]  [Reference Citation Analysis (0)]
36.  Franck E, Haegdorens F, Goossens E, van Gils Y, Portzky M, Somville F, Abuawad M, Slootmans S, Van Bogaert P. The Role of Coping Behavior in Healthcare Workers' Distress and Somatization During the COVID-19 Pandemic. Front Psychol. 2021;12:684618.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 5]  [Cited by in F6Publishing: 5]  [Article Influence: 2.5]  [Reference Citation Analysis (0)]
37.  Mokhtari R, Moayedi S, Golitaleb M. COVID-19 pandemic and health anxiety among nurses of intensive care units. Int J Ment Health Nurs. 2020;29:1275-1277.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 30]  [Cited by in F6Publishing: 25]  [Article Influence: 10.0]  [Reference Citation Analysis (0)]
38.  Şanlıtürk D. Perceived and sources of occupational stress in intensive care nurses during the COVID-19 pandemic. Intensive Crit Care Nurs. 2021;67:103107.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 11]  [Cited by in F6Publishing: 8]  [Article Influence: 5.5]  [Reference Citation Analysis (0)]
39.  da Silva FCT, Neto MLR. Psychiatric symptomatology associated with depression, anxiety, distress, and insomnia in health professionals working in patients affected by COVID-19: A systematic review with meta-analysis. Prog Neuropsychopharmacol Biol Psychiatry. 2021;104:110057.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 89]  [Cited by in F6Publishing: 85]  [Article Influence: 44.5]  [Reference Citation Analysis (0)]
40.  Qi J, Xu J, Li BZ, Huang JS, Yang Y, Zhang ZT, Yao DA, Liu QH, Jia M, Gong DK, Ni XH, Zhang QM, Shang FR, Xiong N, Zhu CL, Wang T, Zhang X. The evaluation of sleep disturbances for Chinese frontline medical workers under the outbreak of COVID-19. Sleep Med. 2020;72:1-4.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 92]  [Cited by in F6Publishing: 80]  [Article Influence: 30.7]  [Reference Citation Analysis (0)]
41.  Fukuti P, Uchôa CLM, Mazzoco MF, Cruz IDGD, Echegaray MVF, Humes EC, Silveira JB, Santi TD, Miguel EC, Corchs F; COMVC-19 program, Fatori D, Campello G, Oliveira GM, Argolo FC, Ferreira FM, Machado G, Argeu A, Oliveira GMR, Serafim AP, Siqueira LL, Rossi L, Rios IC, Oliveira TR, Antoniazzi LCK, Gagliotti DAM, Abelama Neto E, Oliveira Junior PN, Correia AV, Gonçalves LS, Tortato LS, Busato WMM, Guimarães-Fernandes F, Alves M, Leite Netto OF, Schoueri PCL, Roque MA, Merlin SS, Boer GCM, Sallet PC, Malbergier A, Spedo MA, Kamitsuji CS, Faria E, Moreira MVG, Kaufman A, Abdo C, Scanavino MT, Lancman S, Tavares H, Polanczyk G, Brunoni AR, Forlenza OV, Barros-Filho TEP. COMVC-19: A Program to protect healthcare workers' mental health during the COVID-19 Pandemic. What we have learned. Clinics (Sao Paulo). 2021;76:e2631.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 2]  [Cited by in F6Publishing: 5]  [Article Influence: 1.0]  [Reference Citation Analysis (1)]
42.  Koinis A, Giannou V, Drantaki V, Angelaina S, Stratou E, Saridi M. The Impact of Healthcare Workers Job Environment on Their Mental-emotional Health. Coping Strategies: The Case of a Local General Hospital. Health Psychol Res. 2015;3:1984.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 81]  [Cited by in F6Publishing: 88]  [Article Influence: 10.1]  [Reference Citation Analysis (0)]
43.  Zhang C, Yang L, Liu S, Ma S, Wang Y, Cai Z, Du H, Li R, Kang L, Su M, Zhang J, Liu Z, Zhang B. Survey of Insomnia and Related Social Psychological Factors Among Medical Staff Involved in the 2019 Novel Coronavirus Disease Outbreak. Front Psychiatry. 2020;11:306.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 375]  [Cited by in F6Publishing: 384]  [Article Influence: 125.0]  [Reference Citation Analysis (0)]
44.  Htay MNN, Marzo RR, Bahari R, AlRifai A, Kamberi F, El-Abasiri RA, Nyamache JM, Hlaing HA, Hassanein M, Moe S, Abas AL, Su TT. How healthcare workers are coping with mental health challenges during COVID-19 pandemic? Clin Epidemiol Glob Health. 2021;11:100759.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 19]  [Cited by in F6Publishing: 25]  [Article Influence: 9.5]  [Reference Citation Analysis (0)]
45.  De Kock JH, Latham HA, Leslie SJ, Grindle M, Munoz SA, Ellis L, Polson R, O'Malley CM. A rapid review of the impact of COVID-19 on the mental health of healthcare workers: implications for supporting psychological well-being. BMC Public Health. 2021;21:104.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 238]  [Cited by in F6Publishing: 264]  [Article Influence: 119.0]  [Reference Citation Analysis (0)]
46.  Buselli R, Corsi M, Veltri A, Baldanzi S, Chiumiento M, Lupo ED, Marino R, Necciari G, Caldi F, Foddis R, Guglielmi G, Cristaudo A. Mental health of Health Care Workers (HCWs): a review of organizational interventions put in place by local institutions to cope with new psychosocial challenges resulting from COVID-19. Psychiatry Res. 2021;299:113847.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 43]  [Cited by in F6Publishing: 45]  [Article Influence: 21.5]  [Reference Citation Analysis (0)]
47.  Rieckert A, Schuit E, Bleijenberg N, Ten Cate D, de Lange W, de Man-van Ginkel JM, Mathijssen E, Smit LC, Stalpers D, Schoonhoven L, Veldhuizen JD, Trappenburg JC. How can we build and maintain the resilience of our health care professionals during COVID-19? BMJ Open. 2021;11:e043718.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 49]  [Cited by in F6Publishing: 52]  [Article Influence: 24.5]  [Reference Citation Analysis (0)]
48.  Appelbom S, Bujacz A, Finnes A, Ahlbeck K, Bromberg F, Holmberg J, Larsson L, Olgren B, Wanecek M, Wetterborg D, Wicksell R. The Rapid Implementation of a Psychological Support Model for Frontline Healthcare Workers During the COVID-19 Pandemic: A Case Study and Process Evaluation. Front Psychiatry. 2021;12:713251.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 8]  [Cited by in F6Publishing: 7]  [Article Influence: 4.0]  [Reference Citation Analysis (0)]
49.  Gonzalez A, Cervoni C, Lochner M, Marangio J, Stanley C, Marriott S. Supporting health care workers during the COVID-19 pandemic: Mental health support initiatives and lessons learned from an academic medical center. Psychol Trauma. 2020;12:S168-S170.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 27]  [Cited by in F6Publishing: 27]  [Article Influence: 9.0]  [Reference Citation Analysis (0)]
50.  Fukuti P, Uchôa CLM, Mazzoco MF, Corchs F, Kamitsuji CS, Rossi L, Rios IC, Lancman S, Bonfa E, Barros-Filho TEP, Miguel EC. How Institutions Can Protect the Mental Health and Psychosocial Well-Being of Their Healthcare Workers in the Current COVID-19 Pandemic. Clinics (Sao Paulo). 2020;75:e1963.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 23]  [Cited by in F6Publishing: 27]  [Article Influence: 7.7]  [Reference Citation Analysis (0)]
51.  Pollock A, Campbell P, Cheyne J, Cowie J, Davis B, McCallum J, McGill K, Elders A, Hagen S, McClurg D, Torrens C, Maxwell M. Interventions to support the resilience and mental health of frontline health and social care professionals during and after a disease outbreak, epidemic or pandemic: a mixed methods systematic review. Cochrane Database Syst Rev. 2020;11:CD013779.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 94]  [Cited by in F6Publishing: 129]  [Article Influence: 31.3]  [Reference Citation Analysis (0)]
52.  Visco V, Vitale C, Rispoli A, Izzo C, Virtuoso N, Ferruzzi GJ, Santopietro M, Melfi A, Rusciano MR, Maglio A, Di Pietro P, Carrizzo A, Galasso G, Vatrella A, Vecchione C, Ciccarelli M. Post-COVID-19 Syndrome: Involvement and Interactions between Respiratory, Cardiovascular and Nervous Systems. J Clin Med. 2022;11.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 34]  [Cited by in F6Publishing: 33]  [Article Influence: 34.0]  [Reference Citation Analysis (0)]
53.  Tirozzi A, Santonastaso F, de Gaetano G, Iacoviello L, Gialluisi A. Does COVID-19 increase the risk of neuropsychiatric sequelae? World J Psychiatry. 2022;12:536-540.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in CrossRef: 3]  [Cited by in F6Publishing: 2]  [Article Influence: 3.0]  [Reference Citation Analysis (0)]
54.  Sampogna G, Del Vecchio V, Giallonardo V, Luciano M, Albert U, Carmassi C, Carrà G, Cirulli F, Dell'Osso B, Menculini G, Nanni M, Pompili M, Sani G, Volpe U, Bianchini V, Fiorillo A. What Is the Role of Resilience and Coping Strategies on the Mental Health of the General Population during the COVID-19 Pandemic? Brain Sci. 2021;11.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 18]  [Cited by in F6Publishing: 21]  [Article Influence: 9.0]  [Reference Citation Analysis (0)]