What Mattered Most: Personal, Work-Related, and Psychopathological Characteristics Associated with Healthcare Workers’ Impairment of Functioning during COVID-19

Abstract

Background: The COVID-19 pandemic greatly impacted healthcare workers (HWs) around the world. Italy was the first Western country hit by the pandemic, and several studies have been published targeting the mental health burden held by Italian HWs. Notwithstanding, only a few studies focused on the impact of COVID-19 on HWs’ levels of functioning. Methods: An online survey was distributed to HWs in Italy through physicians’ and nurses’ associations, social networks, and researchers’ direct contacts, between 4 April and 13 May 2020. Participants provided sociodemographic, work-related, and pandemic-related data and filled out a set of psychometric questionnaires (Patient Health Questionnaire-9—PHQ-9, General Anxiety Disorder-7—GAD-7, Impact of Event Scale—Revised—IES-R, and Work and Social Adjustment Scale—WSAS). Results: The final sample included 1041 HWs (mean age 45.01 ± 11.62, 63.9% females). In total, 58.1% of the subjects screened positive on the GAD-7, 27.5% on the PHQ-9, and 25.9% on the IES-R. Furthermore, 67.4% showed a significant level of impairment in functioning according to the WSAS, while 35.8% reached scores of moderate or worse impairment. In the multiple linear regressions, screening positive on any of the psychometric scales and being exposed to unusual suffering significantly predicted worse scores in all WSAS domains (p < 0.05). Having a history of mental disorders significantly predicted worse scores in the WSAS domain of work ability (p = 0.002), while being the parent of children younger than 18 years significantly predicted worse WSAS family functioning scores (p < 0.001). Conclusions: Our results corroborate extant data about the impact of the COVID-19 pandemic on HWs’ mental health and shed light on its detrimental effect on functioning. Tailored interventions should be designed in order to support HWs during times of crisis.

Full text

Citation: Gesi, C.; Cafaro, R.; Cerioli,
M.; Achilli, F.; Boscacci, M.; Cirnigliaro,
G.; Dell’Osso, B. What Mattered Most:
Personal, Work-Related, and
Psychopathological Characteristics
Associated with Healthcare Workers’
Impairment of Functioning during
COVID-19. J. Clin. Med. 2024,13, 5821.
https://doi.org/10.3390/jcm13195821
Academic Editor: Michele Roccella
Received: 26 August 2024
Revised: 26 September 2024
Accepted: 27 September 2024
Published: 29 September 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Journal of
Clinical Medicine
Article
What Mattered Most: Personal, Work-Related, and
Psychopathological Characteristics Associated with Healthcare
Workers’ Impairment of Functioning during COVID-19
Camilla Gesi 1, Rita Cafaro 1,2 , Matteo Cerioli 1, 2, * , Francesco Achilli 1,2 , Maria Boscacci 1,2,
Giovanna Cirnigliaro 1,2 and Bernardo Dell’Osso 1,2,3,4
1Department of Mental Health and Addiction, ASST Fatebenefratelli-Sacco, 20157 Milan, Italy;
camilla.gesi@asst-fbf-sacco.it (C.G.); rita.cafaro@unimi.it (R.C.); achilli.francesco@asst-fbf-sacco.it (F.A.);
maria.boscacci@asst-fbf-sacco.it (M.B.); giovanna.cirnigliaro@asst-fbf-sacco.it (G.C.);
bernardo.dellosso@unimi.it (B.D.)
2Department of Biomedical and Clinical Sciences “Luigi Sacco”, University of Milan, 20157 Milan, Italy
3Department of Psychiatry and Behavioural Sciences, Stanford University, Stanford, CA 94305, USA
4CRC “Aldo Ravelli” for Neurotechnology and Experimental Brain Therapeutics, University of Milan,
20122 Milan, Italy
*Correspondence: matteo.cerioli@unimi.it; Tel.: +39-0239042904
Abstract: Background: The COVID-19 pandemic greatly impacted healthcare workers (HWs) around
the world. Italy was the first Western country hit by the pandemic, and several studies have been
published targeting the mental health burden held by Italian HWs. Notwithstanding, only a few
studies focused on the impact of COVID-19 on HWs’ levels of functioning. Methods: An online
survey was distributed to HWs in Italy through physicians’ and nurses’ associations, social networks,
and researchers’ direct contacts, between 4 April and 13 May 2020. Participants provided sociodemo-
graphic, work-related, and pandemic-related data and filled out a set of psychometric questionnaires
(Patient Health Questionnaire-9—PHQ-9, General Anxiety Disorder-7—GAD-7, Impact of Event
Scale—Revised—IES-R, and Work and Social Adjustment Scale—WSAS). Results: The final sample
included 1041 HWs (mean age 45.01
±
11.62, 63.9% females). In total, 58.1% of the subjects screened
positive on the GAD-7, 27.5% on the PHQ-9, and 25.9% on the IES-R. Furthermore, 67.4% showed a
significant level of impairment in functioning according to the WSAS, while 35.8% reached scores
of moderate or worse impairment. In the multiple linear regressions, screening positive on any of
the psychometric scales and being exposed to unusual suffering significantly predicted worse scores
in all WSAS domains (p< 0.05). Having a history of mental disorders significantly predicted worse
scores in the WSAS domain of work ability (p= 0.002), while being the parent of children younger than
18 years significantly predicted worse WSAS family functioning scores (p< 0.001). Conclusions: Our
results corroborate extant data about the impact of the COVID-19 pandemic on HWs’ mental health
and shed light on its detrimental effect on functioning. Tailored interventions should be designed in
order to support HWs during times of crisis.
Keywords: levels of functioning; post-traumatic stress symptoms; healthcare workers; COVID-19
pandemic
1. Introduction
In February 2020, Italy became the first European hotbed of the Coronavirus Diseases
(COVID-19), firstly reported between November and December 2019 in Wuhan (China).
After a few weeks, the epidemic spread across the entire country, exerting an unprecedented
pressure on Italian hospitals and their professional staff. Hospital overcrowding and the
overwhelming workload have been abundantly described in the countries hardest hit [
1
,
2
].
Healthcare workers (HWs) worldwide have endured a great number of stressors, from the
J. Clin. Med. 2024,13, 5821. https://doi.org/10.3390/jcm13195821 https://www.mdpi.com/journal/jcm

J. Clin. Med. 2024,13, 5821 2 of 11
risk of being infected during work, to grueling work-shifts, to the exposure to an amount of
suffering and death that was most likely unrivaled in their career until then. Several studies
have shown that the COVID-19 pandemic, especially during the first wave, fostered feelings
of threat, uncertainty, inadequacy, and fatigue among HWs as they had to come to terms
with infection risks, changes in work tasks, worries about standards of care, lack of effective
treatment or treatment guidelines, scarce or improper equipment, increased workload, and
inconsistent messages from the government, the media, and hospital managers [
3
–
5
]. In
this scenario, HWs became easily vulnerable to a broad range of psychiatric symptoms,
including anxiety, depression, sleep disturbances, and post-traumatic stress symptoms,
arising during or after the pandemic [
6
–
9
]. Since the COVID-19 pandemic started, several
online surveys have been undertaken to evaluate the consequences on HW’s mental health,
assessing diverse psychopathological dimensions [
10
]. It has been documented that HWs
working in emergency care have been at high risk of developing post-traumatic stress
disorder (PTSD), with prevalence estimates as high as 20%, especially within the Intensive
Care Unit (ICU) workforce [
11
–
13
]. According to the most recent meta-analysis in the
literature [
14
], which included 401 studies up to February 2022, the pooled prevalence of
depression was 28.5%, while anxiety had a prevalence of 28.7%, followed by alcohol and
substance use disorder (25.3%), and insomnia (24.4%). Higher odds of negative mental
health outcomes were mostly associated with female gender, working on the frontline, and
working in high-risk units [15].
Despite the mounting evidence documenting the large psychological response of
HWs to the pandemic, studies investigating the extent of functional impairment among
HWs are scant. According to Bosc, functioning implies the ability of an individual to
weave effective interactions with their environment, whether it be work, family, social
activities, or emotional relationships [
16
]. In fact, the assessment of functioning provides
a comprehensive measure of a person’s ability to accomplish his or her roles, summing
the contribution of several underlying causes, from personal life to work-related factors,
from individual stress responses to mental resilience. Conversely, some literature points
out the need to foster research and educational programs designed to enhance resilience
and posttraumatic growth (PTGI) in HWs so as to be beneficial to their ability to respond to
challenges in times of crisis and to experience positive psychological changes in different
domains of the human experience (e.g., relating to others, personal strength, spiritual
change, new opportunities, and understanding of life) [
17
,
18
]. Moreover, the assessment of
functioning is essential to determine the clinical relevance of a constellation of psychological
symptoms. Thus, despite the fact that the concept of functioning is complex and that
the definitions may stand out as somewhat overlapping [
16
,
19
–
21
], assessing functional
impairment may still provide guidance to disentangle factors that deserve to be addressed
by tailored intervention from those that do not.
The aim of this paper is to assess the impairment along different dimensions of
functioning among HWs during the first wave of COVID-19 in Italy. By exploring a broad
set of sociodemographic, COVID-19-related, and work-related variables and the prevalence
of depressive, anxiety, and post-traumatic symptoms, we tried to identify factors that were
associated with higher rates of functioning impairment. A better understanding of the
impact of both work-related and non-work determinants on HWs’ functioning may help
us provide specific interventions in their support.
2. Materials and Methods
2.1. Study Design and Participants
In this cross-sectional study, we collected and analyzed answers from an online survey
submitted to Italian healthcare workers (HWs) during the first wave of the COVID-19
pandemic. Data were collected between 4 April and 13 May in 2020, the late phase of the
first stay-at-home order in Italy. An invitation to take part in the survey was addressed to
HWs actively working in Italy during this period. It was sent to healthcare institutions,
associations, and social network groups along with an invitation letter including a short

J. Clin. Med. 2024,13, 5821 3 of 11
presentation of the project. There was no exclusion in relation to rehabilitation, diagnostic,
and administrative professionals. Snowball sampling was also allowed. Participants gave
their informed consent to participate in the study and to have their personal data used for
research purposes. Their answers were collected anonymously. The study was conducted
in accordance with the principles stated in the Declaration of Helsinki. The study protocol
was approved by the Department of Psychiatry of the ASST Fatebenefratelli-Sacco of Milan
as the relevant institutional review board for low-risk studies (code: dsm 12-20).
2.2. Survey Design
The first part of the survey aimed to investigate sociodemographic (e.g., gender, age,
marital, and parenthood status) and work-related variables (e.g., occupation, job seniority)
and changes that occurred at home or in the workplace due to COVID-19 (e.g., changes
in cohabitation status, relocation to another hospital unit). COVID-19 cases within the
family or among co-workers were also assessed in this section of the survey. Previous
history of mental disorders was assessed by means of a single, self-assessed question. The
variable “Exposure to unusual suffering” was meant to investigate whether participants
were exposed to extraordinary patients’ suffering due to COVID-19. The remaining part
was meant to assess a range of psychiatric symptoms by means of a set of validated
psychometric questionnaires.
2.3. Assessment of Anxiety Symptoms
The General Anxiety Disorder 7 (GAD-7) was used to assess the severity of anxiety
symptoms. The questionnaire evaluates the presence of anxiety symptoms in the previous
two weeks through seven items, scoring on a 4-point Likert scale. The GAD-7 has been
extensively validated in the general population [
22
] and largely used to assess anxiety
symptoms in healthcare providers during the COVID-19 pandemic [
23
]. In proceeding
with previous literature, a cut-off score of 10 was used to assign a provisional diagnosis of
generalized anxiety disorder [24].
2.4. Assessment of Depressive Symptoms
Depression was evaluated with the Patient Health Questionnaire-9 (PHQ-9), a nine-
item self-report questionnaire assessing depressive symptoms in the last 15 days [
25
]. Each
item is rated on a 4-point scale, with total scores ranging from 0 to 27. In accordance with
the literature, a score of 10 or above is a reliable cut-off to assign a provisional diagnosis of
major depression [
26
]. The PHQ-9 has been largely used as a screening tool for depression
in primary care settings, as well as to assess depressive symptoms in healthcare workers
during COVID-19 [27,28].
2.5. Assessment of Post-Traumatic Symptoms
Participants were asked to answer a gate question about possible traumatic events
experienced during or because of the pandemic. Subjects providing a positive answer
were invited to briefly describe the event and to fill out the Impact of Event Scale-Revised
(IES-R) [29]. The scale consists of 22 items, scored on a five-point Likert scale and divided
into three subscales (investigating intrusion, avoidance, and hyperarousal symptoms in the
last 15 days). A score of 33 or higher on the IES-R is suggestive of a provisional diagnosis of
PTSD [
30
]. While the IES-R is not meant to be a diagnostic tool, a total score of 33 has been
indicated as having good diagnostic sensitivity (0.91) and specificity (0.82) compared to a
clinical diagnosis of post-traumatic stress disorder (PTSD) [
30
,
31
]. The Italian version has
also shown optimal psychometric properties and validity [
32
]. The IES-R has already been
validated to assess PTSD during the COVID-19 pandemic [
33
]. In our sample, the IES-R
showed good reliability (Cronbach’s alpha = 0.93). In the present study, subjects answering
positive to the gate question and receiving a score of 33 or higher were given a provisional
diagnosis of PTSD.

J. Clin. Med. 2024,13, 5821 4 of 11
2.6. Assessment of Impairment of Functioning
HWs’ impairment of functioning was evaluated by means of the Work and Social Ad-
justment Scale (WSAS), a five-item instrument that has been extensively used and validated
to assess the impact of psychiatric disorders on work and social functioning [
34
,
35
]. The
first item aims to assess working ability, the second home management, the third and the
fourth items social and private recreational activities, whereas the last item concerns the
impairment within the family and other close relationships. Each item can score from 0 to
8, with lower scores reflecting better functioning, and a maximum total score of 40. Among
subjects with depression, a WSAS score between 10 and 20 relates to a significant functional
impairment, while a score above 20 indicates moderately/severe or worse impairment [
34
].
2.7. Statistical Analysis
Statistical analyses were performed using the Statistical Package for Social Science,
version 27.0 (IBM Corp. Released 2020, Armonk, NY, USA). Continuous and categorical
variables were reported respectively as the mean
±
standard deviation (SD) and total
number and percentage. Student’s ttest was used to compare WSAS scores across a series
of dichotomous characteristics of the sample and to compare WSAS scores between subjects
falling above and below the cut-off scores of the GAD-7, IES-R, and PHQ-9. Five multiple
linear regression models were performed with the aim of identifying significant predictors
of global functioning impairment among a range of variables that showed a significant
difference in the t-test analyses. A pvalue < 0.5 was considered statistically significant.
3. Results
A total of 1044 subjects completed the survey. As the survey was distributed with
the assistance of healthcare institutions, associations, and social networks, the response
rate could not be calculated. After data cleaning, three subjects were excluded for incom-
plete/inconsistent data or duplicated records. The final sample thus included 1041 HWs.
The mean age was 45.01
±
11.62 years, 665 (63.9%) were females, 455 (43.7%) were from
Lombardy, and 586 (56.3%) were from other regions. The majority of the sample (77.1%)
completed the survey before May 4th, the date that marks the end of the first lockdown in
Italy. Among the respondents, 832 (79.9%) were physicians, 105 (10.1%) were nurses, and
104 (10.0%) were a mixed group mostly composed of midwives, rehabilitation personnel,
and laboratory technicians. A total of 50 participants (4.8%) were newly employed (less
than 12 months of activity), while 90 (8.6%) had a frontline role in the management of
COVID-19 patients.
3.1. Correlates of Positive Screening to GAD-7, PHQ-9, and IES-R
Altogether, 58.1% of the subjects screened positive for generalized anxiety disorder, as
assessed by scoring at least 10 on the GAD-7. Positive screening was significantly associated
with participating in the survey before May 4th (p= 0.006), being female (p< 0.001), having a
positive psychiatric history (p< 0.001), being from other regions than Lombardy (p= 0.035),
being exposed to an unusual amount of suffering (p< 0.001), and having faced separation
from family (p< 0.001). Conversely, 27.5% of the sample screened positive for major
depression, as assessed by scoring at least 10 on the PHQ-9. Positive screening was
significantly associated with age up to 40 years (p= 0.001), being female (p< 0.001), having
a positive psychiatric history (p< 0.001), COVID-19 infection among close ones (p= 0.01),
changes in work tasks (p< 0.001), being moved to other wards (p< 0.001), being moved
to COVID-19 wards (p< 0.001), exposure to an unusual level of suffering (p< 0.001), and
having faced separation from family (p< 0.001). In addition, 270 (25.9%) subjects tested
positive for a provisional diagnosis of PTSD, as assessed by scoring at least 33 on the IES-R.
3.2. Impairment in Functioning
A total of 67.4% of subjects showed a significant level of functioning impairment accord-
ing to the WSAS, while 35.8% reached scores of moderate or worse functioning impairment.

J. Clin. Med. 2024,13, 5821 5 of 11
The WSAS total scores were found to be significantly higher among HWs of female
gender (p< 0.001), younger than 40 (p= 0.017), responsible for a minor child (p= 0.005),
and who were separated from their family due to fear of contagion (p< 0.001). Significantly
higher WSAS total scores were also found among HWs who witnessed infections (p= 0.009)
or deaths (p= 0.001) among close ones, those who experienced a change in tasks (p< 0.001),
who were moved to other wards (p< 0.001) or to COVID-19 wards (p< 0.001), and who
were exposed to an unusual level of suffering (p< 0.001). A full overview of the associations
among WSAS total and subscale scores and sociodemographic, clinical, and work-related
variables is shown in Table 1and in Table 2.
Table 1. WSAS scores (mean +/
−
SD) in the total sample (n = 1041) and divided by HWs’ sociodemo-
graphic characteristics, with effect sizes calculated according to Cohen’s d.
WSAS 1
Working Activities
WSAS 2
Household Chores
WSAS 3
Social Activities
WSAS 4
Private Activities
WSAS 5
Family Relationships
N (%) Mean ±SD pMean ±SD pMean ±SD pMean ±SD pMean ±SD p
Sex d = 0.147 d = 0.314 d = 0.309 d = 0.346 d = 0.232
Female 665 (63.9) 2.48 ±1.8 0.029 3.4 ±1.8 <0.001 3.9 ±2.4 <0.001 3.9 ±2.4 <0.001 3.4 ±2.2 <0.001
Male 376 (36.1) 2.2 ±1.9 2.8 ±2.0 3.1 ±2.4 3.1 ±2.5 2.9 ±2.3
Age (yrs. old) d = 0.154 d = 0.158 d = 0.181 d = 0.136 d = 0.068
<40 429 (41.2) 2.6 ±1.8 0.020 3.4 ±1.9 0.017 3.9 ±2.4 0.006 3.8 ±2.5 0.040 3.3 ±2.2 0.303
>40 612 (58.8) 2.3 ±1.8 2.1 ±2.0 3.4 ±2.5 3.5 ±2.5 3.2 ±2.3
Time of completion d = 0.087 d = −0.062 d = −0.128 d = −0.067 d = −0.070
Before May 4th 803 (77.1) 2.3 ±1.8 0.245 3.2 ±1.9 0.405 3.7 ±2.4 0.087 3.7 ±2.5 0.371 3.3 ±2.3 0.350
After May 4th 238 (22.9) 2.5 ±2.0 3.1 ±2.1 3.4 ±2.6 3.5 ±2.6 3.1 ±2.3
Region d = −0.074 d = 00.31 d = −00.83 d = −00.53 d = −00.93
Lombardy 455 (43.7) 2.5 ±1.9 0.260 3.2 ±1.9 0.638 3.6 ±2.5 0.497 3.5 ±2.5 0.255 3.2 ±2.3 0.594
Other 586 (56.3) 2.3 ±1.8 3.2 ±1.9 3.7 ±2.4 3.7 ±2.5 3.3 ±2.3
Role d = −0.080 d = −0.075 d = −0.120 d = −00.47 d = −0.008
Doctor/nurse 937 (90) 2.4 ±1.8 0.446 3.2 ±1.9 0.473 3.7 ±2.5 0.250 3.7 ±2.5 0.655 3.2 ±2.3 0.939
Other * 104 (10) 2.3 ±1.9 3.1 ±1.9 3.3 ±2.4 3.5 ±2.4 3.2 ±2.3
Work area d = −0.61 d = −1.88 d= −1.34 d = −0.118 d = −0.318
Frontline 90 (8.6) 2.8 ±2.2 0.041 3.4 ±1.8 0.366 3.8 ±2.3 0.490 3.7 ±2.5 0.712 3.3 ±2.3 0.792
Other ◦951 (91.4) 2.3 ±1.8 3.2 ±2.0 3.6 ±2.5 3.6 ±2.5 3.2 ±2.3
Caregiver of minor child d = −0.061 d = −0.188 d = −0.134 d = −0.118 d = −0.318
Yes 652 (62.6) 2.3 ±1.8 0.364 3.1 ±1.9 0.006 3.5 ±2.4 0.051 3.5 ±2.5 0.86 3.0 ±2.3 <0.001
No 389 (37.4) 2.5 ±1.9 3.4 ±2.0 3.8 ±2.5 3.8 ±2.5 3.7 ±2.2
Length of service d = −0.019 d = −0.081 d = −0.018 d = 0.005 d = −0.245
>1 year 991 (95.2) 2.4 ±1.9 0.903 3.2 ±1.9 0.593 3.6 ±2.5 0.904 3.6 ±2.5 0.971 3.2 ±2.3 0.126
<1 year 50 (4.8) 2.4 ±1.9 3.4 ±2.0 3.7 ±2.5 3.6 ±2.4 3.8 ±2.4
* including administrative, rehabilitation, diagnostic, and technical personnel.
◦
including Emergency Department
and Intensive Care Unit personnel.
Table 2. WSAS scores (mean +/
−
SD) in the total sample (n = 1041) and divided by HWs’ pandemic-
related and clinical characteristics, with effect sizes calculated according to Cohen’s d.
N (%) WSAS 1
Working Activities
WSAS 2
Household Chores
WSAS 3
Social Activities
WSAS 4
Private Activities
WSAS 5
Family Relationships
Mean ±SD pMean ±SD pMean ±SD p Mean ±SD pMean ±SD p
History of mental disorders d = −0.428 d = −0.404 d = −0.281 d = −0.319 d = −0.368
Negative 779 (74.8) 2.2 ±1.8 <0.001 3.0 ±1.9 <0.001 3.4 ±2.5 <0.001 3.4 ±2.5 <0.001 3.0 ±2.3 <0.001
Positive 262 (25.2) 2.97 ±1.9 3.8 ±1.9 4.1 ±2.4 4.2 ±2.4 3.8 ±2.2
Infection among close ones d = −0.190 d= −0.182 d = −0.183 d = −0.124 d = −10.27
No 715 (68.7) 2.3 ±1.8 0.007 3.1 ±2.0 0.009 3.5 ±2.5 0.009 3.5 ±2.5 0.076 3.1 ±2.3 0.061
Yes 326 (31.3) 2.6 ±1.9 3.5 ±1.8 3.9 ±2.5 3.8 ±2.4 3.4 ±2.1
Deaths among close ones d = −0.258 d= −0.251 d = −0.224 d = −0.156 d = −0.228
No 762 (73.2) 2.3 ±1.8 <0.001 3.1 ±1.9 0.001 3.5 ±2.4 0.003 3.5 ±2.5 0.034 3.1 ±2.2 0.002
Yes 279 (26.8) 2.7 ±2.0 3.6 ±2.0 4.0 ±2.5 3.9 ±2.5 3.6 ±2.3
Changes in usual tasks d = −2.68 d = −0.334 d = −0.323 d = −0.327 d = −0.205
No 750 (72) 2.2 ±1.8 <0.001 3.0 ±2.0 <0.001 3.4 ±2.4 <0.001 3.4 ±2.4 <0.001 3.1 ±2.2 0.007
Yes 291(28) 2.7±2.0 3.7 ±2.0 4.2 ±2.5 4.2 ±2.6 3.6±2.5
Relocation to other units d = −0.259 d = −0.322 d = 0.327 d = 0.286 d = −0.215
No 851 (81.7) 2.3 ±1.8 0.002 3.1 ±1.9 <0.001 3.5 ±2.4 <0.001 3.5 ±2.5 0.001 3.1 ±2.2 0.009
Yes 190 (18.3) 2.7 ±2.1 3.7 ±2.0 4.3 ±2.5 4.2 ±2.5 3.6 ±2.5

J. Clin. Med. 2024,13, 5821 6 of 11
Table 2. Cont.
N (%) WSAS 1
Working Activities
WSAS 2
Household Chores
WSAS 3
Social Activities
WSAS 4
Private Activities
WSAS 5
Family Relationships
Mean ±SD pMean ±SD pMean ±SD p Mean ±SD pMean ±SD p
Relocation to COVID-19 units d = −0.299 d= −0.359 d = −0.346 d = −0.325 d = −0.243
No 741 (71.2%) 2.2 ±1.7 <0.001 3.0 ±1.9 <0.001 3.4 ±2.4 <0.001 3.4 ±2.4 <0.001
3.05
±
2.2
0.001
Yes 300 (28.8%) 2.8 ±2.03 3.7 ±1.9 4.2 ±2.5 4.2 ±2.5 3.6 ±2.4
Exposure to unusual suffering d= −0.470 d = −0.595 d = −0.583 d = −0.513 d = −0.425
No 614 (59%) 2.0 ±1.6 <0.001 2.72 ±1.8 <0.001 3.0 ±2.2 <0.001 3.1 ±2.3 <0.001 2.8 ±2.2 <0.001
Yes 427 (41%) 2.9 ±2.0 3.8 ±2.0 4.3 ±2.5 4.3 ±2.5 3.7 ±2.3
Separation from family d = −0.305 d = −0.567 d = −0.538 d = −0.562 d = −0.473
No 908 (87.2%) 2.3 ±1.8 0.002 3.1 ±1.9 <0.001 3.5 ±2.4 <0.001 3.5 ±2.4 <0.001 3.1 ±2.2 <0.001
Yes 133 (12.8%) 2.9 ±2.1 4.15 ±2.1 4.8 ±2.6 4.8 ±2.5 4.2±2.3
PHQ-9 screening d = −0.967 d= −1.427 d = −1.288 d = −1.208 d = −1.095
Negative 663 (63.7%) 2.0 ±1.5 <0.001 2.5 ±1.6 <0.001 2.7 ±2.1 <0.001 2.8 ±2.2 <0.001 2.6 ±2.0 <0.001
Positive 286 (27.5%) 3.5 ±2.0 4.8 ±1.7 5.5 ±2.2 5.5 ±2.2 4.8 ±2.2
GAD-7 screening d = −0.562 d = −0.887 d = −0.748 d = −0.831 d = −0.773
Negative 344 (33%) 1.7 ±1.6 <0.001 2.2 ±1.6 <0.001 2.5 ±2.1 <0.001 2.4 ±2.2 <0.001 2.2 ±1.8 <0.001
Positive 605 (58.1%) 2.8 ±1.9 3.8 ±1.9 4.3 ±2.4 4.3 ±2.4 3.8 ±2.3
IES-R screening d = −0.917 d = −1.148 d = −0.958 d = −0.999 d = −0.888
Negative 771 (74.1%) 1.94 ±1.6 <0.001 2.7 ±1.7 <0.001 3.0 ±2.2 <0.001 3.0 ±2.3 <0.001 2.7 ±2.1 <0.001
Positive 270 (25.9%) 3.50 ±2.0 4.6 ±1.8 5.2 ±2.4 5.3 ±2.3 4.6 ±2.2
A series of five linear regression models were run in order to test possible independent
predictors for each WSAS domain score (see Table 3). As shown in Table 2, screening
positive for any of the psychometric scales—namely, IES-R, PHQ-9, or GAD-7—significantly
predicted worse scores to all WSAS domains (all p< 0.001). Being exposed to unusual
suffering was a significant predictor of all WSAS domains scores as well (<0.05). Having a
history of mental disorders significantly predicted higher scores in the WSAS assessing the
impairment in work ability (p= 0.002), while being the parent of children younger than
18 years significantly predicted worse WSAS family functioning scores (p< 0.001).
Table 3. Multiple linear regression analyses for predictors of WSAS item scores, with effect sizes
according to adjusted r2.
Factors WSAS 1
Working Activities
WSAS 2
Household Chores
WSAS 3
Social Activities
WSAS 4
Private Activities
WSAS 5
Family Relationships
b (SE)
CI 95% p
b (SE)
CI 95% p
b (SE)
CI 95% p
b (SE)
CI 95% p
b (SE)
CI 95% p
Adjusted r20.230 0.390 0.320 0.312 0.273
K
(constant)
1.33
(0.14) 1.05–1.62
<0.001
1.78
(0.13) 1.52–2.05
<0.001
2.11
(0.18) 1.75–2.46
<0.001
2.08
(0.18) 1.72–2.44
<0.001
1.61
(0.17) 1.27–1.95
<0.001
Sex 0.14
(0.12) −0.08–0.37 0.228 −0.41
(0.11) −0.25–0.17 0.704 −0.15
(0.15) −0.43–0.14 0.306 −0.22
(0.15) −0.51–0.06
0.127
0.07
(0.14) −0.20–0.34 0.609
Age −0.16
(0.11) −0.38–0.06 0.149 −0.09
(0.10) −0.29–0.12 0.406 0.14
(0.14) −0.42–0.14 0.323 −0.02
(0.14) −0.30–0.26
0.876
−0.02
(0.13) −0.29–0.24 0.862
Work area 0.33
(0.20) −0.06–0.73 0.099 0.02
(0.19) −0.34–0.41 0.851 −0.60
(0.26) −0.56–0.44 0.818 −0.83
(0.26) −0.59–0.43
0.749
−0.50
(0.24) −0.53–0.43 0.836
History of mental
disorders
0.40
(0.13) 0.14–0.64 0.002 0.18
(0.12) −0.05–0.42 0.121 −0.01
(0.16) −0.32–0.31 0.977 0.08
(0.16) −0.24–0.39
0.637
0.26
(0.15) −0.04–0.56 0.086
Children <18 years 0.01
(0.11) −0.21–0.23 0.932 0.19
(0.10) −0.02–0.39 0.074 0.12
(0.14) −0.16–0.40 0.400 0.07
(0.14) −0.22–0.35
0.647
0.55
(0.13) 0.29–0.82
<0.001
COVID-19 infection
among close ones
0.58
(0.13) −0.19–0.31 0.652 0.01
(0.12) −0.23–0.24 0.983 0.06
(0.16) −0.26–0.37 0.731 −0.01
(0.16) −0.33–0.31
0.955
−0.10
(0.15) −0.40–0.21 0.541
COVID-19 death
among close ones
0.21
(0.13) −0.05–0.47 0.080 0.16
(0.12) −0.08–0.41 0.190 0.17
(0.17) −0.16–0.50 0.317 0.02
(0.17) −0.31–0.35
0.900
0.25
(0.16) −0.07–0.56 0.120
Change in tasks 0.16
(0.15) −0.13–0.45 0.294 0.20
(0.14) −0.07–0.47 0.148 0.22
(0.19) −0.15–0.59 0.238 0.36
(0.19) −0.02–0.73
0.060
0.07
(0.18) −0.28–0.42 0.701
Relocation to other
units
−0.86
(0.18) −0.44–0.27 0.630 −0.16
(0.17) −0.49–0.17 0.337 −0.13
(0.23) −0.58–0.31 0.557 −0.28
(0.23) −0.73–0.16
0.214
−0.14
(0.22) −0.56–0.28 0.518
Relocation to
COVID-19 units
0.04
(0.16) −0.14–0.42 0.816 0.09
(0.15) −0.20–0.38 0.534 0.10
(0.20) −0.28–0.49 0.600 0.17
(0.20) −0.023–0.56
0.407
0.06
(0.19) −0.31–0.43 0.759
Exposure to unusual
suffering
0.29
(0.12) 0.05–0.53 0.020 0.399
(0.114)
0.17–0.62
<0.001
0.594
(0.155)
0.290–0.898
<0.001
0.41
(0.16) 0.10–0.72
0.009
0.30
(0.15) 0.02–0.59 0.039
Separation from
family
due to COVID-19
−0.13
(0.17) −0.46–0.20 0.436 0.14
(0.16) −0.17–0.44 0.380 0.206
(0.211)
−0.207–0.620 0.328 0.32
(0.21) −0.096–0.740
0.130
0.15
(0.20) -0.24–0.54 0.458
GAD-7 screening 0.32
(0.13) 0.08–0.57 0.009 0.66
(0.12) 0.43–0.89
<0.001
0.66
(0.16) 0.35–0.97
<0.001
0.86
(0.16) 0.55–1.17
<0.001
0.79
(0.15) 0.50–1.09
<0.001

J. Clin. Med. 2024,13, 5821 7 of 11
Table 3. Cont.
Factors WSAS 1
Working Activities
WSAS 2
Household Chores
WSAS 3
Social Activities
WSAS 4
Private Activities
WSAS 5
Family Relationships
b (SE)
CI 95% p
b (SE)
CI 95% p
b (SE)
CI 95% p
b (SE)
CI 95% p
b (SE)
CI 95% p
PHQ-9 screening 0.96
(0.14) 0.69–1.23
<0.001
1.48
(0.13) 1.23–1.73
<0.001
1.91
(0.18) 1.56–2.25
<0.001
1.64
(0.18) 1.29–1.98
<0.001
1.42
(0.17) 1.09–1.74
<0.001
IES-R screening 0.82
(0.14) 0.55–1.10
<0.001
0.78
(0.13) 0.52–1.04
<0.001
0.70
(0.18) 0.35–1.05
<0.001
0.90
(0.18) 0.54–1.25
<0.001
0.70
(0.17) 0.36–1.03
<0.001
4. Discussion
The aim of the present study was to evaluate the interplay between sociodemographic
and clinical variables and global functioning in a large sample of HWs who faced the acute
phase of the COVID-19 pandemic in Italy.
As expected, the presence of anxious, depressive, or post-traumatic symptoms left
the greatest footprint on the levels of functioning. Specifically, HWs exceeding the cut-
off scores of the PHQ-9, GAD-7, and IES-R reported significantly worse WSAS scores
compared to those who did not, reporting a greater risk of impaired functioning in the
multiple linear regressions as well. Other studies in the literature pointed out how, during
or after disease outbreaks, HWs are at risk of mental disorders, highlighting the urge to
promote interventions to help them endure the hardships and provide relief for the negative
mental outcomes [3,7,9,28].
Our prevalence estimates of depression and PTSD (27.5% and 25.9%, respectively)
were quite comparable to those provided by most recent meta-analyses on HWs’ mental
health issues during COVID-19 [
14
,
36
], while the prevalence of generalized anxiety (58.1%)
was way above the upper end of the confidence interval suggested by most of the pooled
analyses from the literature (ranging from 30 to 38%). One possible explanation could be
that our data started to be collected in a very precocious phase and that a big share of
participants worked in a geographical area that was hardest hit by the pandemic. Therefore,
it is possible that in addition to the later-developing post-traumatic, anxious, and depressive
symptoms, our survey had the chance to catch the transient surge in anxiety that often
represents the first psychological response to extreme stress. Most importantly, our data
enriched the extant literature by showing the great impact of such conditions on the levels
of functioning across different areas, including the professional one. Only a few studies,
in fact, have focused on functional impairment among HWs during COVID-19. Carmassi
et al. found anxiety, depression, and PTSD symptoms being among the predictors of
impaired functioning, together with younger age and frontline activity, while being a
medical doctor (vs. nurse/other) was shown to play a protective role for some dimensions
of functioning [
9
]. Another study conducted in US HWs found a predictive role of PTSD and
depressive symptoms, but not anxiety, on work functioning [
37
]. Strikingly, more than two-
thirds of HWs in our sample displayed impaired functioning at a significant level, and more
than one-third endorsed a level of impairment between moderate and severe. Although
several unassessed factors could have contributed to the broad functional impairment found
in our study, psychopathological symptoms clearly stood out as extensive and modifiable
factors. As such, they should be addressed by means of screening procedures and tailored
interventions, be these specific training, work–family conciliation interventions, or new
tools such as telemedicine, towards which HWs can cultivate positive attitudes in terms of
reliability, speed, confidentiality, and productivity [10,38–40].
Furthermore, some sociodemographic factors, such as female gender, younger age,
and parenting a child under 18 years of age were found to be associated with greater
impairment in several areas of functioning. None of these factors, however, was a significant
predictor of functioning in the multiple linear regressions, except for parenting a young
child. Taking care of young children has been shown to increase the risk of post-traumatic
stress symptoms in a previous study based on the same dataset [
28
]. The present results
further highlight the importance of non-work determinants in shaping the impact of
massive sanitary events such as COVID-19 on healthcare personnel. Ideally, our results

J. Clin. Med. 2024,13, 5821 8 of 11
could additionally provide valuable hints to healthcare institutions to include work–family
conciliation interventions in their policies as a better safeguard of HWs’ wellbeing and
work functioning. Consistently, the results showed that being separated from or witnessing
COVID-19 among close ones was associated with worse functioning.
Several work-related variables were also shown to have a detrimental impact on
functioning. Most of these variables were aimed at assessing changes in daily routines due
to COVID-19 (e.g., change in usual tasks; relocation to other units), while work-related
variables that are stable for a long time, such as work area (frontline vs. no frontline) or role
(medical doctor/nurse vs. other), did not affect the levels of functioning in a differential
way. Again, while a number of variables were significantly associated with functioning in
the bivariate Ttest, in the multiple linear regressions, only the exposure to unusual suffering
demonstrated a significant predictive effect on all functioning domains. Taken together,
these results seem to suggest that factors affecting the levels of functioning are likely
rooted in the psychological burden and professional challenge posed by the unforeseen
confrontation with critical patients. They also highlight the importance of adequate training
for facing large-scale emergencies in the workplace. Somewhat consistently, the study by [
9
],
conducted in Italy during COVID-19, suggested how different outcomes on functioning
may depend on uneven professional training and a dearth of experience due to different
roles or younger work age. Surprisingly, we did not find any correlation between frontline
activity and impairment in functioning despite a body of literature supporting poorer
mental outcomes and worse functioning in this group [
41
–
43
]. One possible explanation
is that our sample included very few frontline HWs (8.9%) compared to previous studies,
which may have prevented some differences from manifesting. On the other hand, our
data further support the hypothesis that working on the frontline while not prepared and
trained for it—instead of working on the frontline on its own—may increase the risk of
worse functioning.
The findings of our study must be seen in light of some limitations. First and foremost,
the data have been collected through self-reporting instruments. Second, the cross-sectional
design did not allow us to infer about causality effects. Moreover, the type of recruitment
may result in potential sample bias with the underrepresentation of some groups (e.g., pro-
fessionals other than doctors/nurses) and overrepresentation of certain characteristics
(e.g., female gender). Furthermore, although we aimed to include a large set of different
groups of variables, several unassessed factors arguably played a role in HWs’ levels of
functioning and might have acted as confounders in our analyses. For instance, we did not
collect data about the presence of medical diseases nor about changes in voluptuary behav-
iors. Eventually, while some solid hints were provided by the linear regressions, further
testing of our data by means of different statistical methods (e.g. mediation/moderation
analysis) might help to understand the extent to which factors that we found to affect the
levels of functioning exerted their detrimental effect in a direct or indirect manner. This
may even better inform health institutions when designing their emergency plans and their
interventions toward HWs.
5. Conclusions
The present paper highlights the burden of the COVID-19 pandemic on healthcare
workers’ mental health, underlining a stronger association between clinical negative out-
comes and functioning. Higher levels of functioning impairment were especially found
among individuals with depressive, anxious, and post-traumatic stress symptoms com-
pared to those without. Psychopathological variables were predictive factors of impairment
in each functioning domain of the WSAS, controlling for a number of covariates. Addi-
tionally, exposure to an unusual amount of suffering was also a predictor of impairment
in every WSAS domain. Thus, our results seem to suggest that functional impairment
is mainly linked to the psychological burden and professional challenge posed by the
unforeseen confrontation with critical patients. By highlighting factors that exert an actual

J. Clin. Med. 2024,13, 5821 9 of 11
impact on functioning, our study provides further information to design support policies
and intervention strategies for HWs during large-scale events.
Author Contributions: Conceptualization, C.G. and R.C.; methodology, C.G. and M.C.; formal
analysis, M.C.; investigation, C.G.; data curation, M.C.; writing—original draft preparation, F.A., M.B.
and G.C.; writing—review and editing, C.G., M.C. and R.C.; supervision, B.D. All authors have read
and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: The study was conducted in accordance with the principles
stated in the Declaration of Helsinki, and the study procedures were approved by the Department of
Psychiatry of the ASST Fatebenefratelli-Sacco of Milan as a relevant institutional review board for
low-risk studies (1 April 2020, code: dsm 12-20.).
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: The data that support the findings of this study are available on request
from the corresponding author.
Conflicts of Interest: The authors declare no conflicts of interest in regard to this publication.
References
1.
Armocida, B.; Formenti, B.; Ussai, S.; Palestra, F.; Missoni, E. The Italian health system and the COVID-19 challenge. In The Lancet
Public Health; Elsevier Ltd.: Amsterdam, The Netherlands, 2020; Volume 5, p. e253.
2.
Ferrara, P.; Albano, L. COVID-19 and healthcare systems: What should we do next? Public Health 2020,185, 1–2. [CrossRef]
[PubMed]
3. Hennein, R.; Mew, E.J.; Lowe, S.R. Socio-ecological predictors of mental health outcomes among healthcare workers during the
COVID-19 pandemic in the United States. PLoS ONE 2021,16, e0246602. [CrossRef] [PubMed]
4.
Digby, R.; Winton-Brown, T.; Finlayson, F.; Dobson, H.; Bucknall, T. Hospital staff well-being during the first wave of COVID-19:
Staff perspectives. Int. J. Ment. Health Nurs. 2021,30, 440–450. [CrossRef] [PubMed]
5.
Wang, C.; Horby, P.W.; Hayden, F.G.; Gao, G.F. A novel coronavirus outbreak of global health concern. Lancet 2020,395, 470–473.
[CrossRef]
6.
Carmassi, C.; Dell’Oste, V.; Bui, E.; Foghi, C.; Bertelloni, C.A.; Atti, A.R.; Buselli, R.; Di Paolo, M.; Goracci, A.; Malacarne, P.; et al.
The interplay between acute post-traumatic stress, depressive and anxiety symptoms among healthcare workers functioning
during the COVID-19 emergency: A multicenter study comparing regions with increasing pandemic incidence. J. Affect Disord.
2022,298, 209–216. [CrossRef]
7.
Muller, A.E.; Hafstad, E.V.; Himmels, J.P.W.; Smedslund, G.; Flottorp, S.; Stensland, S.Ø.; Stroobants, S.; Van De Velde, S.; Vist, G.E.
The mental health impact of the COVID-19 pandemic on healthcare workers, and interventions to help them: A rapid systematic
review. Psychiatr. Res. 2020,293, 113441. [CrossRef]
8.
Giallonardo, V.; Sampogna, G.; Del Vecchio, V.; Luciano, M.; Albert, U.; Carmassi, C.; Carrà, G.; Cirulli, F.; Dell’osso, B.; Nanni,
M.G.; et al. The impact of quarantine and physical distancing following COVID-19 on mental health: Study protocol of a
multicentric italian population trial. Front. Psychiatry 2020,11, 533. [CrossRef] [PubMed]
9.
Carmassi, C.; Pedrinelli, V.; Dell’Oste, V.; Bertelloni, C.A.; Cordone, A.; Bouanani, S.; Corsi, M.; Baidanzi, S.; Malacarne, P.;
Dell’Osso, L.; et al. Work and social functioning in frontline healthcare workers during the COVID-19 pandemic in Italy: Role of
acute post-traumatic stress, depressive and anxiety symptoms. Riv. Psichiatr. 2021,56, 189–197.
10.
Pappa, S.; Ntella, V.; Giannakas, T.; Giannakoulis, V.G.; 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. [CrossRef]
11.
Hruska, B.; Barduhn, M.S. Dynamic psychosocial risk and protective factors associated with mental health in Emergency Medical
Service (EMS) personnel. J. Affect. Disord. 2021,282, 9–17. [CrossRef]
12.
Cameron, L.; McCauley, M.; Broek, N.v.D.; McCauley, H. The occurrence of and factors associated with mental ill-health amongst
humanitarian aid workers: A systematic review and meta-analysis. PLoS ONE 2024,19, e0292107. [CrossRef] [PubMed]
13.
Tong, J.; Zhang, J.; Zhu, N.; Pei, Y.; Liu, W.; Yu, W.; Hu, C.; Sun, X. Effects of COVID-19 pandemic on mental health among
frontline healthcare workers: A systematic review and meta-analysis. Front. Psychol. 2023,13, 1096857. [CrossRef]
14.
Lee, B.E.C.; Ling, M.; Boyd, L.; Olsson, C.; Sheen, J. The prevalence of probable mental health disorders among hospital healthcare
workers during COVID-19: A systematic review and meta-analysis. J. Affect. Disord. 2023,330, 329–345. [CrossRef]
15.
Vizheh, M.; Qorbani, M.; Arzaghi, S.M.; Muhidin, S.; Javanmard, Z.; Esmaeili, M. The mental health of healthcare workers in the
COVID-19 pandemic: A systematic review. J. Diabetes Metab. Disord. 2020,19, 1967–1978. [CrossRef] [PubMed]
16. Bosc, M. Assessment of social functioning in depression. Compr. Psychiatry 2000,41, 63–69. [CrossRef]

J. Clin. Med. 2024,13, 5821 10 of 11
17.
Gesi, C.; Cafaro, R.; Achilli, F.; Boscacci, M.; Cerioli, M.; Cirnigliaro, G.; Loupakis, F.; Di Maio, M.; Dell’osso, B. The relationship
among posttraumatic stress disorder, posttraumatic growth, and suicidal ideation among Italian healthcare workers during the
first wave of COVID-19 pandemic. CNS Spectr. 2024,29, 60–64. [CrossRef] [PubMed]
18.
Filippou, A.; Giannouli, V. Exploring the Experiences of Integrative Psychotherapists Regarding Resilience during the COVID-19
Pandemic in Greece: An Interpretative Phenomenological Analysis. Psych 2023,5, 1270–1287. [CrossRef]
19.
Reich, K.; Nemeth, L.S.; Mueller, M.; Sternke, L.M.; Acierno, R. Psychosocial functioning in veterans with combat-related PTSD:
An evolutionary concept analysis. Nurs. Forum 2021,56, 194–201. [CrossRef]
20.
Flanagan, J.C.; Fischer, M.S.; Badour, C.L.; Ornan, G.; Killeen, T.K.; Back, S.E. The Role of Relationship Adjustment in an Integrated
Individual Treatment for PTSD and Substance Use Disorders Among Veterans: An Exploratory Study. J. Dual Diagn. 2017,13,
213–218. [CrossRef]
21.
Campbell, S.B.; Renshaw, K.D. PTSD symptoms, disclosure, and relationship distress: Explorations of mediation and associations
over time. J. Anxiety Disord. 2013,27, 494–502. [CrossRef]
22.
Löwe, B.; Decker, O.; Müller, S.; Brähler, E.; Schellberg, D.; Herzog, W.; Herzberg, P.Y. Validation and Standardization of the
Generalized Anxiety Disorder Screener (GAD-7) in the General Population. Med. Care 2008,46, 266–274. [CrossRef] [PubMed]
23.
Rapisarda, F.; Vallarino, M.; Brousseau-Paradis, C.; De Benedictis, L.; Corbière, M.; Villotti, P.; Cavallini, E.; Briand, C.; Cailhol, L.;
Lesage, A. Workplace Factors, Burnout Signs, and Clinical Mental Health Symptoms among Mental Health Workers in Lombardy
and Quebec during the First Wave of COVID-19. Int. J. Environ. Res. Public Health 2022,19, 3806. [CrossRef] [PubMed]
24.
Spitzer, R.L.; Kroenke, K.; Williams, J.B.W.; Löwe, B. A Brief Measure for Assessing Generalized Anxiety Disorder The GAD-7
[Internet]. 2006. Available online: http://archinte.jamanetwork.com/ (accessed on 26 June 2024).
25.
Kroenke, K.; Spitzer, R.L.; Williams, J.B.W.; Löwe, B. The Patient Health Questionnaire Somatic, Anxiety, and Depressive Symptom
Scales: A systematic review. Gen. Hosp. Psychiatry 2010,32, 345–359. [CrossRef] [PubMed]
26.
Levis, B.; Benedetti, A.; Thombs, B.D. Accuracy of Patient Health Questionnaire-9 (PHQ-9) for screening to detect major
depression: Individual participant data meta-analysis. BMJ 2019,365, l1476. [CrossRef] [PubMed]
27.
Costantini, L.; Pasquarella, C.; Odone, A.; Colucci, M.E.; Costanza, A.; Serafini, G.; Aguglia, A.; Murri, M.B.; Brakoulias, V.;
Amore, M.; et al. Screening for depression in primary care with Patient Health Questionnaire-9 (PHQ-9): A systematic review.
J. Affect. Disord. 2021,279, 473–483. [CrossRef]
28.
Gesi, C.; Cirnigliaro, G.; Achilli, F.; Cerioli, M.; Cafaro, R.; Boscacci, M.; Dell’osso, B. The Impact of COVID-19 Pandemic First
Wave on Healthcare Workers: A New Perspective from Qualifying PTSD Criterion A to Assessing Post-Traumatic Growth. J. Clin.
Med. 2023,12, 1862. [CrossRef]
29.
Beck, J.G.; Grant, D.M.; Read, J.P.; Clapp, J.D.; Coffey, S.F.; Miller, L.M.; Palyo, S.A. The Impact of Event Scale-Revised:
Psychometric properties in a sample of motor vehicle accident survivors. J. Anxiety Disord. 2008,22, 187–198. [CrossRef]
30.
Fattori, A.; Cantù, F.; Comotti, A.; Tombola, V.; Colombo, E.; Nava, C.; Bordini, L.; Riboldi, L.; Bonzini, M.; Brambilla, P. Hospital
workers mental health during the COVID-19 pandemic: Methods of data collection and characteristics of study sample in a
university hospital in Milan (Italy). BMC Med. Res. Methodol. 2021,21, 1–12. [CrossRef]
31.
Creamer, M.; Bell, R.; Failla, S. Psychometric properties of the Impact of Event Scale—Revised. Behav. Res. Ther. 2003,41,
1489–1496. [CrossRef]
32.
Craparo, G.; Faraci, P.; Rotondo, G.; Gori, A. The Impact of Event Scale—Revised: Psychometric properties of the Italian version
in a sample of flood victims. Neuropsychiatr. Dis. Treat. 2013,9, 1427–1432. [CrossRef]
33.
Aljaberi, M.A.; Lee, K.-H.; Alareqe, N.A.; Qasem, M.A.; Alsalahi, A.; Abdallah, A.M.; Noman, S.; Al-Tammemi, A.B.; Ibrahim,
M.I.M.; Lin, C.-Y. Rasch Modeling and Multilevel Confirmatory Factor Analysis for the Usability of the Impact of Event
Scale-Revised (IES-R) during the COVID-19 Pandemic. Healthcare 2022,10, 1858. [CrossRef] [PubMed]
34.
Mundt, J.C.; Marks, I.M.; Shear, M.K.; Greist, J.M. The Work and Social Adjustment Scale: A simple measure of impairment in
functioning. Br. J. Psychiatry 2002,180, 461–464. [CrossRef] [PubMed]
35.
Vázquez Morejón, A.J.; Vázquez-Morejón, R.; Conde Álvarez, P. Work and Social Adjustment Scale (WSAS): Psychometric
characteristics of a Spanish adaptation in a clinical population. Behav. Cogn. Psychother. 2021,49, 764–768. [CrossRef]
36.
Huang, J.; Huang, Z.-T.; Sun, X.-C.; Chen, T.-T.; Wu, X.-T. Mental health status and related factors influencing healthcare workers
during the COVID-19 pandemic: A systematic review and meta-analysis. PLoS ONE 2024,19, e0289454. [CrossRef]
37.
Hendrickson, R.C.; Slevin, R.A.; Hoerster, K.D.; Chang, B.P.; Sano, E.; McCall, C.A.; Monty, G.R.; Thomas, R.G.; Raskind, M.A.
The Impact of the COVID-19 Pandemic on Mental Health, Occupational Functioning, and Professional Retention Among Health
Care Workers and First Responders. J. Gen. Intern. Med. 2022,37, 397–408. [CrossRef]
38.
Sheffler, J.L.; Rushing, N.C.; Stanley, I.H.; Sachs-Ericsson, N.J. The long-term impact of combat exposure on health, interpersonal,
and economic domains of functioning. Aging Ment. Health 2016,20, 1202–1212. [CrossRef] [PubMed]
39.
Pollock, A.; Campbell, P.; Cheyne, J.; Cowie, J.; Davis, B.; McCallum, J.; McGill, K.; Elders, A.; Hagen, S.; McClurg, D.; et al.
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. 2020,2020, CD013779.
40.
Giannouli, V. eHealth before-and-during COVID-19: Does depressive symptomatology influence attitudes of cad patients,
healthcare students and professionals? Psychiatr. Danub. 2023,35, 245–249. [CrossRef] [PubMed]
41.
Liu, Q.; Luo, D.; Haase, J.E.; Guo, Q.; Wang, X.Q.; Liu, S.; Xia, L.; Liu, Z.; Yang, J.; Yang, B.X. The experiences of health-care
providers during the COVID-19 crisis in China: A qualitative study. Lancet Glob. Health 2020,8, e790–e798. [CrossRef]

J. Clin. Med. 2024,13, 5821 11 of 11
42.
Lai, J.; Ma, S.; Wang, Y.; Cai, Z.; Hu, J.; Wei, N.; Wu, J.; Du, H.; Chen, T.; Li, R.; et al. Factors associated with mental health
outcomes among health care workers exposed to coronavirus disease 2019. JAMA Netw. Open 2020,3, e203976. [CrossRef]
43.
Sanghera, J.; Pattani, N.; Hashmi, Y.; Varley, K.F.; Cheruvu, M.S.; Bradley, A.; Burke, J.R. The impact of SARS-CoV-2 on the mental
health of healthcare workers in a hospital setting—A Systematic Review. J. Occup. Health 2020,62, e12175. [CrossRef] [PubMed]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to
people or property resulting from any ideas, methods, instructions or products referred to in the content.