BUS 499, Week 3 The Internal Organization Resources, Capabilitie

BUS 499, Week 3: The Internal Organization: Resources,
Capabilities, Core Competencies, and Competitive Advantages
Slide #
Topic
Narration
1
Introduction
Welcome to the Business Administration Capstone.
In this lesson we will discuss The Internal Organization:
Resources, Capabilities, Core Competencies, and Competitive
Advantages
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2
Objectives
Upon completion of this lesson, you will be able to:
Analyze the internal environment of a company for strengths
and weaknesses that impact the firm’s competitiveness.
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3
Topics
In order to achieve this objective, the following supporting
topics will be covered:
Analyzing the internal organization;
Resources, capabilities, and core competencies;
Building core competencies;
Outsourcing; and
Competencies, strengths, weaknesses, and strategic decisions.
Next slide.

4
Internal Analysis
In the global economy, traditional factors such as labor costs,
access to financial resources and raw materials, and protected or
regulated markets remain sources of competitive advantage, but
to a lesser degree. One important reason is that competitors can
apply their resources to successfully use an international
strategy as a means of overcoming the advantages created by
these more traditional sources.
Increasingly, those who analyze their firm’s internal
organization should use a global mind-set to do so. A global
mind-set is the ability to study an internal organization in ways
that are not dependent on the assumptions of a single country,
culture, or context. Because they are able to span artificial
boundaries, those with a global mind-set recognize that their
firms must possess resources and capabilities that allow
understanding of and appropriate response to competitive
situations that are influenced by country-specific factors and
unique societal cultures.
Finally, analysis of the firm’s internal organization requires that
evaluators examine the firm’s portfolio of resources and the
bundles of heterogeneous resources and capabilities managers
have created. This perspective suggests that individual firms
possess at least some resources and capabilities that other
companies do not.
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5
Creating Value
By exploiting their core competencies or competitive
advantages to at least meet if not exceed the demanding
standards of global competition, firms create value for
customers. Value is measured by a product’s performance
characteristics and by its attributes for which customers are

willing to pay.
Firms with a competitive advantage offer value to customers
that are superior to the value competitors provide. Firms create
value by innovatively bundling and leveraging their resources
and capabilities. Firms unable to creatively bundle and leverage
their resources and capabilities in ways that create value for
customers suffer performance declines. Sometimes, it seems
that these declines may happen because firms fail to understand
what customers value.
Ultimately, creating value for customers is the source of above-
average returns for a firm. What the firm intends regarding
value creation affects its choice of business-level strategy and
its organizational structure.
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6
Resources, Capabilities, and Core Competencies
Resources, capabilities, and core competencies are the
foundation of competitive advantage. Resources are bundled to
create organizational capabilities. In turn, capabilities are the
source of a firm’s core competencies, which are the basis of
competitive advantages.
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7
Resources
Broad in scope, resources cover a spectrum of individual,
social, and organizational phenomena. Typically, resources
alone do not yield a competitive advantage. In fact, a
competitive advantage is generally based on the unique
bundling of several resources.
Some of a firm’s resources are tangible while others are
intangible. Tangible resources are assets that can be seen and

quantified. Production equipment, manufacturing facilities,
distribution centers, and formal reporting structures are
examples of tangible resources.
Intangible resources are assets that are rooted deeply in the
firm’s history and have accumulated over time. Because they
are embedded in unique patterns of routines, intangible
resources are relatively difficult for competitors to analyze and
imitate.
The four types of tangible resources are financial,
organizational, physical, and technological. The three types of
intangible resources are human, innovation, and reputational.
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8
Capabilities
Capabilities exist when resources have been purposely
integrated to achieve a specific task or set of tasks. These tasks
range from human resources selection to product marketing and
research and development activities. Critical to the building of
competitive advantages, capabilities are often based on
developing, carrying, and exchanging information and
knowledge through the firm’s human capital. Client-specific
capabilities often develop from repeated interactions with
clients and the learning about their needs that occurs.
As a result, capabilities often evolve and develop over time.
The foundation of many capabilities lies in the unique skills and
knowledge of a firm’s employees and, often, their functional
expertise. Hence, the value of human capital in developing and
using capabilities and, ultimately, core competencie s cannot be
overstated.
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9

Core Competencies
Core competencies are capabilities that serve as a source of
competitive advantage for a firm over it rivals. Core
competencies distinguish a company competitively and reflect
its personality. Core competencies emerge over time through an
organizational process of accumulating and learning how to
deploy different resources and capabilities. As the capacity to
take action, core competencies are crown jewels of a company,
the activities the company performs especially well compared
with competitors and through which the firm adds unique value
to its goods or services over a long period of time.
Capabilities that are valuable, rare, costly to imitate, and
nonsubstitutable are core competencies.
Value capabilities allow the firm to exploit opportunities or
neutralize threats in its external environment. By effectively
using capabilities to exploit opportunities, a firm creates value
for customers.
Rare capabilities are capabilities that few, if any, competitors
possess. Capabilities possessed by many rivals are unlikely to
be sources of competitive advantage for any one of them.
Instead, valuable but common resources and capabilities are
sources of competitive parity. Competitive advantage results
only when firms develop and exploit valuable capabilities that
differ from those shared with competitors.
Costly-to-imitate capabilities are capabilities that other firms
cannot easily develop. Capabilities that are costly to imitate are
created because of one reason or a combination of three reasons.
First, a firm sometimes is able to develop capabilities because
of unique historical conditions. A second condition occurs when
the link between the firm’s capabilities and its competitive
advantage is causally ambiguous. Social complexity is the third
reason that capabilities can be costly to imitate.

Nonsubstitutable capabilities are capabilities that do not have
strategic equivalents. This final criterion for a capability to be a
source of competitive advantage is that there must be no
strategically equivalent valuable resources that are themselves
either not rare or imitable.
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10
Check Your Understanding
11
Value Chain
Value chain analysis allows the firm to understand the parts of
its operations that create value and those that do not.
Understanding these issues is important because the firm earns
above-average returns only when the value it creates is greater
thanthe costs incurred to create that value.
The value chain is a template that firms use to understand their
cost position and to identify the multiple means that might be
used to facilitate implementation of a chose business-level
strategy. Today’s competitive landscape demands that firms
examine their value chains in global, rather than a domestic-
only context. In particular, activities associated with supply
chains should be studied within a global context.
A firm’s value chain is segmented into primary and support
activities. Primary activities are involved with a product’s
physical creation, its sale and distribution to buyers, and its
service after the sale. Support activities provide the assistance
necessary for the primary activities to take place.
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12
Outsourcing

Concerned with how components, finished goods, or services
will be obtained, outsourcing is the purchase of a value-creating
activity from an external supplier. Not-for-profit agencies as
well as for-profit organizations actively engage in outsourcing.
Firms engaging in effective outsourcing increase their
flexibility, mitigate risks, and reduce their capital investments.
In multiple global industries, the trend toward outsourcing
continues at a rapid pace.
Outsourcing can be effective because few, if any, organizations
possess the resources and capabilities required to achieve
competitive superiority in all primary and support activities.
Next slide.
13
Competencies, Strengths, Weaknesses, and Strategic Decisions
When firms analyze the internal organization, they are able to
identify their strengths and weaknesses in resources,
capabilities, and core competencies. An example of this would
be when a firm has weak capabilities or does not have core
competencies in areas required to achieve a competitive
advantage. On the other hand, the firm could decide to
outsource a function or activity where it is weak in order to
improve its ability to use its remaining resources to create
value.
After looking over the results of the examination dealing with a
firm’s internal organization, managers should understand that
having a significant quantity of resources is not the same as
having the right resources. When we talk about the right
resources, we refer to them as resources that have the potential
to be formed into core competencies. These core competencies
will then serve as the foundation for creating value for
customers and developing competitive advantages.
Decision-makers sometimes become more focused and

productive when looking to find the right resources, especially
when the firm has constrained resources. Using tools like
outsourcing can help a firm focus on its core competencies and
use those as its source of competitive advantage. It is important
to note that the value-creating abilities of core competencies
should not be taken advantage of or relied on as a permanent
competitive advantage. This is due to all core competencies
having the potential to become core rigidities. Usually, events
occurring in the firm’s external environment create conditions
where core competencies can become core rigidities, generate
inertia, and stifle innovation. The bad news about core
capabilities deals with the external events that can take away
the competitive advantage. This can occur when new
competitors figure out a better way to serve the firm’s
customers, when new technologies emerge, or when political or
social events stir things up.
An example of external environment affecting a competitive
advantage involves the Borders Group Incorporated. This
company relied on its large storefronts that drew customers into
their stores to browse through books and magazines in a
pleasant atmosphere as sources of its competitive success. Over
the years, however, digital technologies have rapidly changed
customers’ shopping patterns for reading materials. We saw
earlier that Amazon. com’s use of the Internet has significantly
changed the competitive landscape for Borders and similar
competitors. As a result, it is possible that Borders’ core
competencies of store locations and a desirable physical
environment for customers became core rigidities for this firm.
This change eventually lead to Borders filing for bankruptcy in
early 2011 and subsequent liquidation.
It is important that managers who are studying the firm’s
internal organization take responsibility for making sure that
core competencies do not become core rigidities.

Next slide.
14
Summary
We have reached the end of this lesson. Let’s take a look at
what we have covered.
First, we discussed value. Value is measured by a product’s
performance characteristics and by its attributes for which
customers are willing to pay.
Next, we went over resources. Tangible resources are assets that
can be seen and quantified. Intangible resources are assets that
are rooted deeply in the firm’s history and have accumulated
over time.
We then talked about capabilities. Capabilities exist when
resources have been purposely integrated to achieve a specific
task or set of tasks.
Next, we discussed competencies. Core competencies are
capabilities that serve as a source of competitive advantage for
a firm over it rivals.
We then went over value chain. Value chain analysis allows the
firm to understand the parts of its operations that create value
and those that do not.
Later in the lesson with a discussion on outsourcing. Concerned
with how components, finished goods, or services will be
obtained, outsourcing is the purchase of a value-creating
activity from an external supplier.
Finally, to conclude the lesson we discussed competencies,
strengths, weaknesses, and strategic decisions. We talked about
the importance of having the right resources and considering the
external environment. We also looked at the concept of core

competencies and used the example of Borders Group
Incorporated to illustrate the big picture.
This completes this lesson.
Contents lists available at ScienceDirect
Psychiatry Research
journal homepage: www.elsevier.com/locate/psychres
Factors associated with depression, anxiety, and PTSD
symptomatology
during the COVID-19 pandemic: Clinical implications for U.S.
young adult
mental health
Cindy H. Liu (PhD)a,c,d,⁎ , Emily Zhang (MA)a,c, Ga Tin Fifi
Wong (BA)a,c, Sunah Hyun (PhD)a,c,
Hyeouk “Chris” Hahm (PhD)b,c
a Department of Newborn Medicine, Brigham and Women's
Hospital, Boston, MA, USA
b Department of Psychiatry, Brigham and Women's Hospital,
Boston, MA, USA
c School of Social Work, Boston University, Boston, MA, USA
d Harvard Medical School
A R T I C L E I N F O
Keywords:
Psychological stress, Loneliness

University health services
Social support
Ethnicity
COVID-19
Depression
Anxiety
PTSD
A B S T R A C T
This study sought to identify factors associated with depression,
anxiety, and PTSD symptomatology in U.S.
young adults (18-30 years) during the COVID-19 pandemic.
This cross-sectional online study assessed 898
participants from April 13, 2020 to May 19, 2020,
approximately one month after the U.S. declared a state of
emergency due to COVID-19 and prior to the initial lifting of
restrictions across 50 U.S. states. Respondents
reported high levels of depression (43.3%, PHQ-8 scores ≥ 10),
high anxiety scores (45.4%, GAD-7 scores ≥
10), and high levels of PTSD symptoms (31.8%, PCL-C scores ≥
45). High levels of loneliness, high levels of
COVID-19-specific worry, and low distress tolerance were
significantly associated with clinical levels of de-
pression, anxiety, and PTSD symptoms. Resilience was
associated with low levels of depression and anxiety
symptoms but not PTSD. Most respondents had high levels of
social support; social support from family, but not
from partner or peers, was associated with low levels of
depression and PTSD. Compared to Whites, Asian
Americans were less likely to report high levels across mental
health symptoms, and Hispanic/Latinos were less
likely to report high levels of anxiety. These factors provide
initial guidance regarding the clinical management
for COVID-19-related mental health problems.

1. Introduction
The COVID-19 pandemic that has upended the lives of
individuals
worldwide escalated in the U.S. beginning in March of 2020.
Although
research on acute and widescale stressors (e.g., natural
disasters), de-
monstrates severe implications for mental health (Kessler et al.,
2008),
there is no precedent for understanding the mental health effects
due to
COVID-19, as prospective studies investigating the effects of a
pan-
demic are virtually non-existent. In particular, the identification
of risk
factors associated with depression, anxiety, and post-traumatic
stress
disorder (PTSD) among U.S. young adults (18-30 years) during
the
pandemic is urgently needed. Comprising more than one-third
of the
current U.S. workforce, young adults (often referred to as
“Millennials”
and “Generation Z”) will be a dominant workforce group for the
next
decade, and our societal functioning depends on how they
emerge from
the pandemic. Understanding their health and well-being now is
crucial
as it sets the stage for later outcomes.
Certain risk and protective factors are likely to be implicated in
pandemic-related mental health. COVID-19-related worry (e.g.,
main-

taining employment, getting tested for coronavirus) may be
linked to
mental health symptoms. The early weeks of the pandemic saw
rapid
changes in daily routines, with students moving following
university
closures and attending classes remotely, and for other young
adults,
transitioning to remote work or experiencing loss of work.
These dis-
ruptions may put an already vulnerable group at greater risk for
mental
health challenges (Conrad, 2020). Furthermore, loneliness may
be
particularly prevalent and devastating during the pandemic
given di-
rectives for social distancing and isolation. Those under the age
of 25
already show elevated levels of loneliness (Domagala-Krecioch
and
Majerek, 2013), and the pandemic may exacerbate these
feelings. De-
spite the critical role that social support plays in mitigating the
risks to
mental health problems, directives on social distancing may
impede on
https://doi.org/10.1016/j.psychres.2020.113172
Received 28 April 2020; Received in revised form 30 May
2020; Accepted 30 May 2020
⁎ Corresponding author.
E-mail address: [email protected] (C.H. Liu).
Psychiatry Research 290 (2020) 113172

Available online 01 June 2020
0165-1781/ © 2020 Elsevier B.V. All rights reserved.
T
http://www.sciencedirect.com/science/jour nal/01651781
https://www.elsevier.com/locate/psychres
mailto:[email protected]
http://crossmark.crossref.org/dialog/?doi=10.1016/j.psychres.20
20.113172&domain=pdf
one's typical means for obtaining such support.
Individual resilience, which refers to one's ability to cope with
stress, and distress tolerance, which describes one's ability to
manage
and tolerate emotional distress, may be salient characteristics
that
protect against the mental health symptoms that follow major
stressors.
Individual resilience is a significant protective factor for
depression,
PTSD, and general health after natural disasters (Kukihara et
al., 2014).
Findings have generally demonstrated distress tolerance to be
asso-
ciated with lower symptoms of depression and PTSD following
torna-
does (Cohen et al., 2016). However, the extent to which these
factors
are associated with mental health symptoms during a pandemic
is un-

known.
This study sought to identify potential factors that contribute to
mental health outcomes among young adults during the COVID-
19
pandemic. The CARES 2020 Project (COVID-19 Adult
Resilience
Experiences Study, www.cares2020.com) was launched to track
the
health and well-being of young adults in the U.S. across
multiple time
points in 2020 and 2021. This present analysis assessed
depression,
anxiety, and PTSD symptomatology, and psychological
experiences
including distress tolerance, resilience, social support, and
loneliness.
We included depression and anxiety as these are common
mental health
symptoms among young adults (Blazer et al., 1994; Chen et al.,
2019;
Eisenberg et al., 2007; Liu et al., 2019; Mojtabai et al., 2016).
We as-
sessed PTSD symptoms given documented high rates of trauma
by
young adulthood (Costello et al., 2002; Reynolds et al., 2016;
Vrana and
Lauterbach, 1994); a concern was that the pandemic would
either
create and/or exacerbate symptoms related to prior trauma
(Breslau et al., 2008, 1999; Brunet et al., 2001). New items that
as-
sessed COVID-19-specific concerns were also included. The
objective of
this work is to identify salient psychosocial risks for mental
health

symptoms and to prioritize intervention targets for addressing
mental
health symptoms among young adults.
2. Methods
2.1. Study population
This present cross-sectional study assessed potential risk and
pro-
tective factors for mental health outcomes based on preliminary
CARES
2020 data obtained from Wave 1 data collection (N = 898)
conducted
from April 13, 2020 to May 19, 2020, approximately one month
after
the U.S. declared a state of emergency due to COVID-19 and
prior to the
initial lifting of restrictions across 50 U.S. states. Eligible
participants
were young adults aged 18 to 30 years currently living in the
U.S. or
receiving education from a U.S. institution. Participants were
recruited
online via email list serves, social media, and word of mouth
(i.e., list
serves and Facebook groups for school organizations or clubs,
alumni
groups, classes, churches). This took place initially through
organiza-
tions from the New England area before additional list serves
from
other regions of the U.S. (Midwest, South, and West) were
targeted.
Respondents were asked to complete a 30-minute online
Qualtrics

survey regarding COVID-19-related experiences, risk and
resilience,
and physical and mental health outcomes. To ensure data
quality,
human verification and attention checks were implemented
throughout
the survey; the data were further inspected visually for response
irre-
gularities indicative of bots. Participants were compensated via
raffle in
which one out of every 10 participants received a $25 gift card.
All
procedures were approved by the Institutional Review Board at
Boston
University.
2.2. Measures
Binary scores were created after calculating the mean or sum of
each measure. Rather than relying on the sample characteristics
to
categorize our data (e.g., mean, median, tertile or quartile split),
the
determination of the cutoff score was based on standard cutoffs
from
previous research; when a standard was not available, scale
response
descriptors to determine the cutoffs.
2.2.1. Risk and protective factors
Psychological resilience was measured using the 10-item
Connor-
Davidson Resilience Scale (CD-RISC-10, Connor and Davidson,
2003),

which assesses one's ability to cope with adverse experiences.
Partici-
pants indicated how they felt in the past month on a 5-point
scale, with
0 indicating “not true at all” and 4 indicating “true nearly all
the time.”
Sum scores were recoded dichotomously into “high resilience”
and “low
resilience” with a cutoff score of 30 or greater. This cutoff
score char-
acterizes responses that tended to be “often true” and “true
nearly all
the time,” with those endorsing a score ≥30 considered to be at
“very
high risk with mental disorders” (Andrews and Slade, 2001;
Kessler and
Mroczek, 1992).
The Distress Tolerance Scale is a 15-item measure that assesses
participants’ abilities to withstand and cope with emotional
distress
(Simons and Gaher, 2005). Respondents rated personal attitudes
to-
wards feelings of emotional distress on a 5-point scale, ranging
from 1
(“strongly agree”) to 5 (“strongly disagree”), with higher
ratings in-
dicating greater distress tolerance. A global mean score of
distress tol-
erance was calculated. We considered the scale descriptors and
fol-
lowed the cutoffs used for the CD-RISC, which was also a 5-
point scale.
As such, scores were dichotomously recoded so that global
mean scores
less than 4 indicated “low distress tolerance” and scores of 4-to-

5 in-
dicated “high distress tolerance.”
Perceived social support was measured using the
Multidimensional
Scale of Perceived Social Support (MSPSS, Zimet et al., 1988),
in which
participants rated perceived emotional support using a 7-point
Likert
scale ranging from 1 (“very strongly disagree”) to 7 (“very
strongly
agree”). This measure includes three subscales assessing
perceived
support quality from family, friends, and partners. Because
mean scores
greater than 5 reflected responses indicating “mildly agree,”
“strongly
agree,” and “very strongly agree,” each subscale mean scores
were re-
coded so that scores 5 or greater referred to “high percei ved
social
support,” and scores below 5 were referred to as “low perceived
social
support.”
Instrumental support was assessed through a 4-item subscale of
the
Two-Way Social Support Scale (Shakespeare-Finch and Obst,
2011).
Participants indicated the extent of they received instrumental
support
based on a 6-point Likert scale ranging from 0 (“not at all”) to 5
(“al-
ways”). Items were summed to create a total score with a
possible range
of 0 to 20. Given scale descriptors, a cutoff score with a sum of

16 or
greater indicated “high instrumental support,” whereas scores
lower
than 16 indicated “low instrumental support.”
Loneliness was measured using an adapted 3-item version of the
UCLA Loneliness Scale Short Form (Hughes et al., 2004).
Participants
rated lack of companionship, feelings of being left out, and
isolation
from others on a scale of 1-to-3, with 1 as “hardly ever,” 2 as
“some of
the time,” and 3 as “often.” A sum score for loneliness was
calculated
with a total possible range of 3 to 9 and recoded dichotomously;
a
cutoff score of 6 or greater indicated “high loneliness” as used
in prior
studies (Lowthian et al., 2016; Tymoszuk et al., 2019).
Severity of COVID-19 pandemic-related worry was assessed
using a
newly developed measure consisting of 6 items, which included
the
following concerns: “Having enough groceries during city
lockdowns/
social distancing protocols”, “obtaining a COVID-19 test if I
become
sick”, “getting treated for COVID-19 if I contract it”, “keeping
in touch
with loved ones during social distancing protocols”,
“maintaining em-
ployment during the subsequent economic downturn”, and
“having
enough money to pay for rent and buy basic necessities.”
Participants

were asked to indicate their level of worry for each item on a
scale of 1
to 5, with 1 being “not worried at all,” and 5 being “very
worried.” Sum
scores were calculated with a total possible range of 6 to 30 and
re-
coded into a dichotomous variable with a cutoff score of 24 or
greater
as “highly worried.” Cronbach's alpha for measure items was
.70,
C.H. Liu, et al. Psychiatry Research 290 (2020) 113172
2
http://www.cares2020.com
indicating good reliability.
2.2.2. Mental health outcomes
Depression was assessed with the 8-item version of the Patient
Health Questionnaire (PHQ-8, Kroenke et al., 2009) which
assessed
frequency of depressive symptoms in the past two weeks on a
scale of 0
(“not at all”) to 3 (“nearly every day”). Sum scores of the PHQ-
8 had a
total possible range of 0 to 24 and were recoded dichotomously
based
on a cutoff score of 10 or higher (Wu et al., 2019).
Anxiety was assessed with the Generalized Anxiety Disorder
Scale
(GAD-7, Spitzer et al., 2006) a widely used measure assessing

the fre-
quency of anxiety symptoms in the past two weeks on a scale of
0 to 3,
with 0 being “not at all” and 3 being “nearly every day.” Sum
scores
ranged from 0 to 21. Following the convention of other studies
(Plummer et al., 2016), responses were recoded dichotomously
based
on a cutoff score of 10 or higher to determine elevated anxiety.
The PTSD Checklist—Civilian Version (PCL-C), a validated
17-item
measure, was administered to assess PTSD symptoms (Weathers
et al.,
1993). Participants indicated how much they were bothered by
pro-
blems and experiences in response to stressful life events in the
past
month, with 1 as “not at all” and 5 as “extremely.” Sum scores
of the 17
items were calculated and created into a dichotomous variable
with a
cutoff score of 45 or greater, based on the psychometric
properties for
the measure and as suggested by the National Center for PTSD
(Blanchard et al., 1996).
2.2.3. Statistical analyses
The variables were normally distributed, with predictors
indicating
acceptable levels of collinearity (VIF < 5). To identify potential
risk
and protective factors of mental health symptoms, three logistic
re-
gression models were performed to examine depression,

anxiety, and
PTSD symptoms as primary outcomes. Resilience, distress
tolerance,
perceived social support, instrumental social support,
loneliness, and
COVID-19-specific worry were entered as predictors in
unadjusted
models. Age, gender, income, and race were entered in each of
the three
adjusted models. All variables were binary with exception to
age and
income, which were continuous. Two-tailed p-values were used.
To
guard against Type I error, Bonferroni-adjustments were made
to con-
sider the 8 predictors and 4 covariates used in each model (.05/
12=.004). Our results and interpretations are therefore based on
a
significance set at p<.004 (note that the significance in the
tables re-
main unadjusted to provide more rather than less information to
the
reader). All analyses were performed using SPSS 25.0.
3. Results
Table 1 shows demographic characteristics of our participants
and
descriptive data on all predictors and outcomes. The sample was
ra-
cially and ethnically diverse, with 59.6% White, 21.2% Asian,
5.3%
Black, 6.0% Hispanic/Latino, 0.1% AI/NA, 6.2% mixed race,
and 1.4%
indicating another race. The majority of respondents were
women

(81.3%), U.S.-born (86.3%), employed (66.7%), students
(61.3%), and
those who earned less than $50,000 per year (82.1%). Among
those
identifying as students, 89.7% were enrolled as full-time and
7.3% were
international students. Overall, participants scored as having
high
loneliness (61.5%), low resilience (72.0%), and low distress
tolerance
(74.1%). At the same time, the majority of respondents reported
having
high levels of social support (family, partners, peer, and
instrumental).
Finally, 43.3% of our sample had high levels of depression
(PHQ-8
scores ≥ 10), 45.4% had high anxiety scores (GAD-7 scores ≥
10) and
31.8% had high levels of PTSD symptoms (PCL-C scores ≥ 45).
Table 2 displays the associations between predictors and mental
health outcomes in each of the three models adjusted for the
age,
gender, race, and income. The results described here pertain
only to
significance set at p<.004 with Bonferroni corrections.
Predictors that
were significantly associated with depression, anxiety, and
PTSD
Table 1
Demographic characteristics and variable descriptives from
Wave 1 of CARES
2020.
Factors Means (range) or %

Age (years) 24.5 (18.0 – 30.9)
18-21 28.6 %
22-26 34.7 %
26-30 36.6 %
Gender
Men 14.1 %
Women 81.3 %
Other gender 4.6 %
Race
White 59.6 %
Asian 21.2 %
Black 5.3 %
Hispanic or Latinx 6.0 %
American Indian/Native American 0.1 %
Mixed 6.2 %
Other 1.4 %
U.S.-born
Yes 86.3 %
No 13.7 %
Employed
Yes 66.7 %
No 33.3 %
Individual Income (USD/year)
No income 11.8 %
< $25,000 45.9 %
$25,000 - $49,999 24.4 %
$50,000 – $74,999 11.6 %
$75,000 – $99,999 2.6 %
$100,000 – $124,999 2.1 %
$125,000 – $149,999 0.3 %

$150,000 - $174,999 0.3 %
$175,000 - $199,999 0.6 %
$200,000 - $249,999 0.2 %
≥$250,000 0.2 %
Student
Yes 61.3 %
No 38.7 %
Student Enrollment Status (students only)
Full time 89.7 %
Part time 8.7 %
Other 1.6 %
International Student
Yes 7.3 %
No 92.7 %
Loneliness (LS-SF) 6.1 (3.0 – 9.0)
<6 38.5 %
≥6 61.5 %
COVID-19-specific worry 15.9 (6.0 – 30.0)
<24 89.9 %
≥24 10.1 %
Resilience (CD-RISC-10) 26.0 (4 – 40)
<30 72.0 %
≥30 28.0 %
Distress tolerance (DTS) 3.3 (1.0 – 5.0)
<4 74.1 %
≥4 25.9 %
Family social support (MSPSS) 5.1 (1.0 – 7.0)
<5 37.3 %

≥5 62.7 %
Partner social support (MSPSS) 5.6 (1.0 – 7.0)
<5 26.3 %
≥5 73.7 %
Peer social support (MSPSS) 5.7 (1.0 - 7.0)
<5 16.9 %
≥5 83.1 %
Instrumental social support (2-Way SSS) 16.6 (1.0 – 20.0)
<16 30.1 %
≥16 69.9 %
Depression (PHQ-8) 9.0 (0 – 24.0)
<10 56.7 %
≥10 43.3 %
Anxiety (GAD-7) 9.4 (0 - 21.0)
<10 54.6 %
(continued on next page)
3
included loneliness (OR range = 1.98 – 2.72), COVID-19-
specific worry
(OR range = 2.87 – 5.05), and distress tolerance (OR range =
0.22 –
0.42). Specifically, those who endorsed high levels of loneliness
and
worries about COVID-19 and low levels of distress tolerance

were more
likely to score above the clinical cutoffs for depression,
anxiety, and
PTSD. Those with high levels of resilience were less likely to
score
above the cutoff for depression and anxiety. Those with high
levels of
family support were less likely to score above the clinical cutoff
for
depression and PTSD (OR = 0.46 and 0.44, respectively).
Instrumental
support was negatively associated with depression. No
associations
were obtained between support from partners and friends.
In analyses of associations between covariates and outcomes,
age
and income were not associated with depression, anxiety, or
PTSD.
With regard to gender, men who identified as transgender were
more
likely to report high levels of PTSD (OR = 4.20, CI = 1.62 –
10.89,
p=.003); no differences were observed between men and
women. Asian
Americans compared to Whites were less likely to report high
levels of
depression (OR = 0.50, CI = 0.33 – 0.76, p=.001) and PTSD
(OR = 0.40, CI = 0.25 – 0.64, p<.001). Asians Americans and
Hispanic/Latinos were less likely to report high levels of
anxiety
(OR = 0.35, CI = 0.24 – 0.53, p<.001, OR = 0.35, CI = 0.18 –
0.68,
p=.00, respectively).
4. Discussion

Our findings highlight major psychological challenges faced by
young adults during the initial weeks of the COVID-19
pandemic. At
least one-third of young adults reported having clinically
elevated le-
vels of depression (43.3%), anxiety (45.4%), and PTSD
symptoms
(31.8%). The rates of depression, anxiety, and PTSD in our
study are
considerably higher compared to prior studies that have used the
same
cut points (PHQ-8 ≥ 10; GAD-7 ≥ 10; and PCL-C ≥ 45). For
instance,
PHQ-8 data collected from a study on U.S. adults in 2006
yielded a
prevalence of 6.2% among 18-24-year-olds and a prevalence of
13.1%
among 25-34-year-olds (Kroenke et al., 2009). Studies using the
GAD-7
showed the following rates among similar groups: U.S. primary
care
patients (23.0%; Spitzer et al., 2006), U.S. college students
(21.0%;
Martin et al., 2014), and U.S. non-veteran community college
students
(17.4%; Fortney et al., 2016). Finally, studies using a cutoff of
≥ 45 on
the PCL-C to assess PTSD in trauma survivors showed the
following
rates: U.S. patients following hospital discharge from traumatic
ortho-
pedic injury after one year (22.0%; Archer et al., 2016) and
survivors
from the Wenchuan, China earthquake also after one year

(26.3%;
Zhang et al., 2011). The high rates from our sample may reflect
ongoing
distress, as we measured the symptoms in the weeks following
the
government directives for closures. Young adults may have
been par-
ticularly distressed in managing school or work responsibilities
during
this time while having no sense of certainty regarding the
pandemic's
end. As well, the high rate of mental health concerns among
study
participants may be partially attributable to the specific
characteristics
of our sample; given that the study was launched on the East
Coast, our
young adult respondents may have been located at pandemic
“hot
spots,” with proximity to a greater number of COVID-19 cases
poten-
tially being an added stressor for our sample.
Strikingly, the majority of respondents reported feeling lonely
during the first two months of the pandemic, as well as having
low
resilience and low ability to tolerate distress. However, the
majority
reported having social support from family, partners, and peers,
as well
as instrumental support during this time. We note that the
absolute
rates of low perceived social support seem problematic. For
instance,
approximately 37% of respondents reported low family support.
These

Table 1 (continued)
Factors Means (range) or %
≥10 45.4 %
PTSD (PCL-C) 38.3 (17.0 – 85.0)
<45 68.2 %
≥45 31.8 %
N = 898
Table 2
Odds ratios and confidence intervals for mental health outcomes
from Wave 1 of CARES 2020.
Factors PHQ-8 – DepressionAdjusted ORa(95% CI) GAD-7 –
AnxietyAdjusted ORa(95% CI) PTSD AdjustedAdjusted
ORa(95% CI)
Loneliness (LS-SF)
<6 1.0 1.0 1.0
≥6 2.72 (1.92 – 3.87) ⁎ ⁎ ⁎ 1.98 (1.41 – 2.77) ⁎ ⁎ ⁎ 2.31 (1.55
– 3.43) ⁎ ⁎ ⁎
COVID-19-specific worry
<24 1.0 1.0 1.0
≥24 2.87 (1.67 – 4.94) ⁎ ⁎ ⁎ 4.12 (2.33 – 7.29) ⁎ ⁎ ⁎ 5.05 (2.92
– 874) ⁎ ⁎ ⁎
Resilience (CD-RISC-10)
<30 1.0 1.0 1.0
≥30 0.56 (0.38 – 0.83) ⁎ ⁎ 0.44 (0.30 – 0.64) ⁎ ⁎ ⁎ 0.70 (0.46 –
1.07)
Distress tolerance (DTS)

<4 1.0 1.0 1.0
≥4 0.36 (0.24 – 0.54) ⁎ ⁎ ⁎ 0.42 (0.28 – 0.62) ⁎ ⁎ ⁎ 0.22 (0.13
– 0.37) ⁎ ⁎ ⁎
Family social support (MSPSS)
<5 1.0 1.0 1.0
≥5 0.46 (0.32 – 0.66) ⁎ ⁎ ⁎ 0.64 (0.44 – 0.91)* 0.44 (0.30 –
0.64)⁎ ⁎ ⁎
Partner social support (MSPSS)
<5 1.0 1.0 1.0
≥5 1.26 (0.84 – 1.88) 1.32 (0.89 – 1.96) 1.00 (0.66 – 1.52)
Peer social support (MSPSS)
<5 1.0 1.0 1.0
≥5 1.05 (0.68 – 1.62) 1.27 (0.83 – 1.96) 0.88 (0.56 – 1.39)
Instrumental social support (2-Way SSS)
<16 1.0 1.0 1.0
≥16 0.60 (0.41 – 0.86)⁎ ⁎ 0.67 (0.46 – 0.96)* 0.63 (0.43 –
0.93)*
N = 898
⁎ p<.05
⁎ ⁎ p<.01
⁎ ⁎ ⁎ p<.001 (two-tailed, without Bonferroni adjustment),
a Adjusted covariates include age, race, gender, individual
income
4
findings highlight major psychological challenges currently

faced by
young adults during the initial weeks of the COVID-19
pandemic.
Our study also identified factors associated with clinical levels
of
depression, anxiety, and PTSD symptoms. High loneliness and
low
distress tolerance levels were consistently associated with high
levels of
depression, anxiety, and PTSD. High levels of resilience were
associated
with low anxiety. Social support from family was associated
with low
levels of depression and PTSD symptoms, whereas support from
part-
ners or friends was not associated with any mental health
outcomes.
High levels of instrumental support were associated with low
levels of
depression.
Our data is consistent with findings demonstrating loneliness as
a
risk factor for mental health (Banerjee et al., 2020; Hawkley
and
Cacioppo, 2010; Okruszek et al., 2020); this is particularly
salient with
government directives for social distancing and isolation.
Feeling cut off
from social groups may lead one to feel vulnerable and
pessimistic
about one's circumstances, altogether producing negative mood
states
and anxiety (Muyan et al., 2016) that are further heightened
during a

pandemic. The high levels of reported loneliness in our sample
and its
association with depression, anxiety, and PTSD symptoms
underscore
the severity of experiences of young adults during the
pandemic.
Distress tolerance, or one's ability to manage and tolerate
emotional
distress, was strongly associated low levels of depressive a nd
anxiety,
and PTSD symptoms; individual resilience was associated with
low le-
vels of depression and anxiety symptoms, but not PTSD.
Individual
resilience, which encompasses personal competence and trust in
one's
instincts (Connor and Davidson, 2003), has been associated with
low
levels of depression, anxiety, and PTSD symptomatology after
disasters
(Blackmon et al., 2017). One's perceived ability to tolerate
negative or
aversive emotional and/or physical states may be more
protective than
the personal qualities that comprise psychological resilience,
especially
for those experiencing symptoms of PTSD during a pandemic.
The
pandemic is worldwide stressor without a foreseeable endpoint,
and the
effects of the pandemic cannot be controlled by a single
individual.
Furthermore, the pandemic simultaneously impacts various
domains
(e.g., financial, relational, and health) with this stress

potentially ex-
acerbating the sensations associated with PTSD symptoms. As
such,
psychological resilience that is typically associated with
overcoming
setbacks may not be sufficient for protecting against PTSD
symptoms
within the first several weeks of a widespread pandemic.
Interventions
that target distress tolerance, such as mindfulness-based
interventions,
may be more effective than cognitive interventions targeting
core be-
liefs about the self especially for those with PTSD symptoms
(Nila et al.,
2016). Longitudinal approaches would help to examine this
possibility
further.
Emotional support from family but not from friends and
significant
others was associated with low levels of depression and PTSD.
Friends
and significant others may have or are perceived to have less
capacity
to validate other's emotional experiences during a pandemic,
con-
sidering that they may be young adults who are experiencing
similar
struggles. Emotional support provided by family may be more
stable
and coupled with the provision of material resources that young
adults
may still receive from parents. Our findings are consistent with
prior
work showing that family support but not friend and partner

support
mediates the effects of stress on health (Lee et al., 2018).
Family sup-
port may be more meaningful in providing reassurance to young
adults,
considering the possible concrete needs during the pandemic.
Instrumental support, or tangible assistance, may be an
important
factor for the mental health of young adults during the
immediate
weeks of the COVID-19 pandemic onset given that many were
faced
with acute disruptions, such as unemployment, financial stress,
and
relocation following university campus closures. However,
instru-
mental support was not significantly associated with any of the
out-
comes after adjusting the p-value to .004. Additional research is
needed
to clarify the respective roles on both emotional and instrument
support
given variations in their potential effects on depression,
anxiety, and
PTSD.
Our newly developed COVID-19-related worry measure
uniquely
predicted mental health symptoms, underscoring how the
specific fea-
tures of this pandemic give rise to acute stress. The stress
resulting from
lifestyle changes due to features of COVID-19 itself may lead
to greater

mental health concerns distinct from the endorsement of other
risks.
Our analyses showed that the six items in our measure were
reliable,
and the total subscale score was significantly associated with
the
symptoms assessed in this study; however, additional work is
required
to determine the validity of this measure.
In general, Asian Americans were less likely to report high
levels of
mental health symptoms compared to Whites, with
Hispanic/Latinx
respondents also being less likely to report high anxiety. Asian
and
Latinx immigrants compared to those who are born in the U.S.
are less
likely to endorse psychological distress (Dey and Lucas, 2006;
Takeuchi et al., 2007). It is possible that other experiences such
as
ethnic identity, social networking, and family cohesion serve as
a pro-
tective factor for mental health, especially for non-U.S.-born
partici-
pants (Leong et al., 2013). The under-recognition of distress
symptoms
may also be possible among ethnic minorities (Liu et al., 2020).
Al-
though our sample size of gender minorities was small, men
who
identified as transgender were more likely to report a high level
of
PTSD symptoms, consistent with prior research (Reisner et al.,
2016;
Shipherd et al., 2011). Greater attention to gender differences in

mental
health symptoms as well as a deeper study regarding the
specific ex-
periences faced by racial/ethnic and gender minorities during
pan-
demic is warranted.
The cross-sectional design limits our ability to infer causality
in-
volved in leading to mental health problems. We used a
convenience
sample, and caution must be taken in the generalizability of our
find-
ings to the broader population of young adults in the U.S. given
the
uneven sampling of subgroups. The reliance of self-report itself
has
limitations, such that it may be prone to misinterpretation.
Future
analyses with the anticipated waves of data collection will
enable us to
examine the association of our predictors to outcome measures
of
mental health and to adjust for additional confounds. As well,
we will
have an opportunity to examine potential moderation effects to
un-
derstand whether outcomes vary by circumstances or individual
char-
acteristics, such as socioeconomic capital, social support type,
distress
tolerance, and resilience.
To our knowledge, our study is the first prospective cohort
study to
assess mental health outcomes and risk and resilience factors in

U.S.
young adults during the first several weeks of the COVID-19
pandemic.
In our study, one in three U.S. young adults reported clinical
cut-off
symptoms of depression, anxiety, and PTSD as well as high
levels of
loneliness. We present new evidence that signifies the roles of
lone-
liness, distress tolerance, family support, and COVID-19-related
worry
on mental health outcomes during the first month of the
COVID-19
pandemic. Mental health interventions should incorporate these
con-
structs to help mediate the impact of COVID-19 on adverse
mental
health status among U.S. young adults.
CRediT authorship contribution statement
Cindy H. Liu: Conceptualization, Methodology, Formal
analysis,
Investigation, Writing - original draft, Writing - review &
editing,
Project administration, Supervision, Funding acquisition. Emily
Zhang:
Data curation, Writing - original draft, Writing - review &
editing,
Project administration. Ga Tin Fifi Wong: Data curation,
Writing -
original draft, Project administration. Sunah Hyun: Writing -
review &
editing. Hyeouk “Chris” Hahm: Conceptualization, Writing -
review &
editing, Supervision, Funding acquisition.

Declaration of Competing Interest
There are no conflicts of interest to declare.
5
Acknowledgments
Support for this manuscript was provided through the National
Science Foundation (2027553) award (to C.H.L. and H.C.H.), a
Mary A.
Tynan Faculty Fellowship and a NIMH K23 MH 107714-01 A1
award
(to C.H.L.), as well as a T32 MH 16259-39 award (to. S.H.).
Supplementary materials
Supplementary material associated with this article can be
found, in
the online version, at doi:10.1016/j.psychres.2020.113172.
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https://doi.org/10.1207/s15327752jpa5201_2Factors associated
with depression, anxiety, and PTSD symptomatology during the
COVID-19 pandemic: Clinical implications for U.S. young adult
mental healthIntroductionMethodsStudy
populationMeasuresRisk and protective factorsMental health
outcomesStatistical analysesResultsDiscussionCRediT
authorship contribution statementDeclaration of Competing
Interestmk:H1_13Acknowledgmentsmk:H1_15Supplementary
materialsReferences
Contents lists available at ScienceDirect
Psychiatry Research
journal homepage: www.elsevier.com/locate/psychres
Review article

PTSD symptoms in healthcare workers facing the three
coronavirus
outbreaks: What can we expect after the COVID-19 pandemic
Claudia Carmassia, Claudia Foghia, Valerio Dell'Ostea,b,⁎ ,
Annalisa Cordonea,
Carlo Antonio Bertellonia, Eric Buic, Liliana Dell'Ossoa
a Department of Clinical and Experimental Medicine,
University of Pisa, Pisa, Italy
b Department of Biotechnology Chemistry and Pharmacy,
University of Siena, Siena, Italy
c Department of Psychiatry, Massachusetts General Hospital,
Harvard Medical School, Boston, MA, USA
A R T I C L E I N F O
Keywords:
Corona
Mental health
Nurses
Physicians
Psychological distress
Stress
A B S T R A C T
The COronaVIrus Disease-19 (COVID-19) pandemic has
highlighted the critical need to focus on its impact on
the mental health of Healthcare Workers (HCWs) involved in
the response to this emergency. It has been con-
sistently shown that a high proportion of HCWs is at greater
risk for developing Posttraumatic Stress Disorder
(PTSD) and Posttraumatic Stress Symptoms (PTSS). The
present study systematic reviewed studies conducted in

the context of the three major Coronavirus outbreaks of the last
two decades to investigate risk and resilience
factors for PTSD and PTSS in HCWs. Nineteen studies on the
SARS 2003 outbreak, two on the MERS 2012
outbreak and three on the COVID-19 ongoing outbreak were
included. Some variables were found to be of
particular relevance as risk factors as well as resilience factors,
including exposure level, working role, years of
work experience, social and work support, job organization,
quarantine, age, gender, marital status, and coping
styles. It will be critical to account for these factors when
planning effective intervention strategies, to enhance
the resilience and reduce the risk of adverse mental health
outcomes among HCWs facing the current COVID-19
pandemic.
1. Introduction
The outbreak of Corona Virus Disease-19 (COVID) that
emerged in
December 2019 in Wuhan (China), quickly spread outside of
China,
leading the World Health Organization (WHO) Emergency
Committee
to declare a Public Health Emergency of International Concern
(PHEIC)
on January 30th 2020 (Nishiura, 2020), and a pandemic on
March 11,
2020. The SARS-CoV2 – the virus responsible for COVID-19 –
was
isolated by 7th January 2020, and belongs to the same viral
family as
the coronavirus syndrome (SARS-CoV) and the Middle East
respiratory
coronavirus syndrome (MERS-CoV). Both of these coronavirus-
based

respiratory syndromes infected over 10,000 cases in the past
two dec-
ades, with overall mortality rates as high as 11% and 35%,
respectively
(Peeri et al., al.,2020; de Wit et al., 2016; Leung et al., 2004;
WHO, 2004). Compared to the Severe Acute Respiratory
Syndrome
(SARS) and the Middle East Respiratory Syndrome (MERS), the
Corona
Virus Disease-19 (COVID-19) has a greater transmission rate
but a
lower, though still significant, fatality rate (Peeri et al., 2020;
Huang et al., 2020). To date, with more than 14 million infected
worldwide and a spread that is far from being contained,
investigating
the psychological impact of this pandemic on healthcare
workers
(HCWs) including physicians and nurses, has become
increasingly im-
portant.
In the last two decades, first responders’ mental health
outcomes has
been the focus of increasing attention, particularly in the
aftermath of
September 11 2001, terrorist attacks that shed light on the risks
they
are exposed to when operating in emergency settings, as they
may be
affected by physical and mental disorders, such as burnout and
post-
traumatic stress disorder (PTSD) (Perlman et al., 2011;
Carmassi et al.,
2016, 2018; Martin et al., 2017). The DSM-5 (APA, 2013)
indicates that

"experiencing repeated or extreme exposure to aversive details
of the trau-
matic event(s)" can be considered as potentially traumatic
events (cri-
terion A4: e.g. first responders collecting human remains, police
officers
repeatedly exposed to details of child abuse).
Healthcare Workers (HCWs) in emergency care settings are
parti-
cularly at risk for PTSD because of the highly stressful work-
related
situations they are exposed to, that include: management of
critical
medical situations, caring for severely traumatized people,
frequent
witnessing of death and trauma, operating in crowded settings,
inter-
rupted circadian rhythms due to shift work) (Figley, 1995;
Crabbe et al.,
Received 1 May 2020; Received in revised form 18 July 2020;
Accepted 18 July 2020
⁎ Corresponding author at: Department of Clinical and
Experimental Medicine, University of Pisa, Via Roma 67, 56100
Pisa, Italy.
E-mail address: [email protected] (V. Dell'Oste).
Psychiatry Research 292 (2020) 113312
Available online 20 July 2020
0165-1781/ © 2020 Elsevier B.V. All rights reserved.
T

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20.113312&domain=pdf
2004; Cieslak et al., 2014; Berger et al., 2012; Hegg-Deloye et
al., 2013;
Garbern et al., 2016). PTSD rates have been reported to range
from 10
to about 20% (Grevin, 1996; Clohessy and Ehlers, 1999;
Robertson and
Perry, 2010; DeLucia et al., 2019), with even higher PTSD rates
(8% to
30%) among Intensive Care Unit (ICU) staff, (Mealer et al.,
2009;
Karanikola et al., 2015; Machado et al., 2018).
Although most individuals prove to be resilient after being
exposed
to a traumatic event (Bonanno et al., 2007), several risk factors
may
compromise the effectiveness of adaptation, including prior
psychiatric
history, female sex, lack of social support (Brewin et al., 1999;
Ozer et al., 2003; Carmassi et al., 2020a, 2020b), having young
children
(Yehuda et al., 2015; Bryant 2019); experiencing feelings of
help-
lessness during the trauma or intensity of emotions when
exposed (i.e.,

anger, peritraumatic distress) (Vance et al., 2018; Carmassi et
al.,
2017). On the other hand, resilience, defined as the capacity to
react to
stress in a healthy way through which goals are achieved at a
minimal
psychological and physical cost (Epstein and Krasner, 2013),
plays a
key role in mitigating the impact of traumatic events and hence
redu-
cing PTSS, enhancing at the same time the quality of care
(Wrenn et al.,
2011; Ager et al., 2012; Haber et al., 2013; McGarry et al.,
2013;
Craun and Bourke, 2014; Hamid and Musa, 2017; Colville et al.,
2017;
Cleary et al., 2018; Winkel et al., 2019).
This interplay of risk and resilience factors becomes even more
complex and challenging when applied in the context of an
infectious
epidemic. This statement is first supported by the fact that, as
previous
studies outlined, during epidemics a high percentage of HCWs,
(up to 1
in 6 of those providing care to affected patients), develops
significant
stress symptoms (Lu et al., 2006; McAlonan et al., 2007) It is
worth
considering that in epidemic contexts HCWs are first in line
facing the
clinical challenges intrinsically linked to the course of the
disease while
under the constant personal threat of being infected or
representing a
source of infection.

The current COVID-19 pandemic is characterized by some
relevant
features that increase the risk for PTSD among HCWs
addressing the
emergency, such as the unprecedented numbers of critically ill
patients,
with an often unpredictable course of the disease, high mortality
rates
and lack of effective treatment, or treatment guidelines (Wang,
2020;
Peeri et al., 2020). Thus, the burden of the current outbreak on
healthcare providers deserves the closest attention, as it is
extremely
likely that health care workers involved in the diagnosis,
treatment and
care of patients with COVID-19 are at risk of developing
psychological
distress and other mental health symptoms (Bao et al., 2020; Lai
et al.,
2020; Carmassi et al., 2020c)
The aim of the present paper is therefore to systematically
review
the studies investigating the potential risk and resilience factors
for the
development of PTSD symptoms in HCWs who faced the two
major
Coronavirus outbreaks that occurred worldwide in the last two
decades,
namely the SARS and the MERS, as well as the ongoing
COVID-19
pandemic, in order to outline effective measures to reduce the
HCWs’
psychiatric burden during the current crisis affecting healthcare
sys-

tems all over the world.
2. Methods
2.1. Search strategy
We reviewed articles indexed in the electronic database PubMed
until 20th April 2020. No time limit was set in regard to the
year of
publication. The search terms were combined with the Bool ean
op-
erator as follows: “(Post-traumatic stress OR Post-traumatic
stress dis-
order OR Post-traumatic stress symptoms OR PTSD OR PTSS)
AND
(Severe Acute Respiratory Syndrome OR SARS OR Middle East
Respiratory Syndrome OR MERS OR Corona Virus Disease 19
OR
COVID-19 OR Coronavirus)”. Furthermore, relevant articles
were ex-
tracted from the references section of the manuscripts found in
the
initial search, to complete our search.
2.2. Eligibility criteria
We included articles that met the following inclusion criteria:
ori-
ginal studies on humans investigating possible risk and/or
resilience
factors for PTSD symptoms in HCWs facing the coronavirus
outbreaks
of SARS, MERS and COVID-19. Articles in print or published
ahead of
print were accepted. The exclusion criteria were: (a) studies
involving

general population samples that did not consider a sub-sample
of
HCWs; (b) studies examining other mental health symptoms but
not
PTSS; (c) studies assessing PTSS but not considering potential
risk and
resilience factors; (d) literature reviews; (e) full text not
available; (f)
not available in English.
2.3. Study selection
The first author screened each study for eligibility by reading
the
title and abstract. Any uncertainties about eligibility were
clarified
through discussion among all authors. Decisions for inclusion or
ex-
clusion are summarized in a flowchart according to PRISMA re-
commendations, usually used to conduct meta-analyses and
systematic
reviews of randomized clinical trials, but that have also been
used for
other types of systematic reviews such as our present one
(Moher et al.,
2009).
3. Results
3.1. Process of study selection
The study selection process is outlined in a flow -chart (Fig. 1).
The
electronic database search returned 263 publications. Following
a
preliminary screening of the titles and abstracts, 47 articles

were con-
sidered of potential relevance, their eligibility was assessed by
means of
a full text examination. Twenty-four of these studies, published
be-
tween 2004 and 2020, were included in this review. The main
reasons
for study exclusion were: the absence of a HCW sample or sub-
sample,
the lack of data regarding PTSS and/or about possible risk or
resilience
factors related to psychopathology.
3.2. Characteristics of included studies
The key characteristics of the studies included are summarized
in
Table 1. All retrieved studies were published between January
2004
and April 2020. Nineteen studies were on the SARS 2003
outbreak, two
on the MERS 2012 outbreak, and three on the ongoing Covid-19
out-
break. Nine studies were on a mixed population in which HCWs
re-
presented a sub-sample (Bai et al., 2004; Chong et al., 2004;
Kwek et al., 2006; Reynolds et al., 2007; Lancee et al., 2008;
Wu et al.,
2009; Mak et al., 2010; Wing and Leung, 2012; Li et al., 2020)
while all
other studies included HCWs only. Finally, five studies
included spe-
cifically survivors from the infection (Kwek et al., 2006; Lee et
al.,
2007; Mak et al., 2010; Wing and Leung, 2012; Ho et al., 2005).

3.3. PTSD and PTSS risk factors in HCWs facing the
coronavirus outbreaks
3.3.1. Level of exposure
Ten studies (Chong et al., 2004; Maunder et al., 2004; Lin et
al.,
2007; Su et al., 2007; Styra et al., 2008; Wu et al., 2009; Lee et
al.,
2018; Lai et al., 2020; Kang et al., 2020; Jung et al., 2020)
highlighted
the role of exposure level, such as working in high-risk wards
or in
front-line settings during the Coronavirus outbreaks, as the
major risk
factor for developing PTSS and PTSD. Particularly, they
pointed out the
relevance of perceived threat for health and life and the
experienced
feelings of vulnerability as mediating factors. Most of these
studies re-
ported on the 2003 SARS outbreak. Lin et al. (2007) showed
higher
rates of PTSD (21,7%) among 66 emergency department staff
compared
to 26 HCWs of non-emergency departments (i.e., psychiatric
ward,
C. Carmassi, et al. Psychiatry Research 292 (2020) 113312
2
13%). Wu et al. (2009) investigated a sample of 549 HCWs in
Beijing

(China), including administrative staff, finding 2 to 3 times
higher PTSS
rates among respondents who worked in high-risk locations and
per-
ceived high SARS-related risks, beside an increased risk for
subsequent
alcohol abuse/dependence. This latter resulted significantly
related
with hyper-arousal symptoms. A further study in Toronto (Styra
et al.,
2008) confirmed the impact of operating in a high-risk unit, and
first
reported that caring for only one SARS patient was related to a
higher
risk than caring for multiple SARS patients. A recent study on
147
nurses who worked in MERS units during the outbreak found
higher
PTSD rates among emergency HCWs than among non-
emergency ones
(Jung et al., 2020). To date, two studies have explored this issue
in the
COVID-19 pandemic. Li et al. (2020) found among 526 nurses,
that
those who worked on the frontline appeared to be less prone to
de-
veloping PTSS compared to second-line ones; conversely
Kang et al. (2020) in a large study on 994 HCWs in Wuhan
reported the
exposure level to infected people, more broadly including
colleagues,
relatives or friends, to be a risk factor for mental health
problems, in-
cluding PTSS.
3.3.2. Occupational role

Five studies, four on the SARS epidemic and one on the
COVID-19
pandemic, highlighted the occupational role as a major risk
factor for
PTSD or PTSS in Coronavirus outbreaks. Maunder et al. (2004)
found
on a sample of 1557 HCWs collected in Toronto, higher PTSS
rates
among nurses and explained this finding by means of the longer
contact
and higher exposure to patients of the nursing staff. A study on
96
emergency HCWs, assessed six months after the 2003 SARS
outbreak,
revealed a greater burden of PTSS among nurses than among
physicians
(Tham et al., 2004). A further study by Phua et al. (2005)
confirmed
this finding in a sample of 99 HCWs. Finally, a most recent
study on
1257 hospital physicians and nurses caring for COVID-19
patients
reached the same conclusion (Lai et al., 2020).
3.3.3. Age and gender
Three studies on the SARS outbreak and one on the COVID-19
pandemic reported that younger HCWs had a greater risk of
developing
PTSS (Sim et al., 2004; Su et al., 2007; Wu et al., 2009). From
a wider
perspective, further studies pointed out an association between
fewer
years of work experience and an increased PTSS risk in HCWs,
as de-

scribed in two SARS studies (Chong et al., 2004; Lancee et al.,
2008)
and in one COVID-19 study (Lai et al., 2020). As far as gender
is
concerned, while one recent study on COVID-19 reported a
higher risk
for the female HCWs, a previous study involving 1257 HCWs in
a ter-
tiary hospital affected by SARS found an increased risk of PTSS
among
males (Chong et al., 2004).
3.3.4. Marital status
Three studies focused on the relevance of marital status, two of
which referred to the SARS outbreaks and one to the current
COVID-19
pandemic. Chan and Huak (2004) in a study on 661 HCWs in
Singapore
showed that those who were not married were more adversely
affected
than married ones. In contrast, a further study in Singapore
(Sim et al.,
2004) found a positive association between post-traumatic
morbidities
and being married. Likewise, a recent case control study on
HCWs fa-
cing the COVID-19 pandemic showed that married, divorced or
wi-
dowed operators reported higher scores in vicarious
traumatization
symptoms compared to unmarried HCWs (Li et al., 2020).
3.3.5. Quarantine, isolation and stigma
Three SARS studies on Chinese hospital staff members (Bai et

al.,
2004; Reynolds et al., 2007; Wu et al., 2009) and one on the
MERS
outbreak (Lee et al., 2018) consistently reported high levels of
PTSS
among HCWs who had been quarantined. More specifically,
Bai et al. (2004) examining 338 HCWs in an East Taiwan
hospital found
that 5% of them suffered from acute stress disorder, with
quarantine
being the most frequently associated factor, and a further 20%
felt
stigmatized and rejected in their neighborhood because of their
hospital
work, with also 9% reporting reluctance to work and/or
considering
quitting their job. Similar findings emerged from a Canadian
SARS
study on 1057 subjects (Reynolds et al., 2007), in which
quarantined
HCWs reported more PTSS than non-HCWs quarantined
individuals.
Moreover, in a study on MERS outbreak, Lee et al. (2018)
assessed PTSS
experienced by 359 university HCWs who cared for infected
patients,
observing that quarantined HCWs had a higher risk of
developing PTSS
which persisted over time, particularly sleep and numbness-
related
symptoms. More in general, social isolation and separation from
family
was found to be associated with higher rates of PTSS in SARS
outbreak,
as well as having friends or close relatives with the infection

(Maunder et al., 2004; Chong et al., 2004; Wu et al., 2009).
3.3.6. Previous psychiatric disorders
Three studies on SARS have stressed the presence of previous
psy-
chiatric disorders as a risk factor for the development of PTSS
Fig. 1. PRISMA flowchart of studies selection process.
3
T
ab
le
1
M
ai
n
ch
ar
ac
te
ri
st
ic
s
of

in
cl
u
d
ed
st
u
d
ie
s.
St
u
d
y
O
u
tb
re
ak
T
yp
e
Sa
m
p
le
P
T
SS

/P
T
SD
m
ea
su
re
s
M
ai
n
ge
n
er
al
fi
n
d
in
gs
M
ai
n
ri
sk
an
d
re
si

li
en
ce
fa
ct
or
s
B
ai
et
al
.
(2
0
0
4
)
SA
R
S
C
ro
ss
-s
ec
ti
on
al
st
u

d
y
5
5
7
h
os
p
it
al
st
aff
m
em
be
rs
(H
C
W
s
n
=
4
0
2
;
ad
m
in

is
tr
at
iv
e
p
er
so
n
n
el
n
=
1
5
5
)
SA
R
S-
re
la
te
d
st
re
ss
re
ac
ti
on

s
qu
es
ti
on
n
ai
re
5
%
ac
u
te
st
re
ss
d
is
or
d
er
;
2
0
%
st
ig

m
at
iz
ed
;
an
d
9
%
re
lu
ct
an
ce
to
w
or
k
or
co
n
si
d
er
ed
re
si
gn

at
io
n
R
is
k
fa
ct
or
:
qu
ar
an
ti
n
e
C
h
an
an
d
H
u
ak
(2
0
0
4
)

SA
R
S
C
ro
ss
-s
ec
ti
on
al
st
u
d
y
6
6
1
H
C
W
s
(d
oc
to
rs
an
d
n
u
rs

es
)
Im
p
ac
t
of
E
ve
n
ts
Sc
al
e
2
0
%
IE
S
sc
or
e
>
3
0
;
2
7

%
p
sy
ch
ia
tr
ic
sy
m
p
to
m
s
(3
5
%
of
d
oc
to
rs
an
d
2
5
%
of
n
u

rs
es
)
R
es
il
ie
n
ce
fa
ct
or
s:
Su
p
p
or
t
fr
om
fa
m
il
y/
su
p
er
vi
so
rs
/c

ol
le
ag
u
es
;
w
or
k
or
ga
n
iz
at
io
n
(c
le
ar
d
ir
ec
ti
ve
s/
p
re
ca
u
ti
on

ar
y
m
ea
su
re
s
fr
om
m
an
ag
em
en
t)
C
h
on
g
et
al
.
(2
0
0
4
)

SA
R
S
n
at
u
ra
li
st
ic
,
ob
se
rv
at
io
n
al
st
u
d
y
1
2
5
7
h
os
p
it
al

st
aff
m
em
be
rs
(n
u
rs
es
n
=
6
7
6
;
d
oc
to
rs
n
=
1
3
9
;
h
ea

lt
h
ad
m
in
is
tr
at
iv
e
w
or
ke
rs
n
=
1
4
0
;
ot
h
er
s
n
=
3
0
2
)

Im
p
ac
t
of
E
ve
n
t
Sc
al
e
IE
S
m
ea
n
sc
or
e=
3
4
.8
;
7
5
.3
%
p

sy
ch
ia
tr
ic
sy
m
p
to
m
s
(a
n
xi
et
y
an
d
w
or
ry
in
g,
d
ep
re
ss
io
n
an

d
in
te
rp
er
so
n
al
d
iffi
cu
lt
ie
s,
so
m
at
ic
p
ro
bl
em
s)
in
th
e
in
it
ia
l

p
h
as
e
of
th
e
ou
tb
re
ak
R
is
k
fa
ct
or
s:
m
al
e;
te
ch
n
ic
ia
n
s;
≤

2
ye
ar
s
w
or
k
ex
p
er
ie
n
ce
;
le
ve
l
of
ex
p
os
u
re
M
au
n
d
er
et

al
.
(2
0
0
4
)
SA
R
S
cr
os
s-
se
ct
io
n
al
st
u
d
y
1
5
5
7
H
C
W
s
Im

p
ac
t
of
E
ve
n
ts
Sc
al
e
H
ig
h
er
Im
p
ac
t
of
E
ve
n
t
Sc
al
e
sc
or

es
ar
e
fo
u
n
d
in
n
u
rs
es
an
d
H
C
W
s
h
av
in
g
co
n
ta
ct
w
it
h
SA

R
S
p
at
ie
n
ts
.
R
is
k
fa
ct
or
s:
le
ve
l
of
ex
p
os
u
re
;
n
u
rs
es
;

p
er
ce
iv
ed
th
re
at
fo
r
th
ei
r
h
ea
lt
h
;
so
ci
al
is
ol
at
io
n
Si
m
et
al
.

(2
0
0
4
)
SA
R
S
cr
os
s-
se
ct
io
n
al
st
u
d
y
2
7
7
H
C
W
s
(d
oc
to

rs
n
=
9
1
;n
u
rs
es
n
=
1
8
6
)
Im
p
ac
t
of
E
ve
n
ts
Sc
al
e

9
.4
%
P
T
SS
;
2
0
.6
%
p
sy
ch
ia
tr
ic
m
or
bi
d
it
y
R
is
k
fa
ct
or

s:
yo
u
n
ge
r
ag
e,
be
in
g
m
ar
ri
ed
,
p
sy
ch
ia
tr
ic
m
or
bi
d
it
y,

le
ss
ve
n
ti
n
g,
le
ss
h
u
m
or
,
an
d
le
ss
ac
ce
p
ta
n
ce
.
T
h
am
et

al
.
(2
0
0
4
)
SA
R
S
cr
os
s-
se
ct
io
n
al
st
u
d
y
E
m
er
ge
n
cy
H
C

W
s
(d
oc
to
rs
n
=
3
8
;
n
u
rs
es
n
=
5
8
)
Im
p
ac
t
of
E
ve

n
ts
Sc
al
e
IE
S
sc
or
e
≥
2
6
in
1
3
.2
%
d
oc
to
rs
an
d
2
0
.7
%

n
u
rs
es
;
G
en
er
al
H
ea
lt
h
Q
u
es
ti
on
n
ai
re
-2
8
≥
5
in
1
5
.8

%
d
oc
to
rs
an
d
2
0
.7
%
n
u
rs
es
R
is
k
fa
ct
or
s:
n
u
rs
es
H
o
et

al
.
(2
0
0
5
)
SA
R
S
cr
os
s-
se
ct
io
n
al
st
u
d
y
8
2
H
C
W
s
n
ot

in
fe
ct
ed
an
d
9
7
H
C
W
s
w
h
o
re
co
ve
re
d
fr
om
SA
R
S
Im
p
ac
t
of

E
ve
n
ts
Sc
al
e
(C
h
in
es
e
ve
rs
io
n
)
H
C
W
s
re
co
ve
re
d
re
p
or
te

d
h
ig
h
P
T
SS
in
tr
u
si
on
sy
m
p
to
m
s
an
d
m
or
e
co
n
ce
rn
s
ab
ou
t

ot
h
er
h
ea
lt
h
p
ro
bl
em
s
an
d
d
is
cr
im
in
at
io
n
.
H
C
W
s
n
ot
in

fe
ct
ed
h
ad
st
ro
n
ge
r
fe
ar
re
la
te
d
to
in
fe
ct
io
n
th
an
H
C
W
s
re

co
ve
re
d
;
eq
u
al
co
n
ce
rn
ab
ou
t
in
fe
ct
in
g
ot
h
er
s
(e
sp
ec
ia
ll
y

fa
m
il
y
m
em
be
rs
)
th
an
be
in
g
se
lf
-
in
fe
ct
ed
em
er
ge
d
R
is
k
fa
ct

or
s:
be
in
g
H
C
W
s
su
rv
iv
or
s
P
h
u
a
et
al
.
(2
0
0
5
)
SA
R
S
cr
os

s-
se
ct
io
n
al
st
u
d
y
9
9
H
C
V
s
(d
oc
to
rs
n
=
4
1
;
n
u
rs
e
n

=
5
8
)
Im
p
ac
t
of
E
ve
n
ts
Sc
al
e
1
7
.7
%
IE
S
>
2
6
;
R

is
k
Fa
ct
or
:
n
u
rs
es
R
es
il
ie
n
ce
fa
ct
or
s:
p
os
it
iv
e
co
p
in
g
st
yl

es
(h
u
m
or
an
d
p
la
n
n
in
g)
K
w
ek
et
al
.
(2
0
0
6
)
SA
R
S
cr
os
s-

se
ct
io
n
al
st
u
d
y
6
3
H
C
W
s
SA
R
S
su
rv
iv
or
s
Im
p
ac
t
of
E
ve

n
ts
Sc
al
e
4
1
%
sc
or
ed
in
d
ic
at
iv
e
of
P
T
SD
;
3
0
%
li
ke
ly

an
xi
et
y
an
d
d
ep
re
ss
io
n
.
R
is
k
fa
ct
or
:
be
in
g
H
C
W
su
rv
iv
or

s
M
au
n
d
er
et
al
.
(2
0
0
6
)
SA
R
S
cr
os
s-
se
ct
io
n
al
st
u
d
y

7
6
9
H
C
W
s
(S
A
R
S
an
d
n
o-
SA
R
S
u
n
it
s)
Im
p
ac
t
of
E
ve
n

ts
Sc
al
e
SA
R
S
u
n
it
H
C
W
s
re
p
or
te
d
h
ig
h
er
P
T
SS
,
bu
rn
ou

t,
an
d
p
sy
ch
ol
og
ic
al
d
is
tr
es
s
ra
th
er
th
an
n
o-
SA
R
S
u
n
it
H

C
W
s.
SA
R
S
u
n
it
H
C
W
s
m
or
e
re
d
u
ce
d
p
at
ie
n
t
co
n
ta
ct
an
d
w

or
k
h
ou
rs
.
R
is
k
fa
ct
or
s:
m
al
ad
ap
ti
ve
co
p
in
g
st
ra
te
gi
es
(a
vo

id
an
ce
,
h
os
ti
le
co
n
fr
on
ta
ti
on
,
an
d
se
lf
-
bl
am
e)
.
R
es

il
ie
n
ce
fa
ct
or
s:
tr
ai
n
in
g,
Su
p
p
or
t
fr
om
fa
m
il
y/
su
p
er
vi
so
rs
/c

ol
le
ag
u
es
,
w
or
k
or
ga
n
iz
at
io
n
Le
e
et
al
.
(2
0
0
7
)
SA
R
S

co
h
or
t
st
u
d
y
SA
R
S
su
rv
iv
or
s
(n
on
–H
C
W
s
n
=
4
9
;
H
C
W
s
n

=
3
0
)
Im
p
ac
t
of
E
ve
n
t
Sc
al
e–
R
ev
is
ed
P
ar
ti
ci
p
an
ts
w
it

h
at
le
as
t
m
od
er
at
e
P
T
SS
re
p
or
te
d
3
2
.2
%
In
tr
u
si
on
,
2
0
.0

%
A
vo
id
an
ce
,
an
d
2
2
.2
%
H
yp
er
ar
ou
sa
l.
H
C
W
SA
R
S
su

rv
iv
or
s
w
er
e
m
or
e
d
is
tr
es
se
d
th
an
n
on
–H
C
W
on
e
ye
ar
af
te
r
th
e

ou
tb
re
ak
.
R
is
k
fa
ct
or
s:
be
in
g
H
C
W
su
rv
iv
or
s.
Li
n
et
al
.
(2

0
0
7
)
SA
R
S
cr
os
s-
se
ct
io
n
al
st
u
d
y
6
6
em
er
ge
n
cy
H
C
W

s
an
d
2
6
n
o-
em
er
ge
n
cy
H
C
W
s
D
av
id
so
n
T
ra
u
m
a
Sc
al
e-
C

h
in
es
e
ve
rs
io
n
(D
T
S-
C
)
E
m
er
ge
n
cy
H
C
W
s
re
p
or
te
d
>
D

T
S-
C
sc
or
es
th
an
n
o-
em
er
ge
n
cy
H
C
W
s;
2
1
,7
%
em
er
ge
n
cy

H
C
W
s
an
d
1
3
%
n
o-
em
er
ge
n
cy
H
C
W
s
re
p
or
te
d
D
T
S-
C
>
4

0
(s
u
sp
ec
te
d
P
T
SD
).
R
is
k
fa
ct
or
:
le
ve
l
of
ex
p
os
u
re
R
ey
n

ol
d
s
et
al
.
(2
0
0
7
)
SA
R
S
cr
os
s-
se
ct
io
n
al
st
u
d
y
1
0
5
7
qu

ar
an
ti
n
ed
su
bj
ec
ts
(H
C
W
s
n
=
2
6
9
)
Im
p
ac
t
of
E
ve
n
ts

Sc
al
e
–
R
ev
is
ed
1
4
.6
%
IE
S-
R
≥
2
0
;
qu
ar
an
ti
n
ed
H
C
W

s
ex
p
er
ie
n
ce
d
gr
ea
te
r
P
T
SS
th
an
qu
ar
an
ti
n
ed
n
o-
H
C
W
s
R

is
k
fa
ct
or
s:
qu
ar
an
ti
n
e
Su
et
al
.
(2
0
0
7
)
SA
R
S
p
ro
sp
ec
ti
ve

an
d
p
er
io
d
ic
fo
ll
ow
-u
p
st
u
d
y
1
0
2
H
C
W
s
(7
0
SA
R
S
an

d
3
2
n
o-
SA
R
S
H
C
W
s)
D
av
id
so
n
T
ra
u
m
a
Sc
al
e-
C
h
in
es
e
ve

rs
io
n
(D
T
S-
C
)
SA
R
S
u
n
it
H
C
W
s
re
p
or
te
d
h
ig
h
er
D
ep
re
ss
io

n
(3
8
.5
%
vs
.
3
.1
%
)
in
so
m
n
ia
(3
7
%
vs
.
9
.7
%
)
an
d
P
T
SS

(3
3
%
vs
.
1
8
.7
%
,
bu
t
n
ot
si
gn
ifi
ca
n
t)
.
R
is
k
fa
ct
or
s:
le

ve
l
of
ex
p
os
u
re
La
n
ce
e
et
al
.
(2
0
0
8
)
SA
R
S
cr
os
s-
se
ct
io
n

al
st
u
d
y
1
3
9
h
os
p
it
al
st
aff
(H
C
W
s
n
=
1
0
3
;
cl
er
ic
al

st
aff
n
=
1
3
;
O
th
er
n
=
2
1
)
St
ru
ct
u
re
d
C
li
n
ic
al
In
te
rv

ie
w
fo
r
D
SM
-I
V
;
C
li
n
ic
ia
n
-A
d
m
in
is
te
re
d
P
T
SD
Sc
al
e
3

0
%
li
fe
ti
m
e
p
re
va
le
n
ce
of
d
ep
re
ss
iv
e,
an
xi
et
y,
or
su
bs
ta
n
ce

u
se
d
ia
gn
os
is
.
5
%
n
ew
p
sy
ch
ia
tr
ic
d
is
or
d
er
s
af
te
r
ou

tb
re
ak
R
is
k
fa
ct
or
s:
p
re
vi
ou
s
p
sy
ch
ia
tr
ic
d
is
or
d
er
,
<
ye

ar
s
of
w
or
k
ex
p
er
ie
n
ce
(c
on
ti
nu
ed
on
ne
xt
pa
ge
)
4

T
ab
le
1
(c
on
ti
nu
ed
)
St
u
d
y
O
u
tb
re
ak
T
yp
e
Sa
m
p
le

P
T
SS
/P
T
SD
m
ea
su
re
s
M
ai
n
ge
n
er
al
fi
n
d
in
gs
M
ai
n
ri
sk
an

d
re
si
li
en
ce
fa
ct
or
s
R
es
il
ie
n
ce
fa
ct
or
s:
tr
ai
n
in
g
an
d
su
p
er
vi

so
r/
co
ll
ea
gu
es
su
p
p
or
t.
St
yr
a
et
al
.
(2
0
0
8
)
SA
R
S
cr
os
s-
se

ct
io
n
al
st
u
d
y
SA
R
S
u
n
it
s
H
C
W
s
(n
=
1
6
0
)
an
d
n
o-
SA
R

S
u
n
it
s
H
C
W
s
(n
=
8
8
)
Im
p
ac
t
of
E
ve
n
t
Sc
al
e—
R
ev
is
ed

H
C
W
s
ta
ki
n
g
ca
re
of
on
ly
on
e
SA
R
S
p
at
ie
n
t
h
ad
h
ig
h
er

P
T
SS
le
ve
ls
th
an
th
os
e
ta
ki
n
g
ca
re
of
n
on
e
or
m
or
e
th
an
tw
o

SA
R
S
p
at
ie
n
ts
R
is
k
fa
ct
or
:
le
ve
l
of
ex
p
os
u
re
W
u
et
al
.
(2

0
0
9
)
SA
R
S
cr
os
s-
se
ct
io
n
al
st
u
d
y
5
4
9
h
os
p
it
al
st
aff
(2

1
%
d
oc
to
rs
,
3
8
%
n
u
rs
es
,
2
2
%
te
ch
n
ic
ia
n
s;
2
0
%

ad
m
in
is
tr
at
iv
e
an
d
ot
h
er
s)
Im
p
ac
t
of
E
ve
n
t
Sc
al
e—
R
ev
is
ed

A
bo
u
t
1
0
%
IE
S-
R
≥
2
0
.
R
is
k
fa
ct
or
s:
le
ve
lo
f
ex
p
os
u
re

;y
ou
n
ge
r
ag
e;
qu
ar
an
ti
n
e/
is
ol
at
io
n
(q
u
ar
an
ti
n
e,
h
av
in

g
fr
ie
n
d
s
or
cl
os
e
re
la
ti
ve
s
in
fe
ct
ed
).
R
es
il
ie
n
ce
fa
ct
or
:
co

p
in
g
st
ra
te
gi
es
(a
lt
ru
is
ti
c
ac
ce
p
ta
n
ce
of
w
or
k-
re
la
te
d
ri
sk
s)
M

ak
et
al
.
(2
0
1
0
)
SA
R
S
re
tr
os
p
ec
ti
ve
co
h
or
t
st
u
d
y
9
0
SA

R
S
su
rv
iv
or
s
(3
0
%
H
C
W
s)
St
ru
ct
u
re
d
C
li
n
ic
al
In
te
rv
ie
w
fo

r
th
e
D
SM
-I
V
;
Im
p
ac
t
of
E
ve
n
ts
Sc
al
e–
R
ev
is
ed
4
7
.8
%

P
T
SD
in
th
e
af
te
rm
at
h
of
SA
R
S.
2
5
.6
%
st
il
l
su
ff
er
s
P
T
SD

3
0
-m
on
th
s
p
os
t-
SA
R
S
R
is
k
fa
ct
or
s:
be
in
g
H
C
W
s
su
rv
iv
or
s

(b
u
t
la
rg
e
p
ro
p
or
ti
on
of
th
e
H
C
W
s
w
er
e
fe
m
al
e,
an
d
th
is
co

u
ld
aff
ec
t
re
su
lt
s)
.
W
in
g
an
d
Le
u
n
g
(2
0
1
2
)
SA
R
S
ca
se
-c

on
tr
ol
st
u
d
y
2
3
3
SA
R
S
su
rv
iv
or
s
C
h
in
es
e
bi
li
n
gu
al
ve

rs
io
n
of
th
e
Se
m
i-
St
ru
ct
u
re
d
C
li
n
ic
al
In
te
rv
ie
w
(S
C
ID
-I
)

Im
p
ac
t
of
E
ve
n
t
Sc
al
e-
re
vi
se
d
5
0
%
SA
R
S
su
rv
iv
or
s
a
li
fe
ti
m

e
p
sy
ch
ia
tr
ic
d
is
or
d
er
(d
ep
re
ss
io
n
,
P
T
SD
,
so
m
at
of
or
m
p

ai
n
d
is
or
d
er
,
p
an
ic
d
is
or
d
er
)
R
is
k
fa
ct
or
:
be
in
g
H
C
W
s
su
rv

iv
or
s
Le
e
et
al
.
(2
0
1
8
)
M
E
R
S
co
h
or
t
st
u
d
y
3
5
9
H
C
W

s
(M
E
R
S
an
d
n
o-
M
E
R
S
u
n
it
)
Im
p
ac
t
of
E
ve
n
ts
Sc
al
e–
R

ev
is
ed
5
1
%
H
C
W
s
re
p
or
te
d
IE
S>
2
5
(M
E
R
S
u
n
it
s
>
n
o-

M
E
R
S
u
n
it
s)
in
th
e
fi
rs
t
m
on
th
of
M
E
R
S
ou
tb
re
ak
.
A
ft
er

on
e
m
on
th
:
qu
ar
an
ti
n
ed
M
E
R
S
u
n
it
s
H
C
W
s
sh
ow
ed
h
ig
h

er
sl
ee
p
an
d
n
u
m
bn
es
s
sc
or
es
;
M
E
R
S
u
n
it
s
H
C
W
s
sh
ow
ed

h
ig
h
er
in
tr
u
si
on
sy
m
p
to
m
s
R
is
k
fa
ct
or
s:
le
ve
l
of
ex
p
os
u

re
,
qu
ar
an
ti
n
e
Ju
n
g
et
al
.
(2
0
2
0
)
M
E
R
S
cr
os
s-
se
ct
io
n

al
st
u
d
y
1
4
7
H
C
W
s
(n
u
rs
es
of
M
E
R
S
u
n
it
s)
Im
p
ac
t
of
E

ve
n
t
Sc
al
e–
R
ev
is
ed
K
or
ea
n
ve
rs
io
n
5
7
.1
%
P
T
SD
(2
5
.1
%

fu
ll
P
T
SD
an
d
3
2
.0
%
p
ar
ti
al
P
T
SD
).
P
T
SD
w
as
as
so
ci
at
ed

w
it
h
tu
rn
ov
er
in
te
n
ti
on
R
is
k
fa
ct
or
s:
le
ve
l
of
ex
p
os
u
re
(e

m
er
ge
n
cy
H
C
W
s
>
n
o-
em
eg
en
cy
H
C
W
s)
,
p
re
vi
ou
s
p
sy
ch

ia
tr
ic
d
is
or
d
er
s
R
es
il
ie
n
ce
fa
ct
or
:
su
p
er
vi
so
r
su
p
p
or
t
K
an

g
et
al
.
(2
0
2
0
)
C
O
V
ID
-1
9
cr
os
s-
se
ct
io
n
al
st
u
d
y
9
9

4
W
u
h
an
H
C
W
s
(d
oc
to
rs
an
d
n
u
rs
es
)
Im
p
ac
t
of
E
ve
n
t

Sc
al
e-
R
ev
is
ed
R
eg
ar
d
in
g
m
en
ta
l
h
ea
lt
h
p
ro
bl
em
(i
n
cl
u
d
in

g
P
T
SS
),
3
6
.9
%
h
ad
su
b-
th
re
sh
ol
d
d
is
tu
rb
an
ce
s,
3
4
.4
%
m

il
d
d
is
tu
rb
an
ce
s,
2
2
.4
%
m
od
er
at
e
d
is
tu
rb
an
ce
s,
an
d
6
.2

%
se
ve
re
d
is
tu
rb
an
ce
.
R
is
k
fa
ct
or
s:
le
ve
l
of
ex
p
os
u
re
(t

o
p
eo
p
le
ar
ou
n
d
th
em
w
h
o
w
er
e
in
fe
ct
ed
,
in
cl
u
d
in
g
fa
m

il
y/
co
ll
eg
u
es
/f
ri
en
d
s)
.
R
es
il
ie
n
ce
fa
ct
or
s:
co
p
in
g
st
ra
te

gi
es
(b
ei
n
g
m
ot
iv
at
ed
to
le
ar
n
th
e
n
ec
es
sa
ry
sk
il
ls
to
re
sp
on
d

to
d
iv
er
se
ch
al
le
n
ge
s)
La
i
et
al
.
(2
0
2
0
)
C
O
V
ID
-1
9
cr

os
s-
se
ct
io
n
al
st
u
d
y
1
2
5
7
H
C
W
s
(d
oc
to
rs
n
=
4
9
3
,
n

u
rs
es
n
=
7
6
4
)
Im
p
ac
t
of
E
ve
n
t
Sc
al
e–
R
ev
is
ed
7
1
.5
%

re
p
or
te
d
m
il
d
to
se
ve
re
P
T
SS
(3
6
.5
%
m
il
d
,
2
4
.5
%
m
od

er
at
e,
1
0
.5
se
ve
re
).
R
is
k
fa
ct
or
s:
le
ve
l
of
ex
p
os
u
re
,
n
u

rs
es
,
fe
m
al
e,
fe
w
er
ye
ar
s
of
w
or
k
ex
p
er
ie
n
ce
.
Li
et
al
.
(2
0

2
0
)
C
O
V
ID
-1
9
ca
se
-c
on
tr
ol
st
u
d
y
2
1
4
ge
n
er
al
p
u

bl
ic
an
d
5
2
6
H
C
W
s
(2
3
4
fr
on
t-
li
n
e
n
u
rs
es
,
2
9
2
n
on
-
fr

on
t-
li
n
e
n
u
rs
es
)
V
ic
ar
io
u
s
tr
au
m
at
iz
at
io
n
qu
es
ti
on
n
ai
re

(b
as
ed
on
se
ve
ra
l
qu
es
ti
on
n
ai
re
s,
in
cl
u
d
in
g
IE
S-
R
)
V
ic
ar

io
u
s
tr
au
m
at
iz
at
io
n
w
as
si
gn
ifi
ca
n
tl
y
lo
w
er
in
fr
on
t-
li
n
e
n

u
rs
es
th
an
n
on
-f
ro
n
t-
li
n
e
on
es
an
d
ge
n
er
al
p
u
bl
ic
(n
o

d
iff
er
en
ce
be
tw
ee
n
n
on
-f
ro
n
t-
li
n
e
n
u
rs
es
an
d
ge
n
er
al
p
u

bl
ic
)
R
is
k
fa
ct
or
s:
le
ve
l
of
ex
p
os
u
re
,
m
ar
it
al
st
at
u
s.
H
C

BUS 499, Week 3 The Internal Organization Resources, Capabilitie

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