Perceived Threat of COVID-19 Contagion and Frontline Paramedics' Agonistic Behaviour: Employing a Stressor-Strain-Outcome Perspective

Abstract

Historically, infectious diseases have been the leading cause of human psychosomatic strain and death tolls. This research investigated the recent threat of COVID-19 contagion, especially its impact among frontline paramedics treating patients with COVID-19, and their perception of self-infection, which ultimately increases their agonistic behaviour. Based on the stressor-strain-outcome paradigm, a research model was proposed and investigated using survey-based data through a structured questionnaire. The results found that the perceived threat of COVID-19 contagion (emotional and cognitive threat) was positively correlated with physiological anxiety, depression, and emotional exhaustion, which led toward agonistic behaviour. Further, perceived social support was a key moderator that negatively affected the relationships between agonistic behaviour and physiological anxiety, depression, and emotional exhaustion. These findings significantly contributed to the current literature concerning COVID-19 and pandemic-related effects on human behaviour. This study also theorized the concept of human agonistic behaviour, which has key implications for future researchers.

Full text

International Journal of
Environmental Research
and Public Health
Article
Perceived Threat of COVID-19 Contagion and
Frontline Paramedics’ Agonistic Behaviour:
Employing a Stressor–Strain–Outcome Perspective
Fakhar Shahzad 1, * , Jianguo Du 1, * , Imran Khan 2, Adnan Fateh 3,
Muhammad Shahbaz 4, Adnan Abbas 5and Muhammad Umair Wattoo 2
1School of Management, Jiangsu University, Zhenjiang 212013, China
2Department of Management Sciences, The Islamia University of Bahawalpur, Punjab 63100, Pakistan;
dr.imran.khan@outlook.com (I.K.); umairwattoo26@yahoo.com (M.U.W.)
3Faculty of Business and Accountancy, University of Malaya, Kuala Lumpur 50603, Malaysia;
adnanfateh1234@gmail.com
4Lyallpur Business School, Government College University Faisalabad, Faisalabad 38000, Pakistan;
shahbaz755@yahoo.com
5
School of Economics and Management, Harbin University of Science and Technology, Harbin 150080, China;
adnan.abbas001@yahoo.com
*Correspondence: fshahzad51@yahoo.com (F.S.); djg@ujs.edu.cn (J.D.);
Tel.: +86-182-6196-7532 (F.S.); +86-136-5613-7998 (J.D.)
Received: 18 June 2020; Accepted: 10 July 2020; Published: 15 July 2020


Abstract:
Historically, infectious diseases have been the leading cause of human psychosomatic
strain and death tolls. This research investigated the recent threat of COVID-19 contagion,
especially its impact among frontline paramedics treating patients with COVID-19, and their
perception of self-infection, which ultimately increases their agonistic behaviour. Based on
the stressor–strain–outcome paradigm, a research model was proposed and investigated using
survey-based data through a structured questionnaire. The results found that the perceived threat of
COVID-19 contagion (emotional and cognitive threat) was positively correlated with physiological
anxiety, depression, and emotional exhaustion, which led toward agonistic behaviour. Further,
perceived social support was a key moderator that negatively affected the relationships between
agonistic behaviour and physiological anxiety, depression, and emotional exhaustion. These findings
significantly contributed to the current literature concerning COVID-19 and pandemic-related effects
on human behaviour. This study also theorized the concept of human agonistic behaviour, which has
key implications for future researchers.
Keywords: COVID-19; anxiety; depression; agonistic behaviour; social support
1. Introduction
Since December 2019, the global health system has been fighting with the growing number of
cases of COVID-19, a viral respiratory syndrome that first appeared in China and tentatively named
2019-nCoV1 or SARS-CoV-2 [
1
]. The World Health Organization has assessed that the rate of COVID-19
spread is expected to be very high and long-lasting [
2
]. As of 4 July 2020, the confirmed number of
patients with COVID-19 had reached 11.108 million, causing over 525,790 mortalities worldwide [
3
].
The rare history and lack of vaccines to control this novel virus may also cause a high level of
panic. During a panic, healthcare personnel (in this study, paramedics, defined as “a person who is
trained to give emergency medical treatment of sick persons or assist medical professionals”) face
not only physical challenges but also mental burdens, including psychological distress and fear [
4
,
5
].
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Int. J. Environ. Res. Public Health 2020,17, 5102 2 of 22
The unexpected rise in confirmed cases has brought huge self-infection threats and physical and mental
pressure to frontline paramedics [
6
]. Many health professionals’ worldwide have become infected and
died [
7
]. The threat of contagion is very high due to the novel nature of COVID-19 and its exponential
spread rate compared to other diseases that paramedics encounter every day. Meanwhile, the lack
of availability of appropriate drugs to treat COVID-19 patients is another potential cause of the high
threat from the current pandemic. This risk is bound to alter frontline paramedics’ behaviour and
working conditions, and it may influence the treatment of patients. Thus, a study is needed to measure
the pervasiveness of several health disorders caused by the threat of self-infection from COVID-19
among frontline paramedics treating patients with COVID-19.
Owing to the increased mortality rate associated with the virus, healthcare professionals and
the public have experienced psychological concerns such as anxiety, depression, and emotional
exhaustion [
8
]. Healthcare personnel not only tolerate too much workload but also have an extreme
risk of self-infection [
9
]. This risk and the accompanying work environments with inadequate
protection, lack of contact with family members, frustration, prejudice, and fear of getting infected
further exacerbate the noted psychological health issues [
10
]. Furthermore, prolonged fear of illness
increases individuals’ health disorders [
9
], leading to behavioural shifts. Similarly, a prior study
posited that perceived threats and the resulting anxiety, discomfort, emotional stress, adaptation
difficulties, and depression affect behavioural changes [
11
]. Concurrently, mass tragedies, especially
those involving infectious diseases, often prompt high fear that causes enormous interruptions to
individuals’ behaviour and psychological well-being [
12
]. In contrast with the current literature and
editorial reviews, this investigation expects to lure public focus to the agonistic behaviour of frontline
paramedics, which is caused by their physiological anxiety, depression, and emotional exhaustion.
Psychosocial interventions have demonstrated that stress-related ailments may affect individual
behaviours. The negative consequences of stressors are especially common in humans; perhaps
since we have a high degree of symbolic thinking, which may cause a constant strain response
to various adverse living and working environments [
13
]. Based on the assessment of perceived
threat, humans and other animals respond accordingly. Therefore, considering the theory of agonistic
behaviour [
14
], this research examined the agonistic behaviour of frontline paramedics treating patients
with COVID-19. Agonistic behaviour refers to “the complex of aggression, threat, appeasement,
and avoidance behaviours that occur during encounters between members of the same species” [
15
].
Agonistic behaviour varies among species, which is integrated with a threat, aggression, and submissive
interaction. These are also related to aggression in function and physiology; but not in the narrow
sense of aggressive behaviour [
16
]. A prior study [
17
] extended the theory of agonistic behaviour
from the biology and psychology disciplines into the context of marketing and found the impact of
perceived scarcity, which increases attractiveness and leads to buying behaviour. However, one piece of
knowledge is still unknown about human agonistic behaviour, and a paucity of theoretical development
in measuring human agonistic behaviour motivated the authors to develop an in-depth understanding
of this vital concept.
Therefore, the authors further extended the theory of agonistic behaviour from the field of animal
biological sciences to human behavioural science. The authors integrated the stressor–strain–outcome
(SSO) model with the theory of agonistic behaviour to examine the effects of the perceived threat of
COVID-19 on human agonistic behaviour. The research contributes by developing and validating the
theoretical framework using real-life scenarios—frontline paramedics treating patients with COVID-19.
This informs the current transformation of studying animal agonistic behaviour to human agonistic
behaviour. Specifically, this study explains that the higher the perceived threat level of COVID-19,
the higher the degree of physiological anxiety, depression, and emotional exhaustion leading towards
agonistic behaviour among frontline paramedics.
Another factor related to agonistic behaviour is perceived social support (PSS), which refers
to individuals’ feelings of being cared for, valued, loved, and having a sense of belonging to those
who are relied on [
18
]. Several studies have shown that, for people with anxiety and depression,

Int. J. Environ. Res. Public Health 2020,17, 5102 3 of 22
a higher sense of social support may be psychologically comforting [
17
–
20
]. Of course, social support
can have a salutary effect on health. Concurrently, the potential moderating effect of PSS on human
agonistic behaviour has received little interest from scholars. Therefore, our study also examined
the moderating role of PSS on the association between selected strain factors (physiological anxiety,
depression, and emotional exhaustion) and the agonistic behaviour of frontline paramedics. In this
study context, understanding human agonistic behaviour will help to determine specific characteristics
and potential mechanisms of human aggression and violence in a variety of contexts.
2. Theoretical Support and Conceptualisation
2.1. Theory of Agonistic Behaviour
Agonistic behaviour is also known as agonism—survivalist animal behaviour, including defence,
avoidance, and aggression. The term agonistic behaviour was first used to describe animal fighting
behaviour [
21
]. It is an adaptive behaviour resulting from conflicts within the same species members [
14
].
While there is no commonly accepted definition of human agonism, it has usually been defined
as the act of triggering psychological or physical harm to other persons or in the destruction of
property [
15
]. Moreover, it is further defined as ‘the individual’s aggressive verbal and physical
tendencies and aggressive attitudes’ [
22
]. Agonistic behaviour can serve as a tool for distinct antisocial,
constructive activities, and destructive acts. In both human and non-humans, agonistic behaviour is
significantly influenced by the general principles of operant and classical conditioning learning and
social modelling [
17
]. The biologist who favoured this concept recognised that behavioural stimuli
and underlying feelings and approaches are frequently the same; and actual behaviour is dependent
on other factors, especially distance to the stimulus [
23
]. Moreover, the term ‘agonistic’ introduces that
the differences between aggressive and agonistic behaviours have been blurred, and these two labels
are often used interchangeably in the literature. In humans, aggression is repeatedly related to living
conditions [17].
Behaviour also depends on the level of awareness among group members when stressful
events occur in a social environment because individuals are susceptible to behavioural signals [
24
].
One primary reaction during the pandemic is fear of contagion. Humans react like other animals
because they have a similar defence system against ecological threats [
25
]. Negative emotions brought
about by threats can be contagious, and fear makes threats more imminent [
26
,
27
]. Behaviour has,
in part, a genetic basis, which generally is learned in a social context. Several factors can cause positive
and negative behavioural change. Previous literature discussed the change in agonistic behaviour
of animals species rather than the human species. This study thus empirically investigated agonistic
behaviour in humans and assessed the effect of the perceived threat of COVID-19 on agonistic behaviour
by employing the SSO model. This study will make a significant contribution to the existing theory of
agonistic behaviour by elucidating how to measure human psychological cognition and behaviour.
2.2. SSO Perspective
Our framework is based on the SSO model because we examined the influence of the perceived
threat of COVID-19 (a stressor) on agonistic behaviour [
28
]. This model divulges how stressors
become prominent in individuals’ lives, indicating that the stressor source has a direct influence
on the strain, which later contributes to outcome variables. Stressors are environmental stimuli
that individuals experience and transmit stress. Strain and outcomes are an individual’s personal
emotional, and behavioural responses to stressors [
13
]. Summing up, the SSO model considers that
strain is the result of sensing stressors and the antecedent of the outcome variable. In the past,
SSO models have been used to comprehend stress in the workplace and behavioural change as an
outcome variable [
29
–
32
]. However, in the context of measuring agonistic behaviour among humans,
the implementation of the SSO model has not been sufficiently investigated.

Int. J. Environ. Res. Public Health 2020,17, 5102 4 of 22
With the rapid rise in COVID-19 cases, the severe threat to medical staffis imminent, which increases
their physiological and psychosomatic strain [
33
]. In addition, the availability of equipment and
pandemic control preparedness may have a moral effect on medical personnel [
34
]. However, the threat
of getting sick from COVID-19 persists, which also puts stress on paramedical personnel. This stress
further affects the psychosomatic state of frontline paramedics and increases their agonistic behaviour.
Recent studies have also confirmed that the perceived fear of COVID-19 contagion affects individuals’
psychological distress [12,34,35].
Since outcome factors interact with psychological responses and perceived stressors, the current
research model included three valuable and practical individual strains. The first is physiological
anxiety—“a level and nature of anxiety, including physiological worry/oversensitivity, social concerns
and concentration” [
36
]. The second is depression—“a mental illness with physiological and
psychological consequences, including sluggishness, diminished interest and pleasure, and disturbances
in sleep and appetite” [
37
]. The third is emotional exhaustion—“the extent to which employees feel
drained and overwhelmed by their work” [31].
In this study, agonistic behaviour—“adaptive acts which arise out of conflicts between two
members of the same species”—was our dependent variable [
15
]. In prior literature, it was mostly
used interchangeably with aggressive behaviour. Few scholars have discussed human agonistic
behaviour, particularly in the field of marketing and customers’ buying behaviour [
15
,
17
]. However,
there is no empirical evidence concerning the impact of the perceived threat of COVID-19 or any
other pandemic-related fears from the perspective of the SSO model. This motivated the authors to
investigate the possible consequences of human agonistic behaviour. The SSO model can be an effective
way because it emphasises the positive effect of the environmental stimulus on the internal and external
behaviour of frontline paramedics treating patients with COVID-19. Moreover, the sequential process
of the SSO model has been used to test the theoretical avowals made in this study, which includes
how perceived threat of COVID-19 affect the agonistic behaviour of frontline paramedics by creating
physiological anxiety, depression, and emotional exhaustion.
3. Research Model and Hypotheses Development
3.1. Perceived Threat of COVID-19
In this section, we will discuss how threats and risks may be perceived and responded to by
people during a pandemic and its aftermath; specifically, fear causes individuals to change their
behaviour. Intense fear produces the greatest behavioural changes when people experience physical
and psychosomatic disorders such as anxiety, depression, and emotional exhaustion [
38
–
40
]; whereas
intense fear can lead to aggressive and defensive responses [
26
]. Therefore, we adapted the previous
Brief Illness Perception Questionnaire (BIPQ) [
41
] to determine the level of perceived threat among
frontline paramedics treating patients with COVID-19. The concept of illness perception is related
to how a person perceives the illness as well as the cognitive structuring of the status of being ill.
The model recommends that situational stimuli can produce cognitive and emotional representations
of health threats or illness [
41
]. In other terms, illness perception is the cognitive and emotional
representations of patients’ viewpoints about the disease [42].
This cognitive and emotional model also includes beliefs about the treatment and control of the
situation. The emotional and cognitive interpretation and evaluation about the perception of illness
are the determinants of their behavioural reactions, which is shaped by individuals’ experiences,
knowledge levels, and mental strain [
43
]. Therefore, per prior directions [
43
], we divided and validated
the scale into two parts based on the emotional and cognitive perception of the threat of illness
from COVID-19.
First, emotional threat is a psychological disorder characterised by uncontrollable and irrational
fears, extreme hostility, or persistent anxiety. It identifies the illness consequences and concern
that affect individuals’ emotions and create anxiety and depression, making them angry, scared,

Int. J. Environ. Res. Public Health 2020,17, 5102 5 of 22
and exhausted [
41
,
44
,
45
]. However, it is not the amount of emotions but rather the interpretation of
emotional states that is essential for determining an individual’s degree of psychological disorder [
46
].
They confirmed a relationship between the level of distress intolerance, anxiety, and bulimic behaviour
in a non-clinical setting [
46
]. Second, cognitive threat refers to the identification of an illness threat
from a particular disease, understanding its expected effects, and lacking personal control over the
situation [
44
]. It may also contribute to the creation of anxiety disorders and psychological distress,
which ultimately leads to behavioural change [41,43,47].
Fear of illness is inextricably linked with depression and anxiety [
48
]. Per Chinese scholars,
a parallel epidemic of depression, anxiety, and emotional exhaustion is triggered by the COVID-19
pandemic [
4
,
49
]. In addition, recent studies posited that the pandemic had provoked widespread
psychological issues, such as fear, anxiety, and depression, among countries with a high prevalence of
viral infections [
50
,
51
]. Similarly, we assumed that perceived emotional and cognitive threat concerning
COVID-19 would create anxiety, depression, and emotional exhaustion among the paramedics treating
patients with COVID-19, which would ultimately lead to their agonistic behaviour (i.e., outcome).
Thus, we hypothesised the following:
H1a: Perceived emotional threat will be positively related to physiological anxiety.
H1b: Perceived emotional threat will be positively related to depression.
H1c: Perceived emotional threat will be positively related to emotional exhaustion.
H2a: Perceived cognitive threat will be positively related to physiological anxiety.
H2b: Perceived cognitive threat will be positively related to depression.
H2c: Perceived cognitive threat will be positively related to emotional exhaustion.
3.2. Physiological Anxiety
Anxiety disorders are often caused by stressful life events [
13
]. Anxiety is defined as “an emotion
characterized by feelings of tension, worried thoughts and physical changes like increased blood
pressure” [
52
]. Anxiety is also the cause and effect of many psychosomatic diseases and plays a
role in the development of emotional psychosis [
16
]. Prior literature described the possible role
of stress and fear of sickness in the causation of submissive behaviour owing to anxiety [
11
,
53
,
54
].
How long the novel coronavirus will persist and how it will continue to influence the psychological
well-being of healthcare staffis unknown. This psychological influence may lead to adverse behavioural
change [
55
]. Thus, we posited that physiological anxiety will increase extensively if the pandemic
persists, which ultimately will increase frontline paramedics’ agonistic behaviour. Thus, we also
proposed the following hypothesis:
H3: Physiological anxiety will be positively related to agonistic behaviour.
3.3. Depression
Depression refers to a ‘psychological state of low mood and aversion to activity that can affect a
person’s thoughts, behaviour, motivation, feelings, and sense of well-being’ [
56
]. The maladaptive
actions in behavioural theories have underlined the occurrence of depression. Cognitive behavioural
therapy assumes that the root cause of depression is negative thinking patterns, which then lead to
negative behavioural patterns [
57
]. People with depression have extremely negative views about
themselves and the world. It is believed that long-lasting emotional stress is the pathogenic factor
leading to the development of individual depression that leads to behavioural disorders [
16
,
58
].
Generally, during the early stages of a pandemic, people have little information about treatment and
mortality, which exacerbates people’s fear of infection, leading toward behavioural consequences [
59
].
Consistently, depression rates are higher during the COVID-19 pandemic as compared to before [
6
].
Like anxiety, we posited that depression would increase the agonistic behaviours of frontline paramedics:

Int. J. Environ. Res. Public Health 2020,17, 5102 6 of 22
H4: Depression will be positively related to agonistic behaviour.
3.4. Emotional Exhaustion
Emotional exhaustion is a stress-related social issue that may affect individuals’ working
behaviour [
60
]. It describes ‘feelings of being emotionally overextended’ [
61
]. Consequences of
emotional exhaustion can lead to behavioural disorders, a preference for remaining at home, and poor
work performance [
61
,
62
]. Some studies have investigated the causes or consequences of employees’
emotional exhaustion in work-related environments [
31
,
62
–
65
]. Moreover, one study [
63
] concluded
that greater levels of perceived pandemic threat could be used to anticipate increased levels of emotional
exhaustion, leading to increased agonistic behaviour. Given that the increased threat of the COVID-19
pandemic predicts increased emotional exhaustion, it is reasonable to suggest that increased emotional
exhaustion will contribute to exacerbated agonistic behaviour among frontline paramedics treating
patients with COVID-19. Like anxiety and depression, we hypothesised the following:
H5: Emotional exhaustion will be positively related to agonistic behaviour.
3.5. The Moderating Role of Perceived Social Support
Social support is defined as “social interactions or relationships that provide practical assistance
to individuals or embedding individuals into a social system that is considered to provide love, care,
or attachment to a valuable social group” [
24
]. Simply, social support refers to all kinds of support that
individuals obtain from others. Social support is divided into actually received support and perceived
support. Although the received social support includes the assistance already provided, PSS is a faith
that these assisting behaviours will occur when needed in the future [
66
]. Increased social support
is coupled with better psychological outcomes, and PSS (rather than actual social support) seems to
indicate healthier psychological behaviours during times of stress [
26
]. Moreover, PSS was identified in
the SARS outbreak and organisational behaviour literature as adversely associated with burnout [
67
].
Therefore, PSS was selected as the focus of this research.
Various aspects of sociocultural background influence the degree and speed of behavioural change.
Social norms influence employees’ behaviours, what they think about others’ actions, and what they
agree or disagree with at the workplace [
68
]. In addition, many studies have confirmed the relationship
between decreased adolescent social support and increased aggression [
69
–
73
]. Moreover, greater
levels of perceived pandemic threat predict resulted in increased levels of psychological strain, whereas
greater social support predicts a decreased effect of psychological strain on behaviour disorders [
63
].
Increased PSS also protects individuals with high levels of victimisation from increased health disorders
such as depression, anxiety, emotional exhaustion [
19
,
69
]. The moderating role of PSS using the
stress-buffering model was also a significant contributor to depressive symptoms among Chinese
nurses [
20
]. Nonetheless, few studies have explored the impact of PSS on the relationship between
COVID-19-related stress and psychological well-being [74,75].
Consequently, we posited that PSS would buffer or moderate the relationship between strain
(physiological anxiety, depression, emotional exhaustion) and outcome (agonistic behaviour).
Specifically, we hypothesised the following:
H6a:
PSS will moderate the positive association between physiological anxiety and agonistic behaviour; i.e., a rise
in PSS will decrease the relationship strength between physiological anxiety and agonistic behaviour.
H6b:
PSS will moderate the positive association between depression and agonistic behaviour; i.e., a rise in PSS
will decrease the relationship strength between depression and agonistic behaviour.
H6c:
PSS will moderate the positive association between emotional exhaustion and agonistic behaviour; i.e., a rise
in PSS will decrease the relationship strength between emotional exhaustion and agonistic behaviour.
The proposed model of this study is shown in Figure 1.

Int. J. Environ. Res. Public Health 2020,17, 5102 7 of 22
Int. J. Environ. Res. Public Health 2020, 17, x 7 of 22
.
Figure 1. Proposed research model.
4. Material and Method
4.1. Context Selection
The threat of COVID-19 initially started after the first case was reported in China. Regardless of
common health issues, developing countries are still in the initial phases of tackling this uncertain
situation. The COVID-19 pandemic was first verified to have arrived in Pakistan in February 2020
[76] and grew to 69,496 confirmed cases by 31 May 2020 [77]. Paramedics, working in isolation wards,
fever clinics, intensive care units and other related departments with an increased workload and risk
of infection. In this study, the targeted population encompassed paramedics treating patients with
COVID-19 in Pakistan who completed a survey.
4.2. Construct Operationalisation
We adapted the survey items (See Appendix A) for all constructs from prior literature and
refined them to fit the context of this research before final data collection. However, in the preliminary
analysis, an item from PSS (item number 6) was excluded owing to low factor loadings and to
authenticate the results [78]. Moreover, to confirm the content validity of the proposed survey, a team
composed of one professor and four scholars were requested to verify the wording and face validity
of the survey questionnaire. The approved questionnaire was then distributed for data collection.
4.2.1. Perceived Threat of COVID-19
In this study, the Brief Illness Perception Questionnaire (BIPQ) was adapted [41] to measure the
perceived threat of COVID-19 (0 to 10 scale) among frontline paramedics treating patients during the
current pandemic. The initial eight-item questionnaire was divided into two categories as per prior
directions [43]: perceived emotional threat and perceived cognitive threat. A sample item for the
perceived emotional threat was, “How much does your threat of illness from COVID-19 affect you
emotionally”? A sample item for the perceived cognitive threat was, “How well do you feel you
understand COVID-19”?
4.2.2. Physiological Anxiety (PA)
Physiological anxiety was measured using 11 items (7-point Likert scale) [36], which were
obtained from an earlier measure [53]. A sample item was “I cannot concentrate on a task or job
without irrelevant thoughts intruding”.
Figure 1. Proposed research model.
4. Material and Method
4.1. Context Selection
The threat of COVID-19 initially started after the first case was reported in China. Regardless of
common health issues, developing countries are still in the initial phases of tackling this uncertain
situation. The COVID-19 pandemic was first verified to have arrived in Pakistan in February 2020 [
76
]
and grew to 69,496 confirmed cases by 31 May 2020 [
77
]. Paramedics, working in isolation wards,
fever clinics, intensive care units and other related departments with an increased workload and risk
of infection. In this study, the targeted population encompassed paramedics treating patients with
COVID-19 in Pakistan who completed a survey.
4.2. Construct Operationalisation
We adapted the survey items (See Appendix A) for all constructs from prior literature and refined
them to fit the context of this research before final data collection. However, in the preliminary analysis,
an item from PSS (item number 6) was excluded owing to low factor loadings and to authenticate the
results [
78
]. Moreover, to confirm the content validity of the proposed survey, a team composed of
one professor and four scholars were requested to verify the wording and face validity of the survey
questionnaire. The approved questionnaire was then distributed for data collection.
4.2.1. Perceived Threat of COVID-19
In this study, the Brief Illness Perception Questionnaire (BIPQ) was adapted [
41
] to measure the
perceived threat of COVID-19 (0 to 10 scale) among frontline paramedics treating patients during
the current pandemic. The initial eight-item questionnaire was divided into two categories as per
prior directions [
43
]: perceived emotional threat and perceived cognitive threat. A sample item for
the perceived emotional threat was, “How much does your threat of illness from COVID-19 affect
you emotionally”? A sample item for the perceived cognitive threat was, “How well do you feel you
understand COVID-19”?
4.2.2. Physiological Anxiety (PA)
Physiological anxiety was measured using 11 items (7-point Likert scale) [
36
], which were obtained
from an earlier measure [
53
]. A sample item was “I cannot concentrate on a task or job without
irrelevant thoughts intruding”.

Int. J. Environ. Res. Public Health 2020,17, 5102 8 of 22
4.2.3. Depression (DP)
Depression was measured using 19 items (7-point Likert scale) adapted from an earlier study [
79
].
A sample item was, “How often was this happen during the past 10 days; you were bothered by things
that usually do not bother you?
4.2.4. Emotional Exhaustion (EE)
Emotional exhaustion was measured using 12 items (7-point Likert scale) adapted from an earlier
study [
31
], which were obtained from an earlier measure [
80
]. A sample item was, “It is hard for me to
relax after dealing with COVID-19 patients”.
4.2.5. Perceived Social Support (PSS)
Perceived social support was assessed using 8-items (7-point Likert scale) adapted from an earlier
study [
81
]. A sample item was, “How much do you feel that your family pays extra attention to you
during a current pandemic”?
4.2.6. Agonistic Behaviour (AB)
An aggression scale was adapted from an earlier study [
22
] as an objective gauge to assess
individuals’ agonistic behaviour. We critically analysed several aggression scales; however, we found
Regoeczi’s aggression scale to be the most relevant to our definition of agonistic behaviour. A 5-items
scale (7-point Likert) was administered to participants. A sample item was, “How often did you feel
you were too aggressive toward other people during the past 10 days”?
4.3. Data Collection, Sampling, and Analysis Procedure
Consistent with the focus of this study, data were gathered through a structured questionnaire
only from paramedical personnel treating patients with COVID-19 in Pakistan. In the Punjab province
of Pakistan, there are two separate layers of professionals that support core medical personnel in
their healthcare services, namely “paramedics” and “allied health professionals”. Paramedics are
registered with Punjab Medical Faculty (PMF), and Allied Health Professionals are registered with
the Higher Education Commission (HEC) [
82
]. In this study, we have collected the data only from
the frontline paramedics working in Punjab, Pakistan particularly dealing with COVID-19 patients.
For this, we contacted the head of several quarantine centres and hospitals treating patients with
COVID-19 around Punjab province, Pakistan. They were informed of the study purpose. All possible
questions were answered to their satisfaction, but no official data were collected to assure the privacy of
the respondents and the organisations. After getting verbal permission from the concerned authority,
we started our data collection process.
Data collection followed the computer-assisted web interview method—a data-gathering technique
in which participants complete questionnaires through an online survey link without the guidance of
the interviewer [
83
]. The expected circulation of the survey was around 1500 using snowball sampling.
A total of 372 responses were recorded between 3 March 2020 and 17 May 2020. Twenty-seven responses
were omitted from final analyses because they were deemed unreliable [
84
]. Moreover, the same
size exceeding 200 meant it was reasonable to employ structural equation modelling (SEM) [
85
].
Considering the length of the survey (66 questions), utilising SEM analyses was rational. Moreover,
we evaluated the sample adequacy on the advice of [
86
], based on Cohen’s power theory. A post-hoc
was applied for all exogenous indicators (significance level was set at 0.05, the effect size was 0.15,
and the sample size was 345) to verify the statistical intensity of the study sample using G*power 3.1.9
(Heinrich-Heine-Universität, Düsseldorf, Germany) [
87
]. The results of the post-hoc test revealed that
the statistical power was 0.9, much higher than the 0.8 thresholds [
88
]. Therefore, the final sample of
345 respondents was analysed by implementing the partial least square SEM technique in Smart-PLS

Int. J. Environ. Res. Public Health 2020,17, 5102 9 of 22
v3.2.9 (Smart-PLS GmbH, Bönningstedt, Germany). For our purposes, this was more suitable than
covariance-based SEM [89,90].
5. Results
5.1. Participants’ Demographics
Table 1outlines participants’ characteristics (e.g., sex, age, and work experience): 38.6% were
men, and 61.4% were women; 18.3% were aged
≤
29 years old, 39.4% were aged 30 to 39 years,
40.6% were aged 40 to 49 years, and 1.7% were aged
≥
50 years; and 20.9% had one to three years of
work experience, 25.2% had four to six years of work experience, 26.1% had seven to nine years of
work experience, and 27.8% had ≥10 years of work experience.
Table 1. Participants profile.
Category Frequency % Age
Sex
Men 133 38.6
Women 212 61.4
Total 345 100.0
Age
20 to 29 years 63 18.3
30 to 39 years 136 39.4
40 to 49 years 140 40.6
Over 50 years 06 1.7
Total 345 100.0
Work experience
1 to 3 years 72 20.9
4 to 6 years 87 25.2
7 to 9 years 90 26.1
Above 10 years 96 27.8
Total 345 100.0
5.2. The Empirical Results of the Measurement Model
5.2.1. Reliability and Convergent Validity
Convergence validity indicates the correlation level of several indexes in a parallel structure [
78
].
To verify the convergent validity of each item, Smart-PLS v3.2.9 software was used to conduct a
confirmatory factor analysis. Table 2shows the reliability and convergent validity of this study.
In addition, Cronbach’s alpha of all factors ranged from 0.934 to 0.974, which was higher than
the threshold value. Concerning convergent validity, this study examined the similarity between
operationalisation and theory. The composite reliability (CR) was 0.947 to 0.976, and the average
variance extracted (AVE) was 0.684 to 0.861.
Table 2. Reliability and convergent validity.
Constructs Cronbach’s Alpha rho_A CR AVE
AB 0.945 0.946 0.958 0.819
DP 0.974 0.975 0.976 0.684
EE 0.961 0.962 0.966 0.703
PA 0.967 0.968 0.970 0.749
PCT 0.946 0.947 0.961 0.861
PET 0.936 0.939 0.955 0.840
PSS 0.934 0.935 0.947 0.718
AB =agonistic behaviour; DP =depression; EE =emotional exhaustion; PA =physiological anxiety; PCT =perceived
cognitive threat; PET =perceived emotional threat; PSS =perceived social support. CR =composite reliability;
AVE =average variance extracted.

Int. J. Environ. Res. Public Health 2020,17, 5102 10 of 22
The suggested values for Cronbach’s alpha and CR should be greater than 0.7, and AVE should be
greater than 0.5; thus, the instrument was efficient and reliable [78,91] and the data could be used for
further structural analysis.
5.2.2. Discriminant Validity
To distinguish the extent of empirical variance among the constructs, discriminant validity
evaluation has become a widely accepted assumption to analyse the relationship between potential
factors [
89
]. In this study, we used three methods to evaluate discriminant validity. First, by associating
the correlation of the factors with the square root of the AVE. Second, the survey items were checked
through the cross-loading criterion to recognise the relevance. Third, discriminant validity was
measured by the application of Heterotrait-Monotrait Ration (HTMT) [89,92,93].
As described in Table 3, the correlation between constructs and the square root of AVE was linked
to quantify the discriminant validity of the instrument. The diagonal values in Table 3suggest that the
square root of AVE is higher than the correlation coefficients between all variables, a good indication of
discriminant validity [93].
Table 3. Fornell-Larcker criterion.
Constructs AB DP EE PA PCT PET PSS
AB 0.905
DP 0.531 0.827
EE 0.539 0.639 0.838
PA 0.513 0.611 0.560 0.866
PCT 0.333 0.364 0.384 0.382 0.928
PET 0.364 0.368 0.393 0.404 0.705 0.917
PSS −0.254 −0.178 −0.275 −0.104 0.014 −0.032 0.847
Note: Pearson correlations are shown below the diagonals. p<0.05. AB =agonistic behaviour; DP =depression;
EE =emotional exhaustion; PA =physiological anxiety; PCT =perceived cognitive threat; PET =perceived emotional
threat; PSS =perceived social support.
Prior studies suggested cross-loadings criteria to assess discriminant validity [
91
,
94
]. Accordingly,
the loading of each item should be higher than its subsequent construct, and the item loadings are also
regarded as a threshold. The calculation results of item loadings and cross-loadings (see Table 4) show
that the loadings of each item are higher than the cross-loadings of other subsequent construct items.
This shows that it has sufficient discriminant validity by satisfying the cross-loading criteria.
Finally, the HTMT ratio criterion was established to illustrate the insensitivity of Fornell and
Larcker’s criterion and cross-loading criterion. The ratio of HTMT was close to 1, indicating the lack of
discriminant validity [
91
]. HTMT is an estimate of factor correlation (or instead, the upper bound).
To make a clear distinction between the two factors, HTMT should be less than 1 [
92
,
95
]. Therefore,
we employed the HTMT ratio; the value in Table 5shows that the highest value is 0.75, which is lower
than the above threshold, indicating sufficient discriminant validity.
5.3. The Empirical Results of the Structural Model
After examining reliability and validity, we measured the causal relationship between the factors
with Smart-PLS v3.2.9 software [
89
,
95
]. Figure 2shows the value of the path coefficient. The bootstrap
technique was used to measure the significance of the structural model (2000 iterations of resampling).
The expressive power of the research model is represented by the illustrative variation of its results
(i.e., R
2
). The R
2
(R-Square) value of AB was 0.399, indicating that these selected variables represented
39.9% of the variation. Moreover, the R
2
of physiological anxiety was 0.182, indicating that the mutation
rate owing to perceived emotional threat (PET) and perceived cognitive threat (PCT) was 18.2%.
In addition, the R
2
of depression was 0.157 and the R
2
of emotional exhaustion was 0.177, indicating
the active participation of perceived threat.

Int. J. Environ. Res. Public Health 2020,17, 5102 11 of 22
Table 4. Cross-loadings criterion.
Items AB DP EE PA PCT PET PSS
AB1 0.899 0.480 0.458 0.464 0.293 0.312 −0.215
AB2 0.921 0.491 0.561 0.473 0.329 0.331 −0.245
AB3 0.926 0.476 0.532 0.474 0.297 0.321 −0.233
AB4 0.914 0.476 0.453 0.459 0.307 0.355 −0.230
AB5 0.863 0.480 0.424 0.450 0.276 0.330 −0.222
DP1 0.409 0.775 0.453 0.544 0.260 0.268 −0.090
DP2 0.444 0.857 0.508 0.556 0.268 0.293 −0.110
DP3 0.435 0.843 0.535 0.514 0.266 0.252 −0.149
DP4 0.464 0.862 0.503 0.543 0.326 0.328 −0.114
DP5 0.486 0.843 0.551 0.552 0.338 0.326 −0.180
DP6 0.454 0.858 0.506 0.544 0.337 0.350 −0.143
DP7 0.438 0.825 0.506 0.527 0.261 0.275 −0.147
DP8 0.460 0.816 0.498 0.523 0.322 0.316 −0.140
DP9 0.473 0.845 0.557 0.504 0.352 0.360 −0.184
DP10 0.482 0.776 0.540 0.459 0.317 0.305 −0.211
DP11 0.469 0.804 0.579 0.467 0.294 0.328 −0.137
DP12 0.416 0.837 0.559 0.478 0.332 0.315 −0.137
DP13 0.410 0.855 0.536 0.511 0.290 0.256 −0.115
DP14 0.369 0.786 0.499 0.426 0.239 0.233 −0.071
DP15 0.434 0.814 0.548 0.465 0.263 0.287 −0.172
DP16 0.410 0.846 0.549 0.483 0.336 0.334 −0.178
DP17 0.415 0.857 0.526 0.510 0.293 0.315 −0.170
DP18 0.436 0.818 0.562 0.476 0.335 0.342 −0.165
DP19 0.408 0.790 0.519 0.504 0.259 0.256 −0.154
EE1 0.432 0.615 0.786 0.469 0.281 0.310 −0.269
EE2 0.447 0.572 0.791 0.486 0.258 0.305 −0.287
EE3 0.422 0.555 0.842 0.468 0.306 0.309 −0.250
EE4 0.450 0.521 0.861 0.484 0.307 0.306 −0.254
EE5 0.429 0.557 0.854 0.477 0.354 0.319 −0.213
EE6 0.498 0.583 0.870 0.474 0.356 0.362 −0.275
EE7 0.451 0.566 0.882 0.464 0.348 0.353 −0.243
EE8 0.420 0.498 0.831 0.405 0.334 0.334 −0.182
EE9 0.471 0.558 0.902 0.452 0.338 0.362 −0.273
EE10 0.461 0.511 0.833 0.500 0.346 0.385 −0.218
EE11 0.484 0.456 0.816 0.508 0.310 0.307 −0.148
EE12 0.450 0.444 0.785 0.442 0.309 0.286 −0.158
PA1 0.425 0.510 0.507 0.836 0.362 0.348 −0.126
PA2 0.413 0.537 0.500 0.847 0.343 0.332 −0.082
PA3 0.423 0.497 0.448 0.862 0.322 0.366 −0.079
PA4 0.487 0.571 0.493 0.881 0.347 0.369 −0.111
PA5 0.497 0.544 0.484 0.886 0.367 0.379 −0.122
PA6 0.420 0.503 0.497 0.880 0.356 0.387 −0.064
PA7 0.443 0.575 0.485 0.887 0.362 0.361 −0.030
PA8 0.456 0.510 0.483 0.885 0.326 0.350 −0.048
PA9 0.436 0.534 0.496 0.842 0.296 0.336 −0.111
PA10 0.456 0.518 0.479 0.866 0.278 0.318 −0.070
PA11 0.418 0.513 0.458 0.850 0.271 0.290 −0.147
PCT1 0.320 0.361 0.334 0.359 0.935 0.664 0.025
PCT2 0.306 0.314 0.355 0.327 0.928 0.690 −0.005
PCT3 0.322 0.320 0.368 0.357 0.923 0.601 −0.013
PCT4 0.287 0.356 0.366 0.374 0.927 0.663 0.043
PET1 0.337 0.324 0.356 0.350 0.632 0.931 −0.043
PET2 0.365 0.361 0.361 0.373 0.644 0.939 −0.062
PET3 0.313 0.348 0.388 0.391 0.631 0.932 −0.057
PET4 0.320 0.314 0.333 0.365 0.681 0.861 0.051
PSS1 −0.206 −0.130 −0.245 −0.097 −0.003 −0.026 0.909
PSS2 −0.222 −0.109 −0.237 −0.086 −0.032 −0.083 0.755
PSS3 −0.207 −0.191 −0.255 −0.090 0.015 −0.009 0.860
PSS4 −0.211 −0.176 −0.244 −0.082 0.006 −0.041 0.853
PSS5 −0.194 −0.128 −0.197 −0.081 0.039 −0.019 0.839
PSS7 −0.219 −0.157 −0.232 −0.103 0.064 0.005 0.856
PSS8 −0.237 −0.161 −0.220 −0.073 −0.003 −0.016 0.852
AB =agonistic behaviour; DP =depression; EE =emotional exhaustion; PA =physiological anxiety; PCT =perceived
cognitive threat; PET =perceived emotional threat; PSS =perceived social support.

Int. J. Environ. Res. Public Health 2020,17, 5102 12 of 22
Table 5. HTMT ratio criterion.
Constructs AB DP EE PA PCT PET PSS
AB
DP 0.552
EE 0.563 0.661
PA 0.536 0.628 0.581
PCT 0.351 0.376 0.401 0.398
PET 0.388 0.382 0.413 0.423 0.751
PSS 0.269 0.184 0.290 0.109 0.041 0.065
AB =agonistic behaviour; DP =depression; EE =emotional exhaustion; PA =physiological anxiety; PCT =perceived
cognitive threat; PET =perceived emotional threat; PSS =perceived social support.
Int. J. Environ. Res. Public Health 2020, 17, x 12 of 22
Table 5. HTMT ratio criterion.
Constructs AB DP EE PA PCT PET PSS
AB
DP 0.552
EE 0.563 0.661
PA 0.536 0.628 0.581
PCT 0.351 0.376 0.401 0.398
PET 0.388 0.382 0.413 0.423 0.751
PSS 0.269 0.184 0.290 0.109 0.041 0.065
AB = agonistic behaviour; DP = depression; EE = emotional exhaustion; PA = physiological anxiety;
PCT=perceived cognitive threat; PET = perceived emotional threat; PSS = perceived social support.
5.3. The Empirical Results of the Structural Model
After examining reliability and validity, we measured the causal relationship between the
factors with Smart-PLS v3.2.9 software [89,95]. Figure 2 shows the value of the path coefficient. The
bootstrap technique was used to measure the significance of the structural model (2000 iterations of
resampling). The expressive power of the research model is represented by the illustrative variation
of its results (i.e., R
2
). The R
2
(R-Square) value of AB was 0.399, indicating that these selected variables
represented 39.9% of the variation. Moreover, the R
2
of physiological anxiety was 0.182, indicating
that the mutation rate owing to perceived emotional threat (PET) and perceived cognitive threat
(PCT) was 18.2%. In addition, the R
2
of depression was 0.157 and the R
2
of emotional exhaustion was
0.177, indicating the active participation of perceived threat.
Figure 2. SEM results for hypotheses testing.
The SEM results in Figure 2 show that all exogenous factors are positively associated with
endogenous factors. The p-value confirms the level of significance of the relationship between the
proposed relations per the criterion [96,97]. Meanwhile, the value of Standardized Root Mean Square
Residual (SRMR) is 0.042, and the value of Normed Fit Index (NFI) is 0.891, showing the good fitness
of the model. In Figure 2, the SEM analysis results verify the path analysis coefficient between PET
and physiological anxiety is (β = 0.267, p < 0.001). PET had a significant positive effect on physiological
anxiety, and the beta correlation coefficient between PET and depression was significant (β = 0.221, p
< 0.001). The findings further indicated that PET and emotional exhaustion were significantly
positively correlated (β = 0.243, p < 0.001). Based on these statistical findings, H1a, H1b, and H1c were
supported.
Figure 2. SEM results for hypotheses testing.
The SEM results in Figure 2show that all exogenous factors are positively associated with
endogenous factors. The p-value confirms the level of significance of the relationship between the
proposed relations per the criterion [
96
,
97
]. Meanwhile, the value of Standardized Root Mean Square
Residual (SRMR) is 0.042, and the value of Normed Fit Index (NFI) is 0.891, showing the good fitness
of the model. In Figure 2, the SEM analysis results verify the path analysis coefficient between PET and
physiological anxiety is (
β
=0.267, p<0.001). PET had a significant positive effect on physiological
anxiety, and the beta correlation coefficient between PET and depression was significant (
β
=0.221,
p<0.001). The findings further indicated that PET and emotional exhaustion were significantly
positively correlated (
β
=0.243, p<0.001). Based on these statistical findings, H1a, H1b, and H1c
were supported.
The beta coefficient of PCT was significant (
β
=0.194, p<0.01), implying that it positively
impacted physiological anxiety; therefore, H2a was supported. PCT was positively correlated with
depression and emotional exhaustion. PCT and depression were also significantly positively correlated
(
β
=0.209, p<0.01), as were PCT and emotional exhaustion (
β
=0.212, p<0.001). Therefore, H2b and
H2c were supported.
Physiological anxiety also had a considerable effect on AB (Figure 2;
β
=0.234, p<0.001).
The coefficient values of depression and AB (
β
=0.223, p<0.001) and emotional exhaustion and
AB (
β
=0.232, p<0.001) indicated that the selected strain factors (physiological anxiety, depression,
and emotional exhaustion) had a substantial positive effect on the AB. Therefore, H3, H4, and H5 are
were supported.

Int. J. Environ. Res. Public Health 2020,17, 5102 13 of 22
5.4. The Moderating Role of Perceived Social Support
Figure 2shows the interaction value of the beta coefficient of PSS on the association between
physiological anxiety and AB (
β
=
−
0.242, p<0.001), the coefficient value of PSS on the association
between depression and AB is (
β
=
−
0.238, p<0.001), and the coefficient value of PSS on the
relationship between emotional exhaustion and AB (
β
=
−
0.221, p<0.001). PSS significantly and
negatively influenced the relationships between physiological anxiety, depression, and emotional
exhaustion with AB (Figure 3). Consequently, H6a, H6b, and H6c were supported. Figure 3also
illustrates the moderating effect of PSS on the relationship between physiological anxiety, depression,
and emotional exhaustion with AB. In sum, per the present analyses, the proposed theoretical model
was acceptable.
Int. J. Environ. Res. Public Health 2020, 17, x 13 of 22
positively correlated (β = 0.243, p < 0.001). Based on these statistical findings, H1a, H1b, and H1c were
supported.
The beta coefficient of PCT was significant (β = 0.194, p < 0.01), implying that it positively
impacted physiological anxiety; therefore, H2a was supported. PCT was positively correlated with
depression and emotional exhaustion. PCT and depression were also significantly positively
correlated (β = 0.209, p < 0.01), as were PCT and emotional exhaustion (β = 0.212, p < 0.001). Therefore,
H2b and H2c were supported.
Physiological anxiety also had a considerable effect on AB (Figure 2; β = 0.234, p < 0.001). The
coefficient values of depression and AB (β = 0.223, p < 0.001) and emotional exhaustion and AB (β =
0.232, p < 0.001) indicated that the selected strain factors (physiological anxiety, depression, and
emotional exhaustion) had a substantial positive effect on the AB. Therefore, H3, H4, and H5 are were
supported.
5.4. The Moderating Role of Perceived Social Support
Figure 2 shows the interaction value of the beta coefficient of PSS on the association between
physiological anxiety and AB (β = −0.242, p < 0.001), the coefficient value of PSS on the association
between depression and AB is (β = −0.238, p < 0.001), and the coefficient value of PSS on the
relationship between emotional exhaustion and AB (β = −0.221, p < 0.001). PSS significantly and
negatively influenced the relationships between physiological anxiety, depression, and emotional
exhaustion with AB (Figure 3). Consequently, H6a, H6b, and H6c were supported. Figure 3 also
illustrates the moderating effect of PSS on the relationship between physiological anxiety, depression,
and emotional exhaustion with AB. In sum, per the present analyses, the proposed theoretical model
was acceptable.
Figure 3. Moderating effect of PSS on the relationship of PA, DP, and EE with AB. DP = depression;
EE = emotional exhaustion; PA = physiological anxiety; PSS = perceived social support.
Figure 3.
Moderating effect of PSS on the relationship of PA, DP, and EE with AB. DP =depression;
EE =emotional exhaustion; PA =physiological anxiety; PSS =perceived social support.
5.5. Common Method Bias and Multicollinearity
The common method bias (CMB) possibly exposes the efficacy of this study. The survey notes
informed participants that there were no right or wrong answers and that their replies would remain
anonymous and confidential. Moreover, Harman’s single factor test is usually used to test for the
existence of CMB [
98
,
99
]. We used SPSS v26 (IBM SPSS Inc., Chicago, IL, USA) software to perform
Harman’s single factor test.
The first factor accounted for 40.9% of the variation. In social science literature, a value below 50%
is the threshold of the CMB [
98
,
100
,
101
]. Concurrently, the inner variance inflation factor (VIF) was
also used to evaluate the CMB problem. According to Kock (2015), inner-VIF should be less than 3.3.
We discovered that the value varied between 1.09 to 2.01; thus, CMB was not a problem in this study.
The values of outer-VIF were used for multicollinearity assessment of the survey items.
The literature shows that if the VIF value of a study is lower than 10, multicollinearity may not

Int. J. Environ. Res. Public Health 2020,17, 5102 14 of 22
be a problem [
102
–
104
]. The highest value of VIF was 5.93; thus, there was no severe multicollinearity
problem. In sum, the proposed model did not have CMB or multicollinearity problems, indicating that
the structural model measured significant differences between the constructs.
6. Discussion
The global understanding of disease transmission and management has improved during the
several pandemics in history. However, COVID-19 has limited global health authorities’ abilities.
As previous studies disclosed, working directly with patients will increase individuals’ fear of getting
sick and uncertainty about pandemic contagion [
63
,
105
], which we called perceived threat of COVID-19
in this study. Therefore, we investigated the impact of perceived COVID-19 threat in forecasting greater
levels of physiological anxiety, depression, and emotional exhaustion among frontline paramedics,
which may boost their agonistic behaviour. Another objective was to examine the moderating influence
of PSS in reducing the adverse consequences of physiological anxiety, depression, and emotional
exhaustion on agonistic behaviour owing to the perceived threat of COVID-19. The BIPQ [
41
] was
used to measure the perceived threat of COVID-19, which was divided into two constructs: perceived
emotional threat and perceived cognitive threat. SEM was applied to the data to test the research
model under the podium of the SSO framework.
The results revealed that frontline paramedics in the isolation wards did not think that they
were exempt from the peril, which was associated with increased psychological distress. Moreover,
paramedics worried about the inadequacy of protective measures and vigilance taken by the health
department. Paramedics’ perception of risk contributed to their psychological morbidity and irregular
behaviour. Based on the empirical results, we postulated that an increased perceived threat of
COVID-19 would increase the level of paramedics’ physiological anxiety and depression, which would
ultimately increase their agonistic behaviour. A causal link between the perceived threat of COVID-19
and psychological distress was found. After working in isolation for a considerable period, paramedics
reported emotional exhaustion. Treating patients with COVID-19 had become routine, and they were
inured to being around death almost every day. However, they also experienced substantial stress
owing to the fear of getting ill during the pandemic. The cognitive and emotional threat from COVID-19
was positively associated with increased emotional exhaustion at work, which was associated with
paramedics’ behavioural change.
Moreover, the results showed that PSS reduced the effect of anxiety, depression, and emotional
exhaustion on agonistic behaviour. PSS is helpful as friends or family members provide social support
and express empathy. With the increase in the number of cases of COVID-19 infection around the
globe, frontline paramedics are required to wear protective masks, protective clothing, and treat
many patients with COVID-19, which may cause added stress. However, PSS can help reduce this
stress by reducing the perception of the threat of stressful events and the physiological response
and inappropriate behaviour that can result from stress. These results are also supported by prior
studies [
75
,
106
]. Positive social feedback should thus be provided to frontline paramedics in times of
uncertainty to offset potential agonistic behaviour.
7. Implications, Limitations, and Research Directions
7.1. Theoretical Implications
First, this research offers a more account of the theory of agonistic behaviour from the field of
animal biological sciences to human behavioural science. The authors integrated the SSO model
with the theory of agonistic behaviour to examine the effects of the perceived threat of COVID-19
on human agonistic behaviour. This empirical investigation elucidated human behaviour research.
Second, by using the SSO model, this study tested several theoretical-based relationships between the
perceived threat of COVID-19 and human agonistic behaviour. Most of the recent studies concerning
COVID-19 discussed the consequences and adverse effect on patients’ health, daily life, economy,

Int. J. Environ. Res. Public Health 2020,17, 5102 15 of 22
and education [
4
,
55
,
107
–
110
]; however, this study mainly concentrated on the perceived threat of
COVID-19 among frontline paramedics, and how it influenced their psychological strain and increased
their agonistic behaviour. Therefore, the authors hope that this model can be further extended and
used as an ideal platform for future work in a similar context. Third, this study further divided the
BIPQ into two major parts—emotional and cognitive threats—and empirically tested it during the
current pandemic situation. This significantly contributes to validating the existing scales and can be
used in future research.
7.2. Practical Implications
This study also provides some useful insights for practice. First, the findings significantly
highlighted the risk of infection that frontline paramedics face, which may cause several mental
health problems such as anxiety and depression. Health organisations should implement full security
measures to protect this at-risk population to mitigate the threat of COVID-19. Second, the results
emphasised the need for healthcare managers to understand the magnitude and sources of psychosocial
stress faced by frontline paramedics. Providing adequate protection and facilities, communicating
effectively, creating transparent guidelines, and implementing appropriate feedback mechanisms
for healthcare personnel are essential to reduce the strain in the current pandemic situation. Third,
this study highlights the significant role of PSS in reducing the effect of psychological strain on agonistic
behaviour. Concerning stress management, it is also essential to strengthen social support in the
workplace. For frontline paramedics with severe psychological strain, it is necessary to identify
high-risk groups early, and provide counselling, social support, and stress management to mitigate
negative behavioural change.
7.3. Limitations and Research Directions
Some limitations need to be addressed while discussing the outcomes of the current study. First,
a cross-sectional design was employed, and the agonistic behaviour of paramedics was measured
during the current pandemic. Future scholars should employ a multimethod or longitudinal design
by comparing the results obtained during and after the COVID-19 pandemic. Second, this study
did not examine sex and age differences. The level of threat may not be the same between female
and male paramedics. Similarly, those in different age groups will respond to strain differently and
may display agonistic behaviour in diverse ways. Therefore, multigroup analyses should examine
any possible sex or age differences. Third, the strain factors discussed in this study are not limited
to these particular factors; future researchers could extend the model using several other factors
such as scepticism, sadism, and poor sleep quality, which may impact human agonistic behaviour.
Organisational and government support can also be used as a moderating factor in addition to PSS.
Finally, future researcher should continuously validate the scale used in the current study.
8. Conclusions
Our study concludes that the effect of perceived COVID-19 threat on predicting greater levels of
physiological anxiety, depression, and emotional exhaustion among frontline healthcare paramedics
may contribute to their agonistic behaviour. Moreover, we have concluded the moderating role of
PSS in decreasing the adverse effect of physiological anxiety, depression, and emotional exhaustion
on agonistic behaviour due to the perceived threat of COVID-19. Our study provides understanding
about human agonistic behaviour will help to identify precise characteristics and probable mechanisms
of human aggression and violence in several contexts, which will contribute to the implementation of
conflict management practices in the workplace.
Author Contributions:
Conceptualization, F.S. and J.D.; methodology, I.K. and A.F.; software, F.S. and I.K.;
formal analysis, F.S. and A.A; data curation, I.K., M.S., and M.U.W.; writing—original draft preparation, F.S.;
writing—review and editing, A.F., M.S, A.A., and M.U.W.; supervision, J.D.; funding acquisition, J.D. All authors
have read and agreed to the published version of the manuscript.

Int. J. Environ. Res. Public Health 2020,17, 5102 16 of 22
Funding:
This work was supported by the Special Funds of the National Social Science Fund of China [18VSJ038]
and supported in part by the National Science Foundation of China under grant 71974081, 71704066, and 71971100.
Acknowledgments:
We acknowledge the anonymous reviewers and editor for their feedback and all paramedics
who participated in this study.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A : Survey Items
Perceived Emotional Threat
“How much does COVID-19 affect your life?”
“How much do you feel symptoms COVID-19 contagion?”
“How concerned are you about COVID-19 contagion?”
“How much does your threat of illness from COVID-19 affect you emotionally? (e.g., does it make you
angry, scared, upset or depressed)”
Perceived Cognitive Threat
“How long do you think COVID-19 will continue?”
“How much control do you feel over COVID-19 contagion?”
“How much do you think that current treatment is helpful from the recovery of COVID-19 contagion?”
“How well do you feel you understand COVID-19?”
Physiological Anxiety
“I picture some future misfortune.”
“I cannot get some thoughts out of my mind.”
“I abide on mistakes that I have made.”
“I think about possible misfortunes to my loved ones.”
“I cannot concentrate on a task or job without irrelevant thoughts intruding.”
“I keep trying to avoid uncomfortable thoughts.”
“I cannot get some pictures or images out of my mind.”
“I imagine myself appearing foolish with a person whose opinion of me is important.”
“I am concerned that others might not think well of me.”
“I have to be careful not to let my real feelings show.”
“I have an uneasy feeling.”
Depression
How often was this happen during the past 10 days:
“you were bothered by things that usually do not bother you?”
“you did not feel like eating, and your passion was poor?”
“you felt that you could not shake the blues, even with help from family and friends?”
“you felt that you were just as good as other people?”
“you had trouble keeping your mind on what you were doing?”
“you felt depressed?”
“you felt that you were too tired to do things?”
“you felt hopeful about the future?”
“you thought your life had been a failure?”
“you felt fearful?”
“you were happy?”
“you talked less than usual?”
“you felt lonely?”
“people were unfriendly to you?”
“you enjoyed life?”

Int. J. Environ. Res. Public Health 2020,17, 5102 17 of 22
“you felt sad?”
“you felt that people disliked you?”
“it was hard to get started doing things?”
“you felt life was not worth living?”
Emotional Exhaustion
“It is hard for me to relax after dealing with COVID-19 patients.”
“If others speak to me, I will sometimes give an errant reply.”
“I mostly feel annoyed while dealing with COVID-19 patients.”
“I sometimes act aggressively, although I do not want to do so. “
“I feel irritable after dealing with COVID-19 patients for hours.”
“I feel emotionally drained sometimes.”
“I feel used up at the end of my work.”
“I feel fatigued when I get up in the morning and being confronted with news of COVID-19 patients.”
“I feel burned out from dealing with COVID-19 patients. “
“I feel frustrated after work.”
“I fell, I am working with COVID-19 patients for too long.”
“Dealing with COVID-19 patients puts too much stress on me.”
Perceived Social Support
“How much do you feel that adults care about you?”
“How much do you feel that your employer care about you?”
“How much do you feel that your parents care about you?”
“How much do you feel that your friends care about you?”
“How much do you feel that people in your family understand you?”
“How much do you feel that you want to leave home?” (deleted)
“How much do you feel that you and your family have fun together during a current pandemic?”
“How much do you feel that your family pays extra attention to you during a current pandemic?”
Agonistic Behaviour
“How often did you feel, you were too aggressive toward other people during the past 10 days?”
“How often did you feel, you influence other people too much to get what you want during the past
10 days?”
“How often did you feel, not at all aggressive-aggressive during the past 10 days?”
“How often did you feel, you like people to be afraid of you during the past 10 days?”
“How often did you feel, you try to get into a position of authority during the past 10 days?”
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