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Research article Special Issues

Behavioral dysregulation at work: A moderated mediation analysis of sleep impairment, work-related stress, and substance use

  • Background 

    Sleep impairment and work-related stress are common issues that influence employee well-being and organizational outcomes. Impaired sleep depletes cognitive and emotional resources, increasing stress and the likelihood of counterproductive work behaviors directed toward the organization (CWB-O). This cross-sectional study, guided by the conservation of resources (COR) theory, explores the relationships between impaired sleep, work-related stress, and CWB-O, considering substance use as a dysfunctional coping strategy.

    Methods 

    A sample of 302 Italian employees completed an online survey. Sleep impairment was assessed using the Insomnia Severity Index, work-related stress was assessed with the Perceived Stress Scale, CWB-O was assessed with the Counterproductive Work Behavior Checklist, and substance use as a coping strategy was assessed using the Brief COPE. A moderated mediation model was tested to examine the indirect effects of sleep impairment on CWB-O via work-related stress, with substance use moderating both the sleep–stress and stress–CWB-O relationships.

    Results 

    The results supported the hypothesis that the relationship between sleep impairment and CWB-O is mediated by work-related stress. Sleep difficulties significantly increased work-related stress, which in turn led to higher levels of CWB-O. Substance use did not moderate the relationship between sleep and work-related stress. It did, however, significantly moderate the relationship between work-related stress and CWB-O, with higher levels of substance use amplifying the impact of stress on behavioral dysregulation.

    Conclusion 

    This study contributes to our understanding of how impaired sleep, work-related stress, and substance use interact to influence deviant behaviors at work. The findings align with COR theory, highlighting the role of resource depletion and dysfunctional coping in workplace behavior, and suggest that organizational interventions should also consider programs aimed at improving sleep quality and addressing substance use to reduce the likelihood of deviant behaviors at work.

    Citation: Francesco Marcatto, Donatella Ferrante, Mateusz Paliga, Edanur Kanbur, Nicola Magnavita. Behavioral dysregulation at work: A moderated mediation analysis of sleep impairment, work-related stress, and substance use[J]. AIMS Public Health, 2025, 12(2): 290-309. doi: 10.3934/publichealth.2025018

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  • Background 

    Sleep impairment and work-related stress are common issues that influence employee well-being and organizational outcomes. Impaired sleep depletes cognitive and emotional resources, increasing stress and the likelihood of counterproductive work behaviors directed toward the organization (CWB-O). This cross-sectional study, guided by the conservation of resources (COR) theory, explores the relationships between impaired sleep, work-related stress, and CWB-O, considering substance use as a dysfunctional coping strategy.

    Methods 

    A sample of 302 Italian employees completed an online survey. Sleep impairment was assessed using the Insomnia Severity Index, work-related stress was assessed with the Perceived Stress Scale, CWB-O was assessed with the Counterproductive Work Behavior Checklist, and substance use as a coping strategy was assessed using the Brief COPE. A moderated mediation model was tested to examine the indirect effects of sleep impairment on CWB-O via work-related stress, with substance use moderating both the sleep–stress and stress–CWB-O relationships.

    Results 

    The results supported the hypothesis that the relationship between sleep impairment and CWB-O is mediated by work-related stress. Sleep difficulties significantly increased work-related stress, which in turn led to higher levels of CWB-O. Substance use did not moderate the relationship between sleep and work-related stress. It did, however, significantly moderate the relationship between work-related stress and CWB-O, with higher levels of substance use amplifying the impact of stress on behavioral dysregulation.

    Conclusion 

    This study contributes to our understanding of how impaired sleep, work-related stress, and substance use interact to influence deviant behaviors at work. The findings align with COR theory, highlighting the role of resource depletion and dysfunctional coping in workplace behavior, and suggest that organizational interventions should also consider programs aimed at improving sleep quality and addressing substance use to reduce the likelihood of deviant behaviors at work.



    In December 2019, the world is facing the emergence of a new pandemic, which is called coronavirus disease 2019 (COVID-19). Then, COVID-19 spreads to world widely over the first two months in 2020. There were 492,510 confirmed cases of COVID-19 infection and 22,185 dead cases in world [1], [2]. Therefore, it poses a continuing threat to human health because of its high transmission efficiency and serious infection consequences as well, it transmits by direct contact. Many researchers have tried to study and understand the dynamical behavior of COVID-19 through the transmission dynamics and calculate the basic reproduction number of COVID-19. It has become a key quantity to determine the spread of epidemics and control it. For example, in [3], Li et al. conducted a study of the first 425 confirmed cases in Wuhan, China, showing that the reproduction number of COVID-19 was 2.2, and revealed that person to person transmission occurred between close contacts. Other research [4] shows that the reproduction number of COVID-19 becomes 2.90, which is being increasing. In [5], Riou et al. studied pattern of early human to human transmission of COVID-19 in Wuhan, China. In [6], Hellewell et al. investigated the feasibility of controlling 2019-nCoV outbreaks by isolation of cases and contacts. Chen et al.[7], suggested mathematical model for simulation the phase-based transmissibility of novel coronavirus. Bentout et al. [8] developed an susceptible exposed infectious recovered model to estimation and prediction for COVID-19 in Algeria. Belgaid et al.[9] suggested and analysis of a model for Coronavirus spread. Owolabi et al. [10] proposed and analyzed a nonlinear epidemiological model for SARS CoV-2 virus with quarantine class. Flaxman et al. [11] suggested and estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Kennedy et al. [12] suggested a mathematical model involving the effects of intervention strategies on COVID-19 transmission dynamics. Feng et al. [13] studied a COVID-19 model with the effects of media and quarantine in UK. In this present study, we will show effects of the quarantine strategy and media reports on the spread of COVID-19.

    We propose a mathematical model for COVID-19 transmission dynamics with the quarantine strategy and media effects. We start the model formulation by denoting the total size of the population by N which is classified further into five classes, the susceptible S(t), the exposed E(t), the infected I(t), the hospital quarantined Q(t) and the recovery R(t) at any time t, So, N=S+E+I+Q+R. The exposed class means low-level virus carrier, which is considered to be non infectious. The quarantined class in which the individual who is in the process in hospital, we suppose that only those who treat it will be in contact with the infected population. Accordingly, the flow of corona virus pandemic along with the above assumptions can be representing in the following block diagram:

    Figure 1.  Flow diagram of the compartmental model of COVID-19.

    And the corresponding dynamical model has formulated through the nonlinear differential equations as follows,

    dS(t)dt=A(β1β2Im+I)SIdS,dE(t)dt=(1c)(β1β2Im+I)SIk(β1β2Im+I)EIdE,dI(t)dt=c(β1β2Im+I)SI+k(β1β2Im+I)EI(ϵ+γ1+d+µ)I,dQ(t)dt=ϵI(d+γ2)Q,dR(t)dt=γ1I+γ2QdR,
    µ

    with initial conditions

    S(0)>0,E(0)>0,I(0)>0,Q(0)>0,R(0)>0.

    In model (1), the birth rate A is taken into susceptible class and natural death rate of population is given by the parameter d. The susceptible will be infected through sufficient direct contacts with infected people in the absence of media alerts by β1, with fraction parameter c, where c[0,1]. The term β2Im+I reduce the transmission as media continuously alert the susceptible and exposed regarding infected cases and possible preventive measures. Usually, we assume that β1β2. As well, we consider the media awareness cannot stop the outbreak of COVID-19 but can aware the population to minimize the transmission risk through half saturation of media constant m. The death due to disease rate µµ affecting from infected class only. k represent to a fraction denoting the level of exogenous re-infection. The quarantined rate is given by ϵ. And the mean recovery rates of class I, Q are γi,i=1,2, respectively.

    It is easy see that the 4th and 5th equations are a linear differential equation with respect to variables I(t) and R(t), which are not appear in the other equations of model (1). Hence model (1) can be reduced to the following model:

    dS(t)dt=A(β1β2Im+I)SIdS,dE(t)dt=(1c)(β1β2Im+I)SIk(β1β2Im+I)EIdE,dI(t)dt=c(β1β2Im+I)SI+k(β1β2Im+I)EI(ϵ+γ1+d+µ)I.
    µ

    In this paper, we will discuss the dynamics of model (3) with initial conditions

    S(0)>0,  E(0)>0,  I(0)>0.

    This paper is organized as follows. In section 2, we will build the basic properties of model such as (positivity, boundedness of solutions and basic reproduction number). Existence of equilibrium points is presented in section 3. In section, the phenomenon of backward bifurcation is considered. The local and global stability of equilibrium points are studied in sections 4. In section 5, numerical simulation results are given. We conclude this paper with a brief conclusion.

    On the positivity of solutions for model (3), we have the following result.

    Theorem 2.1 Every solution of (3) with initial values (4) is positive as t > 0.

    Proof. Let t1=sup{t>0:S(t)>0,  E(t)>0,  I(t)>0}>0. It follows (3) that

    dS(t)dt=A(β1β2Im+I)SIdS,

    which can be written as

    ddt{S(t)exp[dt+t0(β1β2I(τ)m+I(τ))S(τ)I(τ)dτ]}          =Aexp[dt+t0(β1β2I(τ)m+I(τ))S(τ)I(τ)dτ].

    thus,

    S(t1)exp[dt1+t0(β1β2I(τ)m+I(τ))S(τ)I(τ)dτ]S(0)          =t10Aexp[dy+y0(β1β2I(τ)m+I(τ))S(τ)I(τ)dτ]dy,

    so that

    S(t1)=S(0)exp[dt110(β1β2I(τ)m+I(τ))S(τ)I(τ)dτ]          +exp[dt110(β1β2I(τ)m+I(τ))S(τ)I(τ)dτ]          ×t10Aexp[dy+y0(β1β2I(τ)m+I(τ))S(τ)I(τ)dτ]dy>0.

    Similarly, it can be shown that E(t) > 0 and I(t) > 0 for all time t > 0. Hence all solutions of the model (3) remain positive for all non-negative initial conditions, as required.

    Theorem 2.2 All solutions of model (1) which initiate in 5+ are uniformly bounded.

    Proof. Define the function N(t)=S(t)+E(t)+I(t)+Q(t)+R(t) and then take the time derivative of N(t) along the solution of model (1) gives dNdtALN. Then, dNdt+LNA, where L=min{d,d+µ}.

    Now, it is easy to verify that the solution of the above linear differential inequalities can be written as

    N(t)AL+(N0AL)eLt,

    where N0=(S(0),E(0),I(0),Q(0),R(0)). Hence,

    limsuptN(t)AL.

    and N(t)AL for t>0. Thus all solutions are uniformly bounded and the proof is complete.

    It is easy to see that model (3) always has a disease-free equilibrium P0(S0,0,0), where S0=Ad. We can calculate the reproduction number 0 of model (3) by using the next-generation matrix method illustrated by van den Driessche and Watmough in [14].

    0=cβ1Ad(ϵ+γ1+d+µ).

    Consequently, from Theorem 2 of [14], we have the following result.

    Theorem 2.3 The disease-free equilibrium P0 of the model (3) is locally asymptotically when 0<1 and P0 is unstable when 0>1.

    The basic reproduction number for COVID-19 infection 0 measures the average number of new COVID-19 infections generated by a single infected individual in a completely susceptible population [14], [15]. Theorem 2.3 implies that COVID-19 can be eliminated from the community (when 0<1) if the initial sizes of the sub-populations of the model (3) are in the attraction basin of the disease-free equilibrium P0. To ensure that COVID-19 elimination is independent of the initial sizes of the sub-populations, it is necessary to show that the disease-free equilibrium P0 is globally asymptotically stable when 0<1.

    In this section, we consider the number of equilibrium solutions the model (3). To do so, let P*(S*,E*,I*) be any arbitrary equilibrium of the model (3). Setting the right sides of the model (3) to zero gives

    S*=AX*I*+d,E*=(1c)AX*I*(kXI*+d)(X*I*+d).

    here

    X*=β1β2I*m+I*.

    Since we assume β1>β2, S* and E* are positive. now, substituting (12) in 3rd equation of the model (3) and simplifying it, we get

    D1I*4+D2I*3+D3I*2+D4I*+D5=0,

    where

    D1=k(ϵ+γ1+d+µ)(β1β2)2,D2=kA(β1β2)2(ϵ+γ1+d+µ)(β1β2)[d(k+1)+2kmβ1],D3=A(β1+β2)(2kmβ1+cd)(ϵ+γ1+d+µ)[md(k+1)(2β1+β2)+d2+kβ21m2],D4=kAβ21m2+cdA[β1(m+1)β2md(ϵ+γ1+d+µ)(2d+mβ1),D5=d2m2(ϵ+γ1+d+µ)(01).

    From (15), we can find that D1<0. And D5>0 when 0>1, D5<0 when 0<1. Thus, the number of possible positive real roots the polynomial (12) can have depends on the signs of D2, D3 and D4. Let f(x)=D1x4+D2x3+D3x2+D4x+D5. The various possibilities for the roots of f(x) can be analyzed using the Descartes Rule of Signs. The various possibilities for the roots of f(x) are tabulated in Table 1.

    Table 1.  Number of possible positive real roots of equation (14).
    Cases D1 D2 D3 D4 D5 R0 Number of sign changes Number of possible positive real roots
    1 + + + + 0 > 1 1 1
    + + + 0 < 1 2 0,2
    2 + + + 0 > 1 3 1,3
    + + 0 < 1 2 0,2
    3 + + + 0 > 1 3 1,3
    + + 0 < 1 4 0,2,4
    4 + + 0 > 1 3 1,3
    + 0 < 1 2 0,2
    5 + + + 0 > 1 1 1
    + + 0 < 1 2 0,2
    6 + + 0 > 1 3 1,3
    + 0 < 1 2 0,2
    7 + + 0 > 1 1 1
    + 0 < 1 2 0,2
    8 + 0 > 1 1 1
    0 < 1 0 0

     | Show Table
    DownLoad: CSV

    Theorem 3.1 The model (3)

    (i) has a unique endemic equilibrium if 0>1 and whenever Cases 1, 5, 7 and 8 are satisfied;

    (ii) could have more than one endemic equilibrium if 0>1 and Cases 2, 3, 4 and 6 are satisfied;

    (iii) could have 2 or more endemic equilibria if 0<1 and Cases 1–7 are satisfied.

    From the 4th and 5th equations of model (1) we can determent the values of Q* and R* through

    Q*=ϵI*d+γ2,R*=γ1I*+γ2Q*d.

    The existence of multiple endemic equilibria when 0<1 suggests the possibility of backward bifurcation [16], where the stable disease-free equilibrium co-exists with a stable endemic equilibrium when 0<1. This is can be obtained using Centre Manifold Theory.

    Theorem 3.2 The model (3) exhibits backward bifurcation whenever m>(1c)2Aβ2d2 and no backward bifurcation otherwise.

    Proof. To prove existence of backward bifurcation in the model (3) the Center Manifold approach as outlined by Castillo-Chavez and Song in [17] is used.

    Firstly, for clarity and understanding of the Center Manifold Theory the model (3) variables are transformed as follows x1=S,  x2=E,  x3=I. Define X=(x1,x2,x3)Τ (Τ denotes transpose), such that the model (3) can be rewritten as dXdt=F(X) where F=(f1,f2,f3). Hence,

    dx1(t)dt=f1=A(β1β2x3m+x3)x1x3dx1,dx2(t)dt=f2=(1c)(β1β2x3m+x3)x1x3k(β1β2x3m+x3)x2x3dx2,dx3(t)dt=f3=c(β1β2x3m+x3)x1x3+k(β1β2x3m+x3)x2x3(ϵ+γ1+d+µ)x3.

    Now let β1=β*1 be the bifurcation parameter. Observe that at 0=1,

    β1=β*1=d(ϵ+γ1+d+µ)cA.

    With β1=β*1 the transformed model equation (17) has a simple eigenvalue with zero real part and all other eigenvalues are negative (that is has a hyperbolic equilibrium point). Thus, Center Manifold Theory can be applied to investigate the local dynamics of the transformed system (17) near β1=β*1. Now the Jacobian matrix of the transformed system evaluated at COVID-19 free equilibrium P0 is obtained as

    J(P0)=(d0β1S00d(1c)β1S000cβ1S0(ϵ+γ1+d+µ)).

    It is easy to obtain the right eigenvectors of this Jacobian matrix as V=(v1,v2,v3)Τ, where (v1,v2,v3)Τ=(β1S0d,(1c)β1S0d,1). Similarly, it is possible to obtain the left eigenvectors which are denoted by W=(w1,w2,w3)=(0,0,1). Now proceeding to obtain the bifurcation coefficients a and b as defined in Theorem 4.1 in [17].

    First the non-vanishing partial derivatives of the transformed model (17) evaluated at COVID-19 free equilibrium are obtained as

    2f1x1x3=2f1x3x1=mβ2β1,  2f1x23=2βS0m,

    2f2x1x3=2f2x3x1=(1c)β1,  2f2x2x3=2f2x3x2=kβ1,  2f2x23=2(1c)β2S0m,2f3x1x3=2f3x3x1=cβ1,  2f3x23=2cβ2m,

    so that

    a=3k,i,j=1wkvivj2fkxixj      =2w3v1v32f3x1x3+w3v232f3x23      =2cβ21S0d2(1(1c)2β2S0dm).

    The sign of the bifurcation parameter b is associated with the following non-vanishing partial derivatives of F(X), also evaluated at the disease free equilibrium P0:

    2f1x3β1=S0,  2f2x3β1=(1c)S0,  2f3x3β1=cS0.

    The bifurcation coefficient b is obtained as

    b=3k,i=1vkwi2fkxiβ1      =v1w32f1x3β1+v2w32f2x3β1+v3w32f3x3β1      =cS0(1+(2cβ1)Ad2)>0.

    Obviously, b is always positive. From Theorem 3.2 the system (17) will exhibit backward bifurcation phenomena if the bifurcation coefficient a is positive. The positivity of a in (22) gives the condition for backward bifurcation, which leads to

    m>(1c)2Aβ2d2.

    In this section, the stability analysis of the all equilibrium points of model (3) studied as shown in the following theorems by used some criterion.

    Theorem 4.1 The COVID-19 equilibrium point P* of the model (3) is locally asymptotically if the following conditions are hold

    β2I*(2m+I*)(m+I*)2<β1,

    [(β1β2I*(2m+I*)(m+I*)2)((1c)S*kE*)]+d(d1+k)k<XI*<cdk(1c)c.

    Proof. The Jacobian matrix of model (3) at P* can be written as

    J(P*)=(a110a13a21a22a23a31a320),

    here

    a11=(XI*+d),a13=β2S*I*(2m+I*)(m+I*)2β1S*,                         a21=(1c)XI*,a22=(kXI*+d),                                   a23=(1c)(β1S*β2S*I*(2m+I*)(m+I*)2)k(β1E*β2E*I*(2m+I*)(m+I*)2),a31=cXI*,a32=kXI*.

    clearly, the characteristics equation of J(P*) is given by

    λ3+B1λ2+B2λ+B3=0,

    where

    B1=[a11+a22],                                           B2=a11a22a13a31a23a32,                         B3=[a11(a23a32)+a13(a21a32a22a31)].

    furthermore, we have that

    Δ=B1B2B3                                                                          =a11a22[a11+a22]+a11a13a31+a22a23a32+a13a21a32.

    Now, according to Routh-huewitz criterion P* will be locally asymptotically stable provided that B1>0,B3>0 and Δ>0. It is clear that if above conditions (26)–(27) hold.

    The purpose of this section is to investigate the global stability by using Lyapunov function for COVID-19 free equilibrium point and COVID-19 equilibrium point respectively. We obtain the result in the following theorems

    Theorem 4.2 The disease-free equilibrium P0 is globally asymptotically stable provided that the following condition holds:

    0c<1.

    Proof. Consider the following function

    V0(S,E,I)=(SS0S0lnSS0)+E+I.

    clearly, V0:3+ is a continuously differentiable function such that V0(S0,0,0)=0 and V0(S,E,I)>0,  (S,E,I)(S0,0,0). Further, we have

    dV0dt=(SS0S)[A(β1β2Im+I)SIdS]                                                      +[(1c)(β1β2Im+I)SIk(β1β2Im+I)EIdE]                +[c(β1β2Im+I)SI+k(β1β2Im+I)EI(ϵ+γ1+d+µ)I].

    now, by doing some algebraic manipulation and using the condition (33), we get

    dV0dtdS(SS0)2β2S0I2m+IdE[(ϵ+γ1+d+µ)β1S0]I.

    Obviously, dV0/dt=0 at P0=(S,0,0), moreover dV0/dt<0 otherwise. Hence dV0/dt is negative definite and then the solution starting from any initial point satisfy the condition (33), will approaches asymptotically to COVID-19 free equilibrium point. Hence the proof is complete.

    Theorem 4.3 P* in case i of Th. (3.1) is globally asymptotically stable if 0>1.

    Proof. At the COVID-19 equilibrium point P*=(S*,E*,I*),S*,E* and I* satisfies the following equations

    A(β1β2Im+I)SIdS=0,(1c)(β1β2Im+I)SIk(β1β2Im+I)EIdE=0,c(β1β2Im+I)SI+k(β1β2Im+I)EI(ϵ+γ1+d+µ)I=0

    By above equations (4.4) and assumptions

    SS*=x,  EE*=y,  II*=u

    we obtian

    ˙x=x[AS*(1x1)β1I*(u1)+β2I2*m+I*(u2(m+I*)m+I1)]˙y=y{(1c)[β1S*I*E*(xuy1)β2S*I2*(m+I*)E*((m+I*)xu2(m+I)y1)]kβ1I*(u1)+kβ2I2*m+I*((m+I*)u2m+I1)}˙u=u[cβ1S*(x1)cβ2S*I*m+I*((m+I*)xum+I1)+kβ1E*(y1)kβ2E*I*m+I*((m+I*)yum+I1)]

    now, define the Lyapunov function

    V1=S*(x1lnx)+E*(y1lny)+I*(u1lnu)

    clearly, by derivative of V1 we get

    dV1dt=S*x1x˙x+E*y1y˙y+I*u1u˙u

    dV1dt=(x1)[A(1x1)β1S*I*(u1)+β2S*I2*m+I*(u2(m+I*)m+I1)]        +(y1){(1c)[β1S*I*(xuy1)β2S*I2*m+I*((m+I*)xu2(m+I)y1)]+kβ2E*I2*m+I*((m+I*)u2m+I1)}        +(u1)[cβ1S*I*(x1)cβ2S*I2*m+I*((m+I*)xum+I1)kβ2E*I2*m+I*((m+I*)yum+I1)]
    furthermore, by simplifying the resulting terms, we get that

    =A[2x1x]+β1S*I*[x+uc(x+u)(1c)(y+xuy)]    β2S*I2*m+I*[xy(1c)cu+(u2(1c)xu2ycxu)(m+I*m+I)]    kβ2E*I2*m+I*[yu+(u2uy)(m+I*m+I)]

    Since the arithmetical mean is greater than, or equal to the geometrical mean, then 2x1x0 for x>0 and 2x1x=0 if and only if x=1; x+uc(x+u)(1c)(y+xuy)0 for x,y,u>0 and x+uc(x+u)(1c)(y+xuy)=0 if and only if x=y=u=1; yu+(u2uy)(m+I*m+I)0 for y,u>0 and yu+(u2uy)(m+I*m+I)=0 if and only if y=u=1. Therefore, ˙V10 for x,y,u>0 and ˙V1=0 if and only if x=y=u=1, the maximum invariant set of model (3) on the set {(x,y,u):˙V1=0} is the singleton (1,1,1). Thus, the COVID-19 equilibrium point P* is globally asymptotically stable if 0>1,by LaSalle Invariance Principle [18]. Hence, the proof is complete.

    For the parameters values of model (1.1), we can chosen the parameters values from real data available sense Feb. 24 2020 to Apr. 5 2020. The total population of the Iraq for the year 2020 is approximately 40 × 106 [19]. The life expectancy in Iraq is approximatily 71.08 [19]. Clearly, we can obtain that the natural death rate d=3.8545×105 per day. The birth rate is estimated from A/d=N, and assumed that this is to be the bound population in the disease absence. So, A=1541.8 per day and the other parameters of our model shows that in Table 2.

    Table 2.  Definitions and values of model parameters.
    Parameter Definition Value Source
    A Birth rate 1541.8 [19]
    β1 Transmission contact rate between S and I 0.5 Estimated
    c Fraction constant [0–1] Estimated
    β2 Awareness rate 0.1 Estimated
    m Half saturation of media constant 70 Estimated
    d Natural death rate 3.854510−5 [19],[20]
    k Fraction denoting the level of exogenous re-infection 0.05 Estimated
    ϵ Quarantined rate 1/7 [13]
    γ1 Recovery rate from infected wihout quarantin strategy 0.033 Estimated
    γ2 Recovery rate from quarantin class 1/18 [13]
    µ Death due to disease rate 0.38 [19]

     | Show Table
    DownLoad: CSV

    We plot the solution trajectories of model (1) with initial point (15,20,500,1000,150) which converges to COVID-19 equilibrium point P*=(1,27,2773,5428,19371), shown that in Figure 2.

    Figure 2.  Solution trajectories converge to COVID-19 equilibrium point P*=(1,27,2773,5428,19371), by parameter value in Table 2.
    Table 3.  Different government control measures and corresponding β1 values.
    No. Date Government measures β1
    1 Feb. 24 2020 (1) detection of the first case of COVID-19 in Iraq 0.3
    (2) quarantined as preliminary control
    2 Feb. 25 2020 (1) medical examination for all individuals who are in contact with the affected case 0.1
    (2) cancellation of some mass gatherings
    (3) increase the awareness programs about prevention measures
    3 Feb. 25-Mar. 24 2020 (1) cancellation of all religious and social events throughout Iraq 0.09
    (2) preventing movement between all provinces
    (3) the suspension of attendance at universities and schools
    (4) providing a number of hospitals to be places for prevention confirmed cases
    4 Mar. 24-Apr. 5 2020 (1) close all borders with neighboring countries 0.08
    (2) to declare a state of emergency and impose a curfew
    (3) medical support from the government
    (4) methodological improvement on the diagnosis and treatment strategy
    (5) spontaneous household quarantine by citizens
    (6) more newly-hospitals put into use
    (7) massive online teaching in postponed semester
    (8) addition of new diagnosis method clinically diagnosis in Baghdad and some provinces

     | Show Table
    DownLoad: CSV

    In the face of the COVID-19 outbreak, many stringent measures were taken by Iraqi government will show in Table 3, to simulate the impact of different government control measures on the number of all S(t),E(t),I(t),Q(t) and R(t). We assumed that some values to contact rates with awareness Table 3, with the other parameters in Table 2 staying still on the all stages.

    The following Figure 3 shows the values of S(t),E(t),I(t),Q(t) and R(t) under government measures that above to control of COVID-19 outbreak.

    Figure 3.  Time series to value simulation curve of different values of contact rates β1=0.3,0.1,0.09,0.08 respectively with keeping other parameters values are taken in Table 2.

    Clearly, from above figure for effect of contact rate Table 3, We obtain that in case decrease the contact rate (social isolation) the reproduction number less than one and the dynamical behavior of model (1.1) still approaches to COVID-19 equilibrium point. Hence, the backward bifurcation is occur. Now, to investigate the effect of the quarantined strategy it is given by ϵ on the dynamical behavior of model (1.1) and to control to COVID-19 outbreak in Iraq. We study the impact of this parameter on values of S(t),E(t),I(t),Q(t) and R(t) in follows Table 4 and shows the results in Figure 4.

    Table 4.  Different government control measures and corresponding ϵ values.
    No. Date Government measures ϵ
    1 Feb. 24 2020 (1) quarantined as preliminary control in Iraq 0.2
    2 Feb. 25 2020 (1) medical examination for all individuals who are in contact with the affected case 0.4
    (2) cancellation of some mass gatherings
    (3) increase the awareness programs about prevention measures
    3 Feb. 25-Mar. 24 2020 (1) direct the media to explain the symptoms of the epidemic 2.5
    (2) Preventing movement between all provinces
    (3) Providing a number of hospitals to be places for prevention confirmed cases
    4 Mar. 24-Apr. 5 2020 (1) to declare a state of emergency and impose a curfew to reduce the contact between people 4.5
    (2) medical support from the government
    (3) methodological improvement on the diagnosis and treatment strategy
    (4) spontaneous household quarantine by citizens
    (5) addition of new diagnosis method clinically diagnosis in Baghdad and some provinces

     | Show Table
    DownLoad: CSV

    The following Figure 4 shows the values of S(t),E(t),I(t),Q(t) and R(t) under government measures that above to control of COVID-19 outbreak.

    Figure 4.  Time series to value simulation curve of different values of quarantined rates ϵ=0.2,0.4,2.5,4.5 respectively with keeping other parameters values are taken in Table 2.

    Clearly, from above investigate to impact of the quarantined strategy Table 4, when the quarantine strategy increasing we get the number of infected is decrease and other classes are increase. Here, we ask whether the quarantine strategy is the best solution? The answer is possible, but for specific numbers. Whereas, if the quarantine is more than the capacity of the health institutions. We get the dynamical behavior of model (1.1) lose the stability as shown in Figure 5.

    Figure 5.  Time series to value simulation curve of different values of quarantined rates 20.5 ≤ ϵ ≤ 30.5. With keeping other parameters values are taken in Table 2.

    In this research, a mathematical model of COVID-19 transmission has been proposed by compartment the total population into five epidemiological status, namely, susceptible S(t), exposed E(t), infected I(t), quarantine Q(t) and recovered R(t). The model incorporates the impact of social awareness programs conducted by public health officials with quarantine strategy in hospital. It has been noticed that these awareness programs and quarantine strategy result in human behavioral changes in order to avoid risk of disease transmission. The model mainly accounts for the reduction in disease class due to awareness. While we can say the disease goes away due to applied the quarantine it well. The proposed model has two biological equilibrium points are COVID-19 free and COVID-19. The COVID-19 free has been local stability when 0<1. Otherwise when 0>1, the COVID-19 free point becomes unstable and the dynamical behavior of the model converges to COVID-19 equilibruim point. The backward bifurcation occur if 0=1 at the parameter bifurcation β1=β*1=d(ϵ+γ1+d+µ)/cA. As well as the different government control measures have been also discussed. Furthermore, to shown and understand the effect of quarantine rate of disease we have choosed many different value of it say parameter then we have obtained some different results see Table 4 and Figure 4.


    Acknowledgments



    This study is not funded by any agency and is being conducted by the authors independently.

    Authors' contribution



    Study conception and design: FM and DF; data collection: FM; analysis and interpretation of results: FM, DF, MP, and NM; draft manuscript preparation: FM and EK; critical review and editing: DF, MP, and NM. All authors reviewed the results and approved the final version of the manuscript.

    Conflict of interest



    The authors declare no conflicts of interest.

    [1] European Foundation for the Improvement of Living and Working Conditions. Work organisation. [cited 2024 Dec 08th] Available from: https://www.eurofound.europa.eu/en/topic/work-organisation
    [2] Wilson MG, Dejoy DM, Vandenberg RJ, et al. (2004) Work characteristics and employee health and well-being: Test of a model of healthy work organization. J Occup Organ Psychol 77: 565-588. https://doi.org/10.1348/0963179042596522
    [3] Di Fabio A (2017) Positive Healthy Organizations: Promoting Well-Being, Meaningfulness, and Sustainability in Organizations. Front Psychol 8: 1938. https://doi.org/10.3389/fpsyg.2017.01938
    [4] Elovainio M, Heponiemi T, Sinervo T, et al. (2010) Organizational justice and health; review of evidence. G Ital Med Lav Ergon 32: B5-B9.
    [5] Magnavita N, Chiorri C, Acquadro Maran D, et al. (2022) Organizational Justice and Health: A Survey in Hospital Workers. Int J Environ Res Public Health 19: 9739. https://doi.org/10.3390/ijerph19159739
    [6] Magnavita N, Chiorri C, Karimi L, et al. (2022) The Impact of Quality of Work Organization on Distress and Absenteeism among Healthcare Workers. Int J Environ Res Public Health 19: 13458. https://doi.org/10.3390/ijerph192013458
    [7] Whitford AB, Lee SY, Yun T, et al. (2010) Collaborative Behavior and the Performance of Government Agencies. Int Public Manag J 13: 321-349. https://doi.org/10.1080/10967494.2010.529378
    [8] Fleming AC, O'Brien K, Steele S, et al. (2022) An investigation of the nature and consequences of counterproductive work behavior. Hum Perform 35: 178-192. https://doi.org/10.1080/08959285.2022.2102635
    [9] Spector PE, Fox S (2002) An emotion-centered model of voluntary work behavior: Some parallels between counterproductive work behavior and organizational citizenship behavior. Hum Resour Manag Rev 12: 269-292. https://doi.org/10.1016/S1053-4822(02)00049-9
    [10] De Clercq D, Haq IU, Azeem MU (2019) Time-related work stress and counterproductive work behavior. Pers Rev 48: 1756-1781. https://doi.org/10.1108/PR-07-2018-0241
    [11] Lehman WE, Simpson DD (1992) Employee substance use and on-the-job behaviors. J Appl Psychol 77: 309-321. https://psycnet.apa.org/doi/10.1037/0021-9010.77.3.309
    [12] Garbarino S, Lanteri P, Durando P, et al. (2016) Co-Morbidity, Mortality, Quality of Life and the Healthcare/Welfare/Social Costs of Disordered Sleep: A Rapid Review. Int J Environ Res Public Health 13: 831. https://doi.org/10.3390/ijerph13080831
    [13] Magnavita N, Garbarino S (2017) Sleep, Health and Wellness at Work: A Scoping Review. Int J Environ Res Public Health 14: 1347. https://doi.org/10.3390/ijerph14111347
    [14] Garbarino S, Guglielmi O, Sanna A, et al. (2016) Risk of Occupational Accidents in Workers with Obstructive Sleep Apnea: Systematic Review and Meta-analysis. Sleep 39: 1211-1218. https://doi.org/10.5665/sleep.5834
    [15] Garbarino S, Magnavita N, Guglielmi O, et al. (2017) Insomnia is associated with road accidents. Further evidence from a study on truck drivers. PLoS One 12: e0187256. https://doi.org/10.1371/journal.pone.0187256
    [16] Chang AM, Aeschbach D, Duffy JF, et al. (2015) Evening use of light-emitting eReaders negatively affects sleep, circadian timing, and next-morning alertness. Proc Natl Acad Sci 112: 1232-1237. https://doi.org/10.1073/pnas.1418490112
    [17] Dawson D, Encel N, Lushington K (1995) Improving Adaptation to Simulated Night Shift: Timed Exposure to Bright Light Versus Daytime Melatonin Administration. Sleep 18: 11-21. https://doi.org/10.1093/sleep/18.1.11
    [18] LeBlanc M, Mérette C, Savard J, et al. (2009) Incidence and Risk Factors of Insomnia in a Population-Based Sample. Sleep 32: 1027-1037. https://doi.org/10.1093/sleep/32.8.1027
    [19] Fietze I, Rosenblum L, Salanitro M, et al. (2022) The Interplay Between Poor Sleep and Work-Related Health. Front Public Health 10. https://doi.org/10.3389/fpubh.2022.866750
    [20] Swanson LM, Arnedt JT, Rosekind MR, et al. (2011) Sleep disorders and work performance: findings from the 2008 National Sleep Foundation Sleep in America poll. J Sleep Res 20: 487-494. https://doi.org/10.1111/j.1365-2869.2010.00890.x
    [21] Van Laethem M, Beckers DGJ, Kompier MAJ, et al. (2015) Bidirectional relations between work-related stress, sleep quality and perseverative cognition. J Psychosom Res 79: 391-398. https://doi.org/10.1016/j.jpsychores.2015.08.011
    [22] Magnavita N, Chirico F, Garbarino S, et al. (2024) Stress, sleep, and cardiovascular risk in police officers: A scoping review. J Health Soc Sci 9: 9-23. https://dx.doi.org/10.19204/2024/STRS1
    [23] Garbarino S, Durando P, Guglielmi O, et al. (2016) Sleep Apnea, Sleep Debt and Daytime Sleepiness Are Independently Associated with Road Accidents. A Cross-Sectional Study on Truck Drivers. PLoS One 11: e0166262. https://doi.org/10.1371/journal.pone.0166262
    [24] Qiu D, Li Y, Li R, et al. (2022) Long working hours, work-related stressors and sleep disturbances among Chinese government employees: A large population-based follow-up study. Sleep Med 96: 79-86. https://doi.org/10.1016/j.sleep.2022.05.005
    [25] Barnes CM, Guarana C, Lee J, et al. (2023) Using wearable technology (closed loop acoustic stimulation) to improve sleep quality and work outcomes. J Appl Psychol 108: 1391-1407. https://psycnet.apa.org/doi/10.1037/apl0001077
    [26] Killgore WDS (2010) Effects of sleep deprivation on cognition. Progress in Brain Research . Elsevier 105-129. https://doi.org/10.1016/B978-0-444-53702-7.00007-5
    [27] Walker MP (2009) The Role of Sleep in Cognition and Emotion. Ann N Y Acad Sci 1156: 168-197. https://doi.org/10.1111/j.1749-6632.2009.04416.x
    [28] Pilcher JJ, Huffcutt AI (1996) Effects of Sleep Deprivation on Performance: A Meta-Analysis. Sleep 19: 318-326. https://doi.org/10.1093/sleep/19.4.318
    [29] Lim J, Dinges DF (2010) A meta-analysis of the impact of short-term sleep deprivation on cognitive variables. Psychol Bull 136: 375-389. https://doi.org/10.1037/a0018883
    [30] Åkerstedt T, Nordin M, Alfredsson L, et al. (2012) Predicting changes in sleep complaints from baseline values and changes in work demands, work control, and work preoccupation – The WOLF-project. Sleep Med 13: 73-80. https://doi.org/10.1016/j.sleep.2011.04.015
    [31] Kim G, Min B, Jung J, et al. (2016) The association of relational and organizational job stress factors with sleep disorder: analysis of the 3rd Korean working conditions survey (2011). Ann Occup Environ Med 28: 46. https://doi.org/10.1186/s40557-016-0131-2
    [32] Levi L (1990) Occupational stress: Spice of life or kiss of death?. Am Psychol 45: 1142-1145. https://psycnet.apa.org/doi/10.1037/0003-066X.45.10.1142
    [33] Maslach C, Schaufeli WB, Leiter MP (2001) Job Burnout. Annu Rev Psychol 52: 397-422. https://doi.org/10.1146/annurev.psych.52.1.397
    [34] Podsakoff NP, LePine JA, LePine MA (2007) Differential challenge stressor-hindrance stressor relationships with job attitudes, turnover intentions, turnover, and withdrawal behavior: A meta-analysis. J Appl Psychol 92: 438-454. https://psycnet.apa.org/doi/10.1037/0021-9010.92.2.438
    [35] Guglielmi O, Magnavita N, Garbarino S (2018) Sleep quality, obstructive sleep apnea, and psychological distress in truck drivers: a cross-sectional study. Soc Psychiatry Psychiatr Epidemiol 53: 531-536. https://doi.org/10.1007/s00127-017-1474-x
    [36] Magnavita N, Capitanelli I, Garbarino S, et al. (2018) Work-related stress as a cardiovascular risk factor in police officers: a systematic review of evidence. Int Arch Occup Environ Health 91: 377-389. https://doi.org/10.1007/s00420-018-1290-y
    [37] Marcatto F, Ferrante D (2021) Beyond the assessment of work-related stress risk: the management standards approach for organizational wellbeing. G Ital Med Lav Ergon 43: 126-130.
    [38] Marcatto F, Patriarca E, Bramuzzo D, et al. (2024) Job demands and DHEA-S levels: a study on healthcare workers. Occup Med 74: 225-229. https://doi.org/10.1093/occmed/kqae017
    [39] Acquadro Maran D, Magnavita N, Garbarino S (2022) Identifying Organizational Stressors That Could Be a Source of Discomfort in Police Officers: A Thematic Review. Int J Environ Res Public Health 19: 3720. https://doi.org/10.3390/ijerph19063720
    [40] Rees G (2020) Getting the Sergeants on your side: the importance of interpersonal relationships and cultural interoperability for generating interagency collaboration between nurses and the police in custody suites. Sociol Health Illn 42: 111-125. https://doi.org/10.1111/1467-9566.12989
    [41] Magnavita N (2014) Workplace violence and occupational stress in healthcare workers: a chicken-and-egg situation-results of a 6-year follow-up study. J Nurs Scholarsh 46: 366-376. https://doi.org/10.1111/jnu.12088
    [42] Magnavita N (2013) The exploding spark: workplace violence in an infectious disease hospital--a longitudinal study. BioMed Res Int 2013: 316358. https://doi.org/10.1155/2013/316358
    [43] Marcatto F, Orrico K, Luis O, et al. (2021) Exposure to organizational stressors and health outcomes in a sample of Italian local police officers. Polic J Policy Pract 15: 2241-2251. https://doi.org/10.1093/police/paab052
    [44] Kim H, Kim B, Min K, et al. (2011) Association between Job Stress and Insomnia in Korean Workers. J Occup Health 53: 164-174. https://doi.org/10.1539/joh.10-0032-OA
    [45] Linton SJ (2004) Does work stress predict insomnia? A prospective study. Br J Health Psychol 9: 127-136. https://doi.org/10.1348/135910704773891005
    [46] Åkerstedt T (2006) Psychosocial stress and impaired sleep. Scand J Work Environ Health 32: 493-501. https://doi.org/10.5271/sjweh.1054
    [47] Sonnentag S, Binnewies C, Mojza EJ (2008) ‘Did you have a nice evening?’ A day-level study on recovery experiences, sleep, and affect. J Appl Psychol 93: 674-684. https://psycnet.apa.org/doi/10.1037/0021-9010.93.3.674
    [48] Garbarino S, Magnavita N (2019) Sleep problems are a strong predictor of stress-related metabolic changes in police officers. A prospective study. PLoS One 14: e0224259. https://doi.org/10.1371/journal.pone.0224259
    [49] Törnroos M, Hakulinen C, Hintsanen M, et al. (2017) Reciprocal relationships between psychosocial work characteristics and sleep problems: A two-wave study. Work Stress 31: 63-81. https://doi.org/10.1080/02678373.2017.1297968
    [50] Roehrs T, Roth T (2008) Caffeine: Sleep and daytime sleepiness. Sleep Med Rev 12: 153-162. https://doi.org/10.1016/j.smrv.2007.07.004
    [51] Crain TL, Hammer LB, Bodner T, et al. (2014) Work–family conflict, family-supportive supervisor behaviors (FSSB), and sleep outcomes. J Occup Health Psychol 19: 155-167. https://doi.org/10.1037/a0036010
    [52] Spector PE, Fox S (2005) The Stressor-Emotion Model of Counterproductive Work Behavior. Counterproductive work behavior: Investigations of actors and targets . Washington DC, US: American Psychological Association 151-174. https://psycnet.apa.org/doi/10.1037/10893-007
    [53] Christian MS, Ellis APJ (2011) Examining the Effects of Sleep Deprivation on Workplace Deviance: A Self-Regulatory Perspective. Acad Manage J 54: 913-934. https://doi.org/10.5465/amj.2010.0179
    [54] Barnes CM, Wagner DT (2009) Changing to daylight saving time cuts into sleep and increases workplace injuries. J Appl Psychol 94: 1305-1317. https://doi.org/10.1037/a0015320
    [55] Frone MR (2015) Relations of negative and positive work experiences to employee alcohol use: Testing the intervening role of negative and positive work rumination. J Occup Health Psychol 20: 148-160. https://doi.org/10.1037/a0038375
    [56] Krischer MM, Penney LM, Hunter EM (2010) Can counterproductive work behaviors be productive? CWB as emotion-focused coping. J Occup Health Psychol 15: 154-166. https://psycnet.apa.org/doi/10.1037/a0018349
    [57] Hobfoll SE (1989) Conservation of resources: A new attempt at conceptualizing stress. Am Psychol 44: 513-524. https://doi.org/10.1037//0003-066x.44.3.513
    [58] Hobfoll SE, Halbesleben J, Neveu JP, et al. (2018) Conservation of Resources in the Organizational Context: The Reality of Resources and Their Consequences. Annu Rev Organ Psychol Organ Behav 5: 103-128. https://doi.org/10.1146/annurev-orgpsych-032117-104640
    [59] Heydarifard Z, Krasikova DV (2023) Losing sleep over speaking up at work: A daily study of voice and insomnia. J Appl Psychol 108: 1559-1572. https://doi.org/10.1037/apl0001087
    [60] Hobfoll SE, Shirom A (2001) Conservation of resources theory: Applications to stress and management in the workplace. Handbook of organizational behavior . New York, NY, US: Marcel Dekker 57-80.
    [61] Henderson AA, Horan KA (2021) A meta-analysis of sleep and work performance: An examination of moderators and mediators. J Organ Behav 42: 1-19. https://doi.org/10.1002/job.2486
    [62] Hobfoll SE (2001) The Influence of Culture, Community, and the Nested-Self in the Stress Process: Advancing Conservation of Resources Theory. Appl Psychol 50: 337-421. https://doi.org/10.1111/1464-0597.00062
    [63] Kuper LE, Gallop R, Greenfield SF (2010) Changes in Coping Moderate Substance Abuse Outcomes Differentially across Behavioral Treatment Modality. Am J Addict 19: 543-549. https://doi.org/10.1111/j.1521-0391.2010.00074.x
    [64] Wills TA (1990) Stress and coping factors in the epidemiology of substance use. Research advances in alcohol and drug problems . New York, NY, US: Plenum Press 215-250. https://doi.org/10.1007/978-1-4899-1669-3_7
    [65] Mauro PM, Canham SL, Martins SS, et al. (2015) Substance-use coping and self-rated health among US middle-aged and older adults. Addict Behav 42: 96-100. https://doi.org/10.1016/j.addbeh.2014.10.031
    [66] Jeong J, Lee JH, Karau SJ (2024) Sleepless nights at work: examining the mediating role of insomnia in customer mistreatment. Balt J Manag 19: 308-326. https://doi.org/10.1108/BJM-11-2023-0426
    [67] Walker MP (2008) Cognitive consequences of sleep and sleep loss. Sleep Med 9: S29-S34. https://doi.org/10.1016/S1389-9457(08)70014-5
    [68] Opoku MA, Kang SW, Kim N (2023) Sleep-deprived and emotionally exhausted: depleted resources as inhibitors of creativity at work. Pers Rev 52: 1437-1461. https://doi.org/10.1108/PR-09-2021-0620
    [69] Dahlgren A, Kecklund G, Åkerstedt T (2005) Different levels of work-related stress and the effects on sleep, fatigue and cortisol. Scand J Work Environ Health 31: 277-285. https://psycnet.apa.org/doi/10.5271/sjweh.883
    [70] Hobfoll SE, Freedy J (1993) Conservation of Resources: A General Stress Theory Applied To Burnout. Professional Burnout . Routledge 115-129.
    [71] Opoku MA, Kang SW, Choi SB (2023) The influence of sleep on job satisfaction: examining a serial mediation model of psychological capital and burnout. Front Public Health 11: 1149367. https://doi.org/10.3389/fpubh.2023.1149367
    [72] Penney LM, Spector PE (2005) Job stress, incivility, and counterproductive work behavior (CWB): the moderating role of negative affectivity. J Organ Behav 26: 777-796. https://doi.org/10.1002/job.336
    [73] Fox S, Spector PE, Miles D (2001) Counterproductive Work Behavior (CWB) in Response to Job Stressors and Organizational Justice: Some Mediator and Moderator Tests for Autonomy and Emotions. J Vocat Behav 59: 291-309. https://doi.org/10.1006/jvbe.2001.1803
    [74] Zhao C, Zhu Y, Zhuang JY (2024) Spillover and spillback: Linking daily job insecurity to next-day counterproductive work behavior. Scand J Psychol 65: 195-205. https://doi.org/10.1111/sjop.12968
    [75] Saleem F, Gopinath C (2015) Injustice, Counterproductive Work Behavior and mediating role of Work Stress. Pak J Commer Soc Sci 9: 683-699.
    [76] Francis L, Barling J (2005) Organizational injustice and psychological strain. Can J Behav Sci Rev Can Sci Comport 37: 250-261. https://psycnet.apa.org/doi/10.1037/h0087260
    [77] Cohen A, Diamant A (2019) The role of justice perceptions in determining counterproductive work behaviors. Int J Hum Resour Manag 30: 2901-2924. https://doi.org/10.1080/09585192.2017.1340321
    [78] Kelloway EK, Francis L, Prosser M, et al. (2010) Counterproductive work behavior as protest. Hum Resour Manag Rev 20: 18-25. https://doi.org/10.1016/j.hrmr.2009.03.014
    [79] Bakker AB, Vries JD de (2021) Job Demands–Resources theory and self-regulation: new explanations and remedies for job burnout. Anxiety Stress Coping 34: 1-21. https://doi.org/10.1080/10615806.2020.1797695
    [80] Mitchell MS, Greenbaum RL, Vogel RM, et al. (2019) Can You Handle the Pressure? The Effect of Performance Pressure on Stress Appraisals, Self-regulation, and Behavior. Acad Manage J 62: 531-552. https://doi.org/10.5465/amj.2016.0646
    [81] Johnson RE, Lin SH, Lee HW (2018) Self-Control as the Fuel for Effective Self-Regulation at Work: Antecedents, Consequences, and Boundary Conditions of Employee Self-Control. Advances in Motivation Science . Elsevier 87-128. https://doi.org/10.1016/bs.adms.2018.01.004
    [82] Baqutayan SMS (2015) Stress and Coping Mechanisms: A Historical Overview. Mediterr J Soc Sci 6: 479-488. https://doi.org/10.5901/mjss.2015.v6n2s1p479
    [83] Carver CS, Scheier MF, Weintraub JK (1989) Assessing coping strategies: A theoretically based approach. J Pers Soc Psychol 56: 267-283. https://doi.org/10.1037/0022-3514.56.2.267
    [84] Brady KT, Sonne SC (1999) The role of stress in alcohol use, alcoholism treatment, and relapse. Alcohol Res Health J Natl Inst Alcohol Abuse Alcohol 23: 263-271.
    [85] Provencher T, Lemyre A, Vallières A, et al. (2020) Insomnia in personality disorders and substance use disorders. Curr Opin Psychol 34: 72-76. https://doi.org/10.1016/j.copsyc.2019.10.005
    [86] Taylor DJ, Bramoweth AD, Grieser EA, et al. (2013) Epidemiology of Insomnia in College Students: Relationship With Mental Health, Quality of Life, and Substance Use Difficulties. Behav Ther 44: 339-348. https://doi.org/10.1016/j.beth.2012.12.001
    [87] Angarita GA, Emadi N, Hodges S, et al. (2016) Sleep abnormalities associated with alcohol, cannabis, cocaine, and opiate use: a comprehensive review. Addict Sci Clin Pract 11: 9. https://doi.org/10.1186/s13722-016-0056-7
    [88] Chakravorty S, Vandrey RG, He S, et al. (2018) Sleep Management Among Patients with Substance Use Disorders. Med Clin North Am 102: 733-743. https://doi.org/10.1016/j.mcna.2018.02.012
    [89] Manconi M, Ferri R, Miano S, et al. (2017) Sleep architecture in insomniacs with severe benzodiazepine abuse. Clin Neurophysiol Off J Int Fed Clin Neurophysiol 128: 875-881. https://doi.org/10.1016/j.clinph.2017.03.009
    [90] Weiss NH, Kiefer R, Goncharenko S, et al. (2022) Emotion regulation and substance use: A meta-analysis. Drug Alcohol Depend 230: 109131. https://doi.org/10.1016/j.drugalcdep.2021.109131
    [91] Grant S, Contoreggi C, London ED (2000) Drug abusers show impaired performance in a laboratory test of decision making. Neuropsychologia 38: 1180-1187. https://doi.org/10.1016/S0028-3932(99)00158-X
    [92] de Wit H (2009) Impulsivity as a determinant and consequence of drug use: a review of underlying processes. Addict Biol 14: 22-31. https://doi.org/10.1111/j.1369-1600.2008.00129.x
    [93] Bastien CH, Vallières A, Morin CM (2001) Validation of the Insomnia Severity Index as an outcome measure for insomnia research. Sleep Med 2: 297-307. https://doi.org/10.1016/S1389-9457(00)00065-4
    [94] Castronovo V, Galbiati A, Marelli S, et al. (2016) Validation study of the Italian version of the Insomnia Severity Index (ISI). Neurol Sci 37: 1517-1524. https://doi.org/10.1007/s10072-016-2620-z
    [95] Marcatto F, Di Blas L, Luis O, et al. (2022) The Perceived Occupational Stress Scale: A brief tool for measuring workers' perceptions of stress at work. Eur J Psychol Assess 38: 293-306. https://psycnet.apa.org/doi/10.1027/1015-5759/a000677
    [96] Barbaranelli C, Fida R, Gualandri M (2013) Assessing counterproductive work behavior: A study on the dimensionality of CWB-Checklist. TPM-Test Psychom Methodol Appl Psychol 20: 235-248.
    [97] Bongelli R, Fermani A, Canestrari C, et al. (2022) Italian validation of the situational Brief Cope Scale (I-Brief Cope). PLoS One 17: e0278486. https://doi.org/10.1371/journal.pone.0278486
    [98] Wilson Van Voorhis CR, Morgan BL (2007) Understanding Power and Rules of Thumb for Determining Sample Sizes. Tutor Quant Methods Psychol 3: 43-50. https://doi.org/10.20982/tqmp.03.2.p043
    [99] Kenny DA MedPower: An interactive tool for the estimation of power in tests of mediation [Computer Software] (2017). Available from: https://davidakenny.shinyapps.io/MedPower/
    [100] Wills T, Shiffman S (1986) Coping and Substance Use: A Conceptual Framework. Coping and substance use . Orlando, FL: Academic Press 3-24.
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