The Relationship between Prevention and Panic from COVID-19, Ethical Principles, Life Expectancy, Anxiety, Depression and Stress

The present study aims to assess the relationship between prevention and panic from COVID-19, ethical principles, life expectancy, anxiety, depression, and stress in auditors and financial managers of small- and medium-sized Iraqi firms. In other words, this paper seeks to answer the question of whether different types of prevention and panic from COVID-19 can enhance the ethical principles, life expectancy, anxiety, depression, and stress, or not. The study method is practical in its objective and descriptive survey procedure. The study’s statistical population includes 185 employed auditors in audit firms, and 215 financial managers of small- and medium-sized Iraqi firms were selected as a sample of the study using the Cochran Sampling Method. In this paper, PLS tests are used to assess the effect of independent variables on the dependent variable. The results indicate no significant relationship between prevention from COVID-19 and ethical principles and life expectancy. However, the association between prevention from COVID-19 and anxiety, depression, and stress, and between panic from COVID-19 and ethical principles, life expectancy, anxiety, depression, and stress is positive and significant. The higher the panic from COVID-19, the more ethical principles, life expectancy, anxiety, depression, and stress. Since no study has been carried out so far on the effect of prevention and panic from COVID-19, ethical principles, life expectancy, depression, and stress in Iraqi firms, the present study results can provide valuable information and contribute to the development of science and knowledge.


Introduction
COVID-19 has accelerated dramatically since January 2020, infecting more than 373 million people worldwide and causing the deaths of more than 5 million people worldwide [1]. This pandemic led to a dramatic shift in the way people lived, worked, and played. Around the world, many companies, especially those belonging to the SME sector, have suffered enormous financial losses, have lost liquidity, or have even gone bankrupt [2][3][4].
As the world economy collapsed, families could not see their loved ones because air travel was also disrupted by the COVID [5]. As a result, governments and public health officials worldwide have provided guidelines to help smooth the curve [6]. Forcefully or voluntarily, governments issued three standard guidelines for staying home [7][8][9], using a mask [10][11][12] and observing social distancing when attending public gatherings [13].
The COVID-19 pandemic has created a rapidly evolving and threatened situation. Recent reports have shown that the coronavirus pandemic is significantly associated with the risk of mental disorders (e.g., schizophrenia, anxiety, depression, anxiety, acute stress disorder, and suicide) among health care professionals and the general public [87][88][89][90][91][92]. Similar pandemics (such as SARS) have had serious negative consequences on mental health and have mainly caused anxiety and depressive disorders [89,90]. For example, in the early stages of the SARS pandemic, a wide range of people developed mental illnesses, including persistent depression, anxiety, panic attacks, motor excitement, symptoms of psychosis, delirium, and even suicide [88,92]. Therefore, COVID-19 can significantly affect people's daily emotional experiences. In most cases, no specific cause for anxiety can be found, and it is caused by a set of biological, psychological, and social factors. Studies show that heredity also plays a role in the development of anxiety [91]. Along with psychological and biological factors that allow anxiety in humans, the role of social factors should not be ignored. Being in a certain social situation, especially if that situation plays a decisive role in a person's current or future life, naturally increases anxiety.
Responding to COVID-19 threats and the public health measures taken to assist it has slowed the transmission of the COVID-19 virus and may lead to a wide range of negative emotions that take a specific form [93][94][95][96]. Fear appears to be an emotional response to imminent threats such as COVID-19 [97,98]. Bavel et al. [99] noted that fear might be a significant emotional issue caused by a pandemic. Negative emotions from the threat of an pandemic can be contagious and may relate people's feelings about others [92]. Excessive fear of COVID-19 can worsen stress, anxiety, and depression [82,100]. Negative psychological effects of public health measures have also included confusion and anger [39]. Therefore, the discrete emotional approach may be instructive in describing the psychological impact of the COVID-19 pandemic in comparison with general emotional well-being measures.
During an outbreak, community anxiety can increase following the first death, increasing media coverage and increasing the number of new deaths [60,95]. Continued exposure to COVID-19 in print, on video, and on social media can also increase anxiety and fear among people. Public health measures and their consequences (e.g., job loss, financial insecurity, and disruption of daily activities) are likely to have a negative impact on mental health [60,92]. Most studies on psychological consequences and interventions related to COVID-19 have focused on the risk factors for mental health problems [80][81][82][83][84][85][86]. Challenging stressors are desirable opportunities that lead to growth and development. Such factors, despite boring, cause positive emotions such as pride, passion, and excitement [76]. The negative impact and emotional responsibility have also been the main reasons for the risk of clinical and emotional problems related to the pandemic in Italy [94]. Fear of the pandemic, impatience, frustration, anger, and symptoms of post-traumatic stress and avoidant behaviours were found to be stressors in quarantine [39] and can affect patients with mental health problems [81] because emotional responses are part of the COVID-19 and the inappropriateness of some questions with the status quo in Iraq. This study was carried out in 2020 on the Iraqi Stock Exchange in the audit firm section. The respondents are faced with two parts; by answering the first part, we can figure out whether or not the factor is currently present in Iraqi audit firms considering the professional experience of the respondent. By answering the second part, the amount of significance (extremely high, high, medium, low, extremely low) will be expressed from the respondents' point of view. The opinion of opinion leaders assesses the validity of the questionnaire, and the reliability is examined using Cronbach's Alpha.

Population and Sample
The study's statistical population includes all auditors and financial managers of small-and medium-sized Iraqi firms during 2020. In this paper, the auditors and financial managers of small-and medium-sized Iraq firms are studied as the study sample. In other words, the sampling method in this paper is simple randomising, such that the sample members are selected randomly, and the questionnaires are distributed among them. The sampling method of the present study is based on a Cochran and Morgan Table and finally, 185 employed auditors in audit firms and 215 SME managers were selected as the sample of the study.
The study's statistical population included 402 auditors working in auditing firms and financial managers of small and medium enterprises in Iraq. A total of 198 were selected by the Cochran sampling method as the sample size. Due to the COVID-19 situation in Iraq, and because employees work remotely during the time of COVID-19 and the research population is large, direct access to members has been difficult. For this purpose, 260 questionnaires were distributed in hard copy, and also online questionnaires were considered. During the 45 days, 123 online questionnaires were completed, and 75 hard-copy questionnaires were received, for a total of 198 people who answered the questionnaire questions, so the participation rate is 76%.
In this study, PLS tests were used to investigate the effect of independent variables on the dependent variable.

Research Model and Variables
The measurement model is that part of the model that includes a variable and some related items. This paper has seven prevention models: COVID-19, panic from COVID-19, ethical principles, life expectancy, anxiety, depression, and stress. The model is presented in The variables used in the present study are the questions posed in the questionnaires. These questionnaires include emotional, spiritual, and organisational intelligence, social capital, and organisational performance that are classified in five points (Likert scale) from (1) strongly disagree to (5) strongly agree and are defined as follows: To assess the fear and panic caused by COVID-19 disease, the fear and panic questionnaires in different countries such as Bangladesh, Italy, Turkey, Russia and Belarus, Israel, Peru, and Paraguay have been used [64,65] It contains 25 questions and eight components  of regulating values, honesty, sympathy with others, responsibility, justice and fairness,  loyalty, superiority, and respecting others, that examine the professional ethics based on the  five-point Likert scale, with questions like, to what extent do you respect and implement  your beliefs in doing actions? Anxiety: in this paper, anxiety is an individual's score from the Spielberger State-Trait Anxiety Inventory (STAI).
In this paper, stress is the score an individual achieves from the Holmes-Rahe Stress Inventory. Figure 1. Life expectancy: by life expectancy, we mean the score the respondent obtains from the Miller life expectancy questionnaire that includes 48 questions, in a way that 48 is distressed and 240 is for maximum expectancy. Ethical principles: in this paper, professional ethics is the respondents' score from 25 questions of the professional ethics questionnaire. The questionnaire was designed in 2002 by Kadozir to assess professional ethics. It contains 25 questions and eight components of regulating values, honesty, sympathy with others, responsibility, justice and fairness, loyalty, superiority, and respecting others, that examine the professional ethics based on the five-point Likert scale, with questions like, to what extent do you respect and implement your beliefs in doing actions? Anxiety: in this paper, anxiety is an individual's score from the Spielberger State-Trait Anxiety Inventory (STAI).
In this paper, stress is the score an individual achieves from the Holmes-Rahe Stress Inventory.
The variables used in the present study are the questions that have been asked in the questionnaires of the present study. These questionnaires include the COVID Prevention and Panic Questionnaire based on ethics, life expectancy, anxiety, depression and stress, classified in five points (Likert scale) from strongly agree to strongly disagree.
To assess the quality of the measurement model, a study should examine the validity and reliability of different concepts and variables. Cronbach's alpha reliability is used in survey studies to assess the internal errors of indicators of a variable. The method is one of the most prevalent traditional techniques for examining internal homogeneity among indicators since it is assumed that all indicators of external loads enjoy the same load. The index for total reliability of the scale is a statistic named alpha, the interval of which is The variables used in the present study are the questions that have been asked in the questionnaires of the present study. These questionnaires include the COVID Prevention and Panic Questionnaire based on ethics, life expectancy, anxiety, depression and stress, classified in five points (Likert scale) from strongly agree to strongly disagree.
To assess the quality of the measurement model, a study should examine the validity and reliability of different concepts and variables. Cronbach's alpha reliability is used in survey studies to assess the internal errors of indicators of a variable. The method is one of the most prevalent traditional techniques for examining internal homogeneity among indicators since it is assumed that all indicators of external loads enjoy the same load. The index for total reliability of the scale is a statistic named alpha, the interval of which is between zero and one. The cutting point is 0.7. In other words, the higher the alpha coefficient, the higher the reliability scale would be. In this paper, Cronbach's alpha values for all structures enjoy an appropriate value.
The combined reliability value for prevention from COVID-19 is 0.805, panic from COVID-19 0.892, ethical principles 0.782, life expectancy 0.751, anxiety 0.903, depression 0.744, and stress 0.825. Table 1 shows the combined reliability of the research model variables. Convergent validity is the second criterion for fitting measurement models in the PLS method. The average variance extracted (AVE) criterion is indicative of the average shared variance between each structure and its indicators, and the higher the correlation, the higher the fitting. Fornell and Larcker introduce the AVE criterion to measure convergent validity and state that the critical value is 0.5, which means an AVE value higher than 0.5 shows an acceptable convergent validity [80]. The AVE value higher than 0.4 is sufficient, but for upcoming more accurate calculations, it is better to set the value at 0.5.
As shown in Figure 2, factor load values are favourable in the path coefficient analysis. All factor load values are more than 0.4 except for ethical principle structures and life expectancy with factor loads of 0.258 and 0.353, with no acceptable values.
The combined reliability value for prevention from COVID-19 is 0.805, panic from COVID-19 0.892, ethical principles 0.782, life expectancy 0.751, anxiety 0.903, depression 0.744, and stress 0.825. Table 1 shows the combined reliability of the research model variables. Convergent validity is the second criterion for fitting measurement models in the PLS method. The average variance extracted (AVE) criterion is indicative of the average shared variance between each structure and its indicators, and the higher the correlation, the higher the fitting. Fornell and Larcker introduce the AVE criterion to measure convergent validity and state that the critical value is 0.5, which means an AVE value higher than 0.5 shows an acceptable convergent validity [80]. The AVE value higher than 0.4 is sufficient, but for upcoming more accurate calculations, it is better to set the value at 0.5.
As shown in Figure   Divergent validity is a value that distinguishes a variable from another one in terms of experimental criteria. In other words, this type of credit is expected from the correlation of a variable to be more than that of other variables. According to the variance-based approach, there are two general criteria for evaluating divergent validity: The Fornell and Larcker method is the second scale for assessing divergent credit and compares the second root of AVE values with the correlation of other hidden variables. The rationale behind the method is based on the idea that a variable has more variance in common with its modifiers than other variables. The AVE scale calculates the average variance extracted from a variable. Table 2 shows the divergent validity matrix of the research model by the Fornell-Larker method. The main diameter of the matrix is the root of AVE values for stress (0.721), anxiety (0.708), depression (0.632), life expectancy (0.615), ethical principles (0.608), and panic of COVID-19 (0.604), and prevention from COVID-19 (0.588). As can be seen in Table 3, the value of root AVE of the stress structure (0.721) is higher than the correlation value of the six structures of anxiety, depression, life expectancy, ethical principles, panic from COVID-19, and prevention from COVID-19 (0.449, 0.671, 0.611, 0.276, 0.627, 0.469). That is the same for the structures of anxiety, depression, life expectancy, ethical principles, panic from COVID-19, and prevention from COVID-19. Hence, we can declare that the model's structures (hidden variables) interact more with their indicators than other structures in the present study. In other words, the divergent validity of the model is appropriate. According to the data analysis algorithm in the PLS method, we turn it into structural model fitting after analysing measurement model fittings. In contrast to measurement models, the structural model section is not about questions (explicit variables) and only examines the hidden variables and their relations.
For the analysis of t significance figures, the minimum acceptable value for the criterion is 1.96. When the t value for external weights of each item is more than 1.96, we can say that the external weights for the item of the measurement model in the structure are confirmed at a 95% confidence level. Figure 3 is the output of the conceptual framework along with t significance coefficients, and if the value is 1.96 for a path, the path is confirmed at a 95% confidence level. All indicators of seven conceptual models, except two ethical principles and life expectancy structures, are higher than 1.96 to explain the related structures appropriately.
All indicators of seven conceptual models, except two ethical principles and life expectancy structures, are higher than 1.96 to explain the related structures appropriately. The most important values used for estimating the structural model are the determination coefficient that indicates model prediction. The coefficient is achieved from the square of the relationship of endogenous variables with predictive variables. In other words, the path of the coefficient of determination within a model shows the value of the explained variance of the endogenous hidden variable obtained from the effect of an exogenous hidden variable on an endogenous one, so it calculates for endogenous hidden variables. The R 2 coefficient of determination is a criterion for linking the measurement section and the structural section of structural equation modelling and is indicative of the effect an exogenous variable has on an endogenous one, for which three values of 0.67, 0.33, and 0.19 are considered as the criterion values for weak, medium, and strong values. The R 2 value calculates only for endogenous (dependent) structures, zero for exogenous structures. Table 3 shows the values of R 2 .
According to Table 3, the coefficient of determination for life expectancy, anxiety, depression, and stress is medium. The value is weak for the variable of ethical principles, showing that the effect of prevention from COVID-19 and panic from COVID-19 on the endogenous variable is medium.
The severity of the prediction power of the model about endogenous structures has three values of 0.35, 0.15, and 0.02.
The outputs of the software for the redundancy index of data analysis show that the obtained values for the variables of stress, anxiety, and depression, with values of 0.206, 0.261, and 0.197, reveal a medium prediction power of the redundancy index and the variables of life expectancy and ethical principles have a weak prediction power. As for the panic structures from COVID-19 and prevention from COVID-19, the Q2 criterion value is zero since the structures are exogenous. We can claim that the study's conceptual framework enjoys an appropriate prediction power. The most important values used for estimating the structural model are the determination coefficient that indicates model prediction. The coefficient is achieved from the square of the relationship of endogenous variables with predictive variables. In other words, the path of the coefficient of determination within a model shows the value of the explained variance of the endogenous hidden variable obtained from the effect of an exogenous hidden variable on an endogenous one, so it calculates for endogenous hidden variables. The R 2 coefficient of determination is a criterion for linking the measurement section and the structural section of structural equation modelling and is indicative of the effect an exogenous variable has on an endogenous one, for which three values of 0.67, 0.33, and 0.19 are considered as the criterion values for weak, medium, and strong values. The R 2 value calculates only for endogenous (dependent) structures, zero for exogenous structures. Table 3 shows the values of R 2 .
According to Table 3, the coefficient of determination for life expectancy, anxiety, depression, and stress is medium. The value is weak for the variable of ethical principles, showing that the effect of prevention from COVID-19 and panic from COVID-19 on the endogenous variable is medium.
The severity of the prediction power of the model about endogenous structures has three values of 0.35, 0.15, and 0.02.
The outputs of the software for the redundancy index of data analysis show that the obtained values for the variables of stress, anxiety, and depression, with values of 0.206, 0.261, and 0.197, reveal a medium prediction power of the redundancy index and the variables of life expectancy and ethical principles have a weak prediction power. As for the panic structures from COVID-19 and prevention from COVID-19, the Q2 criterion value is zero since the structures are exogenous. We can claim that the study's conceptual framework enjoys an appropriate prediction power.
In the goodness of fit of the research model, the mean of commonality values is achieved from seven variables: stress, anxiety, depression, life expectancy, ethical principles, panic from COVID-19, and prevention from COVID-19. Communality = (0.520 + 0.574 + 0.500 + 0.502 + 0.528 + 0.541 + 0.514)/7 = 0.525 The R 2 value for the structures of ethical principles, life expectancy, anxiety, depression, and stress is equal to: So, the GOF value for the first model is: The GOF criteria is a value between 0 and 1, for which Wetzels defined three values of 0.36, 0.25, and 0.01 as weak, medium, and strong values, and the higher the value, we can say that the general fit of the model is at an appropriate level. According to the obtained value of 0.308 for the criterion, the medium fit of the model is confirmed, and the study can be continued.

Results
The respondents' information to the questionnaire, including gender, level of education, the field of study, age and level of professional experience, is presented in Table 4. The results show that 82.3% of the respondents are male, and the rest (17.7%) are female. A total of 62.6% of the respondents have a bachelor's degree, 19.7% have a master's degree, and 2% have a PhD. Table 4 shows that 10.6% of respondents are between 20 and 30 years old, and 8.1% are older than 50 years. In addition, 12.1% of respondents have less than five years of professional experience, 62.1% of the respondents have five to 10 years of professional experience, 8.1% of the respondents have 11 to 15 years of professional experience, and 3.5% of the respondents have more than 20 years of professional experience.

Hypotheses Testing
The variance-based structural equation modelling is used to assess the first hypothesis. The independent and dependent variables of the study were entered into the structural equation model in the form of hidden variables and first-order factor models. The estimations related to the evaluation indicators of the general evaluation of the structural equation model and the key parameters of the model (significance of association between variables) are displayed in the following figure and table: The analysis shows that the path coefficient between two prevention variables from COVID-19 and ethical principles is equal to 0.347 ( Figure 4). On the other hand, since the t value between these two variables is equal to 1.719 ( Figure 5), that is, calculated at a significant level of less than 5% (p = 0.086), we can claim that the hypothesis is not confirmed, so we can conclude that there is no significant relationship between prevention from COVID-19 and ethical principles. Table 5 shows the evaluation indicators of the internal model of the research, direction, and significance of direct effects.

Hypotheses Testing
The variance-based structural equation modelling is used to assess the first hypothesis. The independent and dependent variables of the study were entered into the structural equation model in the form of hidden variables and first-order factor models. The estimations related to the evaluation indicators of the general evaluation of the structural equation model and the key parameters of the model (significance of association between variables) are displayed in the following figure and table: The analysis shows that the path coefficient between two prevention variables from COVID-19 and ethical principles is equal to 0.347 ( Figure 4). On the other hand, since the t value between these two variables is equal to 1.719 ( Figure 5), that is, calculated at a significant level of less than 5% (p = 0.086), we can claim that the hypothesis is not confirmed, so we can conclude that there is no significant relationship between prevention from COVID-19 and ethical principles. Table 5 shows the evaluation indicators of the internal model of the research, direction, and significance of direct effects.   The variance-based structural equation modelling is used to assess the second hypothesis. The independent and dependent variables of the study were entered into the structural equation model in the form of hidden variables and first-order factor models. The estimations related to the evaluation indicators of the general evaluation of the structural equation model and the critical parameters of the model (significance of association between variables) are displayed in the following figure and table: The variance-based structural equation modelling is used to assess the first hypothesis. The independent and dependent variables of the study were entered into the structural equation model in the form of hidden variables and first-order factor models. The estimations related to the evaluation indicators of the general evaluation of the structural equation model and the key parameters of the model (significance of association between variables) are displayed in the following figure and table: The analysis shows that the path coefficient between two prevention variables from COVID-19 and ethical principles is equal to 0.347 ( Figure 4). On the other hand, since the t value between these two variables is equal to 1.719 ( Figure 5), that is, calculated at a significant level of less than 5% (p = 0.086), we can claim that the hypothesis is not confirmed, so we can conclude that there is no significant relationship between prevention from COVID-19 and ethical principles. Table 5 shows the evaluation indicators of the internal model of the research, direction, and significance of direct effects.   The variance-based structural equation modelling is used to assess the second hypothesis. The independent and dependent variables of the study were entered into the structural equation model in the form of hidden variables and first-order factor models. The estimations related to the evaluation indicators of the general evaluation of the structural equation model and the critical parameters of the model (significance of association between variables) are displayed in the following figure and table:  The variance-based structural equation modelling is used to assess the second hypothesis. The independent and dependent variables of the study were entered into the structural equation model in the form of hidden variables and first-order factor models. The estimations related to the evaluation indicators of the general evaluation of the structural equation model and the critical parameters of the model (significance of association between variables) are displayed in the following figure and table: According to Table 6, the analysis shows that the path coefficient between two prevention variables from COVID-19 and life expectancy equals 0.312 ( Figure 6). On the other hand, since the t value between these two variables is equal to 1.383 (Figure 7), that is, calculated at a significant level of less than 5% (p = 0.066), we can claim that the hypothesis is not confirmed, so we can conclude that there is no significant relationship between prevention from COVID-19 and life expectancy while according to research such as [105][106][107], life expectancy declined from 2019 to 2020. According to Table 6, the analysis shows that the path coefficient between two prevention variables from COVID-19 and life expectancy equals 0.312 ( Figure 6). On the other hand, since the t value between these two variables is equal to 1.383 (Figure 7), that is, calculated at a significant level of less than 5% (p = 0.066), we can claim that the hypothesis is not confirmed, so we can conclude that there is no significant relationship between prevention from COVID-19 and life expectancy while according to research such as [105][106][107], life expectancy declined from 2019 to 2020.   The variance-based structural equation modelling is used to assess the third hypothesis. The independent and dependent variables of the study were entered into the structural equation model in the form of hidden variables and first-order factor models. The estimations related to the evaluation indicators of the general evaluation of the structural equation model and the key parameters of the model (significance of association between variables) are displayed in the following figure and table: The analysis shown in Table 7 indicates that the path coefficient between two prevention variables from COVID-19 and anxiety is equal to 0.696 (Figure 8). On the other hand, since the t value between these two variables is equal to 14.514, that is, calculated at a significant level of more than 5% (p = 0.0), we can claim that the hypothesis is confirmed, so we can conclude that there is a significant relationship between prevention from COVID-19 and anxiety. This conclusion may be due to anxiety about infected with-COVID-19 [108].   According to Table 6, the analysis shows that the path coefficient between two prevention variables from COVID-19 and life expectancy equals 0.312 ( Figure 6). On the other hand, since the t value between these two variables is equal to 1.383 (Figure 7), that is, calculated at a significant level of less than 5% (p = 0.066), we can claim that the hypothesis is not confirmed, so we can conclude that there is no significant relationship between prevention from COVID-19 and life expectancy while according to research such as [105][106][107], life expectancy declined from 2019 to 2020.   The variance-based structural equation modelling is used to assess the third hypothesis. The independent and dependent variables of the study were entered into the structural equation model in the form of hidden variables and first-order factor models. The estimations related to the evaluation indicators of the general evaluation of the structural equation model and the key parameters of the model (significance of association between variables) are displayed in the following figure and table: The analysis shown in Table 7 indicates that the path coefficient between two prevention variables from COVID-19 and anxiety is equal to 0.696 (Figure 8). On the other hand, since the t value between these two variables is equal to 14.514, that is, calculated at a significant level of more than 5% (p = 0.0), we can claim that the hypothesis is confirmed, so we can conclude that there is a significant relationship between prevention from COVID-19 and anxiety. This conclusion may be due to anxiety about infected with-COVID-19 [108].  The analysis shown in Table 7 indicates that the path coefficient between two prevention variables from COVID-19 and anxiety is equal to 0.696 ( Figure 8). On the other hand, since the t value between these two variables is equal to 14.514, that is, calculated at a significant level of more than 5% (p = 0.0), we can claim that the hypothesis is confirmed, so we can conclude that there is a significant relationship between prevention from COVID-19 and anxiety. This conclusion may be due to anxiety about infected withCOVID-19 [108]. According to Table 6, the analysis shows that the path coefficient between two prevention variables from COVID-19 and life expectancy equals 0.312 ( Figure 6). On the other hand, since the t value between these two variables is equal to 1.383 (Figure 7), that is, calculated at a significant level of less than 5% (p = 0.066), we can claim that the hypothesis is not confirmed, so we can conclude that there is no significant relationship between prevention from COVID-19 and life expectancy while according to research such as [105][106][107], life expectancy declined from 2019 to 2020.   The variance-based structural equation modelling is used to assess the third hypothesis. The independent and dependent variables of the study were entered into the structural equation model in the form of hidden variables and first-order factor models. The estimations related to the evaluation indicators of the general evaluation of the structural equation model and the key parameters of the model (significance of association between variables) are displayed in the following figure and table: The analysis shown in Table 7 indicates that the path coefficient between two prevention variables from COVID-19 and anxiety is equal to 0.696 (Figure 8). On the other hand, since the t value between these two variables is equal to 14.514, that is, calculated at a significant level of more than 5% (p = 0.0), we can claim that the hypothesis is confirmed, so we can conclude that there is a significant relationship between prevention from COVID-19 and anxiety. This conclusion may be due to anxiety about infected with-COVID-19 [108].  The variance-based structural equation modelling is used to assess the fourth hypothesis. The independent and dependent variables of the study were entered into the structural equation model in the form of hidden variables and first-order factor models. The estimations related to the evaluation indicators of the general evaluation of the structural equation model and the key parameters of the model (significance of association between variables) are displayed in Figure 9. The variance-based structural equation modelling is used to assess the fourth hypothesis. The independent and dependent variables of the study were entered into the structural equation model in the form of hidden variables and first-order factor models. The estimations related to the evaluation indicators of the general evaluation of the structural equation model and the key parameters of the model (significance of association between variables) are displayed in Figure 9. According to Table 8, the analysis shows that the path coefficient between two prevention variables from COVID-19 and depression equals 0.705 ( Figure 10). On the other hand, since the t value between these two variables is equal to 14.149 (Figure 11), that is, calculated at a significant level of more than 5% (p = 0.0), we can claim that the hypothesis is confirmed, so we can conclude that there is a significant relationship between prevention from COVID-19 and depression.   According to Table 8, the analysis shows that the path coefficient between two prevention variables from COVID-19 and depression equals 0.705 ( Figure 10). On the other hand, since the t value between these two variables is equal to 14.149 (Figure 11), that is, calculated at a significant level of more than 5% (p = 0.0), we can claim that the hypothesis is confirmed, so we can conclude that there is a significant relationship between prevention from COVID-19 and depression.  The variance-based structural equation modelling is used to assess the fourth hypothesis. The independent and dependent variables of the study were entered into the structural equation model in the form of hidden variables and first-order factor models. The estimations related to the evaluation indicators of the general evaluation of the structural equation model and the key parameters of the model (significance of association between variables) are displayed in Figure 9. According to Table 8, the analysis shows that the path coefficient between two prevention variables from COVID-19 and depression equals 0.705 ( Figure 10). On the other hand, since the t value between these two variables is equal to 14.149 (Figure 11), that is, calculated at a significant level of more than 5% (p = 0.0), we can claim that the hypothesis is confirmed, so we can conclude that there is a significant relationship between prevention from COVID-19 and depression.     The variance-based structural equation modelling is used to assess the fourth hypothesis. The independent and dependent variables of the study were entered into the structural equation model in the form of hidden variables and first-order factor models. The estimations related to the evaluation indicators of the general evaluation of the structural equation model and the key parameters of the model (significance of association between variables) are displayed in Figure 9. According to Table 8, the analysis shows that the path coefficient between two prevention variables from COVID-19 and depression equals 0.705 ( Figure 10). On the other hand, since the t value between these two variables is equal to 14.149 (Figure 11), that is, calculated at a significant level of more than 5% (p = 0.0), we can claim that the hypothesis is confirmed, so we can conclude that there is a significant relationship between prevention from COVID-19 and depression.   According to Table 9, the analysis shows that the path coefficient between two prevention variables from COVID-19 and stress equals 0.630 ( Figure 12). On the other hand, since the t value between these two variables is equal to 6.656, (Figure 13) that is, calculated at a significant level of more than 5% (p = 0.0), we can claim that the hypothesis is confirmed, so we can conclude that there is a significant relationship between prevention from COVID-19 and stress.  The variance-based structural equation modelling is used to assess the fifth hypothesis. The independent and dependent variables of the study were entered into the structural equation model in the form of hidden variables and first-order factor models. The estimations related to the evaluation indicators of the general evaluation of the structural equation model and the key parameters of the model (significance of association between variables) are displayed in the following figure and table: According to Table 9, the analysis shows that the path coefficient between two prevention variables from COVID-19 and stress equals 0.630 (Figure 12). On the other hand, since the t value between these two variables is equal to 6.656, (Figure 13) that is, calculated at a significant level of more than 5% (p = 0.0), we can claim that the hypothesis is confirmed, so we can conclude that there is a significant relationship between prevention from COVID-19 and stress.   The variance-based structural equation modelling is used to assess the sixth hypothesis. The independent and dependent variables of the study were entered into the structural equation model in the form of hidden variables and first-order factor models. The estimations related to the evaluation indicators of the general evaluation of the structural equation model and the key parameters of the model (significance of association between variables) are displayed in the following figure and table: According to Table 10, the analysis shows that the path coefficient between two prevention variables from COVID-19 and ethical principles equals 0.641 (Figures 14 and 15). On the other hand, since the t value between these two variables is equal to 14.120, that is, calculated at a significant level of more than 5% (p = 0.0), we can claim that the hypothesis  The variance-based structural equation modelling is used to assess the fifth hypothesis. The independent and dependent variables of the study were entered into the structural equation model in the form of hidden variables and first-order factor models. The estimations related to the evaluation indicators of the general evaluation of the structural equation model and the key parameters of the model (significance of association between variables) are displayed in the following figure and table: According to Table 9, the analysis shows that the path coefficient between two prevention variables from COVID-19 and stress equals 0.630 (Figure 12). On the other hand, since the t value between these two variables is equal to 6.656, (Figure 13) that is, calculated at a significant level of more than 5% (p = 0.0), we can claim that the hypothesis is confirmed, so we can conclude that there is a significant relationship between prevention from COVID-19 and stress.   The variance-based structural equation modelling is used to assess the sixth hypothesis. The independent and dependent variables of the study were entered into the structural equation model in the form of hidden variables and first-order factor models. The estimations related to the evaluation indicators of the general evaluation of the structural equation model and the key parameters of the model (significance of association between variables) are displayed in the following figure and table: According to Table 10, the analysis shows that the path coefficient between two prevention variables from COVID-19 and ethical principles equals 0.641 (Figures 14 and 15). On the other hand, since the t value between these two variables is equal to 14.120, that is, calculated at a significant level of more than 5% (p = 0.0), we can claim that the hypothesis According to Table 10, the analysis shows that the path coefficient between two prevention variables from COVID-19 and ethical principles equals 0.641 (Figures 14 and 15). On the other hand, since the t value between these two variables is equal to 14.120, that is, calculated at a significant level of more than 5% (p = 0.0), we can claim that the hypothesis is confirmed, so we can conclude that there is a significant relationship between the panic of COVID-19 and ethical principles.

Significance Level
Panic of COVID-19-Ethical principles 0.641 14.120 0.000 Confirmed is confirmed, so we can conclude that there is a significant relationship between the panic of COVID-19 and ethical principles.   The variance-based structural equation modelling is used to assess the seventh hypothesis. The independent and dependent variables of the study were entered into the structural equation model in the form of hidden variables and first-order factor models. The estimations related to the evaluation indicators of the general evaluation of the structural equation model and the key parameters of the model (significance of association between variables) are displayed in the following figure and table: According to Table 11, the analysis shows that the path coefficient between two prevention variables from COVID-19 and life expectancy equals 0.415 ( Figure 16). On the other hand, since the t value between these two variables is equal to 6.757, (Figure 17) that is, calculated at a significant level of more than 5% (p = 0.0), we can claim that the hypothesis is confirmed, so we can conclude that there is a significant relationship between the panic of COVID-19 and life expectancy.  is confirmed, so we can conclude that there is a significant relationship between the panic of COVID-19 and ethical principles.   The variance-based structural equation modelling is used to assess the seventh hypothesis. The independent and dependent variables of the study were entered into the structural equation model in the form of hidden variables and first-order factor models. The estimations related to the evaluation indicators of the general evaluation of the structural equation model and the key parameters of the model (significance of association between variables) are displayed in the following figure and table: According to Table 11, the analysis shows that the path coefficient between two prevention variables from COVID-19 and life expectancy equals 0.415 ( Figure 16). On the other hand, since the t value between these two variables is equal to 6.757, (Figure 17) that is, calculated at a significant level of more than 5% (p = 0.0), we can claim that the hypothesis is confirmed, so we can conclude that there is a significant relationship between the panic of COVID-19 and life expectancy.  The variance-based structural equation modelling is used to assess the seventh hypothesis. The independent and dependent variables of the study were entered into the structural equation model in the form of hidden variables and first-order factor models. The estimations related to the evaluation indicators of the general evaluation of the structural equation model and the key parameters of the model (significance of association between variables) are displayed in the following figure and table: According to Table 11, the analysis shows that the path coefficient between two prevention variables from COVID-19 and life expectancy equals 0.415 ( Figure 16). On the other hand, since the t value between these two variables is equal to 6.757, (Figure 17) that is, calculated at a significant level of more than 5% (p = 0.0), we can claim that the hypothesis is confirmed, so we can conclude that there is a significant relationship between the panic of COVID-19 and life expectancy. is confirmed, so we can conclude that there is a significant relationship between the panic of COVID-19 and ethical principles.   The variance-based structural equation modelling is used to assess the seventh hypothesis. The independent and dependent variables of the study were entered into the structural equation model in the form of hidden variables and first-order factor models. The estimations related to the evaluation indicators of the general evaluation of the structural equation model and the key parameters of the model (significance of association between variables) are displayed in the following figure and table: According to Table 11, the analysis shows that the path coefficient between two prevention variables from COVID-19 and life expectancy equals 0.415 ( Figure 16). On the other hand, since the t value between these two variables is equal to 6.757, (Figure 17) that is, calculated at a significant level of more than 5% (p = 0.0), we can claim that the hypothesis is confirmed, so we can conclude that there is a significant relationship between the panic of COVID-19 and life expectancy.    The independent and dependent variables of the study were entered into the structural equation model in the form of hidden variables and first-order factor models. The estimations related to the evaluation indicators of the general evaluation of the structural equation model and the key parameters of the model (significance of association between variables) are displayed in the following figure and table: Table 12 shows that the path coefficient between two variables of prevention from COVID-19 and anxiety equals 0.585 ( Figure 18). On the other hand, since the t value between these two variables is equal to 10.297, (Figure 19) that is, calculated at a significant level of more than 5% (p = 0.0), we can claim that the hypothesis is confirmed, so we can conclude that there is a significant relationship between the panic of COVID-19 and anxiety. Studies showed that anxiety symptoms were much higher than before the pandemic [109].   The independent and dependent variables of the study were entered into the structural equation model in the form of hidden variables and first-order factor models. The estimations related to the evaluation indicators of the general evaluation of the structural equation model and the key parameters of the model (significance of association between variables) are displayed in the following figure and table: Table 12 shows that the path coefficient between two variables of prevention from COVID-19 and anxiety equals 0.585 ( Figure 18). On the other hand, since the t value between these two variables is equal to 10.297, (Figure 19) that is, calculated at a significant level of more than 5% (p = 0.0), we can claim that the hypothesis is confirmed, so we can conclude that there is a significant relationship between the panic of COVID-19 and anxiety. Studies showed that anxiety symptoms were much higher than before the pandemic [109].   The variance-based structural equation modelling is used to assess the ninth hypothesis. The independent and dependent variables of the study were entered into the structural equation model in the form of hidden variables and first-order factor models. The estimations related to the evaluation indicators of the general evaluation of the structural   The independent and dependent variables of the study were entered into the structural equation model in the form of hidden variables and first-order factor models. The estimations related to the evaluation indicators of the general evaluation of the structural equation model and the key parameters of the model (significance of association between variables) are displayed in the following figure and table: Table 12 shows that the path coefficient between two variables of prevention from COVID-19 and anxiety equals 0.585 ( Figure 18). On the other hand, since the t value between these two variables is equal to 10.297, (Figure 19) that is, calculated at a significant level of more than 5% (p = 0.0), we can claim that the hypothesis is confirmed, so we can conclude that there is a significant relationship between the panic of COVID-19 and anxiety. Studies showed that anxiety symptoms were much higher than before the pandemic [109].   The variance-based structural equation modelling is used to assess the ninth hypothesis. The independent and dependent variables of the study were entered into the structural equation model in the form of hidden variables and first-order factor models. The estimations related to the evaluation indicators of the general evaluation of the structural  Table 13 shows that the path coefficient between two prevention variables from COVID-19 and depression equals 0.654 ( Figure 20). On the other hand, since the t value between these two variables is equal to 11.986, (Figure 21) that is, calculated at a significant level of more than 5% (p = 0.0), we can claim that the hypothesis is confirmed, so we can conclude that there is a significant relationship between the panic of COVID-19 and depression. Studies showed symptoms of depression were at a much higher level than prior to the pandemic [108].  Table 13 shows that the path coefficient between two prevention variables from COVID-19 and depression equals 0.654 ( Figure 20). On the other hand, since the t value between these two variables is equal to 11.986, (Figure 21) that is, calculated at a significant level of more than 5% (p = 0.0), we can claim that the hypothesis is confirmed, so we can conclude that there is a significant relationship between the panic of COVID-19 and depression. Studies showed symptoms of depression were at a much higher level than prior to the pandemic [108].   The variance-based structural equation modelling is used to assess the tenth hypothesis. The independent and dependent variables of the study were entered into the structural equation model in the form of hidden variables and first-order factor models. The estimations related to the evaluation indicators of the general evaluation of the structural equation model and the key parameters of the model (significance of association between variables) are displayed in the following figure and table:

Path Coefficient Significance
According to Table 14, the analysis shows that the path coefficient between two prevention variables from COVID-19 and stress equals 0.682 ( Figure 22). On the other hand, since the t value between these two variables is equal to 14.416, (Figure 23) that is, calculated at a significant level of more than 5% (p = 0.0), we can claim that the hypothesis is confirmed, so we can conclude that there is a significant relationship between the panic of COVID-19 and stress.  COVID-19 and depression equals 0.654 ( Figure 20). On the other hand, since the t value between these two variables is equal to 11.986, (Figure 21) that is, calculated at a significant level of more than 5% (p = 0.0), we can claim that the hypothesis is confirmed, so we can conclude that there is a significant relationship between the panic of COVID-19 and depression. Studies showed symptoms of depression were at a much higher level than prior to the pandemic [108].   The variance-based structural equation modelling is used to assess the tenth hypothesis. The independent and dependent variables of the study were entered into the structural equation model in the form of hidden variables and first-order factor models. The estimations related to the evaluation indicators of the general evaluation of the structural equation model and the key parameters of the model (significance of association between variables) are displayed in the following figure and table: According to Table 14, the analysis shows that the path coefficient between two prevention variables from COVID-19 and stress equals 0.682 ( Figure 22). On the other hand, since the t value between these two variables is equal to 14.416, (Figure 23) that is, calculated at a significant level of more than 5% (p = 0.0), we can claim that the hypothesis is confirmed, so we can conclude that there is a significant relationship between the panic of COVID-19 and stress.   table: According to Table 14, the analysis shows that the path coefficient between two prevention variables from COVID-19 and stress equals 0.682 ( Figure 22). On the other hand, since the t value between these two variables is equal to 14.416, (Figure 23) that is, calculated at a significant level of more than 5% (p = 0.0), we can claim that the hypothesis is confirmed, so we can conclude that there is a significant relationship between the panic of COVID-19 and stress.  Figure 20). On the other hand, since the t value between these two variables is equal to 11.986, (Figure 21) that is, calculated at a significant level of more than 5% (p = 0.0), we can claim that the hypothesis is confirmed, so we can conclude that there is a significant relationship between the panic of COVID-19 and depression. Studies showed symptoms of depression were at a much higher level than prior to the pandemic [108].   The variance-based structural equation modelling is used to assess the tenth hypothesis. The independent and dependent variables of the study were entered into the structural equation model in the form of hidden variables and first-order factor models. The estimations related to the evaluation indicators of the general evaluation of the structural equation model and the key parameters of the model (significance of association between variables) are displayed in the following figure and table: According to Table 14, the analysis shows that the path coefficient between two prevention variables from COVID-19 and stress equals 0.682 ( Figure 22). On the other hand, since the t value between these two variables is equal to 14.416, (Figure 23) that is, calculated at a significant level of more than 5% (p = 0.0), we can claim that the hypothesis is confirmed, so we can conclude that there is a significant relationship between the panic of COVID-19 and stress.

Discussion and Conclusions
The present study assesses the relationship between the prevention and panic from COVID-19, ethical principles, life expectancy, anxiety, depression, and stress in auditors and financial managers of small-and medium-sized firms. The results of hypothesis testing show no significant relationship between prevention from COVID-19 and ethical principles and life expectancy. However, there is a positive and significant relationship between prevention from COVID-19 and anxiety, depression, and stress, and between panic from COVID-19 and ethical principles, life expectancy, anxiety, depression, and stress. The higher the panic from COVID-19, the higher the ethical principles, life expectancy, anxiety, depression, and stress. Participants in the study show a high level literature on stress, anxiety, and depression [93]. That shows a considerable proportion of psychological health issues among students is lockdown due to COVID-19, which appears with mild to severe signs of anxiety, stress, and depression in the initial stages of the pandemic [74].
The results of the study on the hypothesis of panic from COVID-19, stress, anxiety, and depression are in line with that of Huang and Zhao [74] and Sandín et al. [103], who declare that there is a positive and significant relationship between panic from COVID-19, stress, anxiety, and depression.
The obtained results propose novel knowledge since, in most of the topical literature, the levels of the studies have been about students and health workers instead of auditors and managers of small-and medium-sized firms [95]. Moreover, by analysing the topical literature, we conclude that the association discovered between the panic of COVID-19 and depression conforms with some studies, including Alyami et al. [77] and Tzur et al. [83]. Moreover, the associations between panic from COVID-19 and anxiety, and anxiety and depression, are also in line with Mertens [104][105][106][107]109].
While variations across countries will exist in responding to the COVID-19 pandemic, the human rights of individuals with mental health disorders must be protected, and appropriate and safe services provided for their treatment. Moreover, the negative impact of this pandemic on government budgets should not be used as an excuse to reduce essential services for people with mental illness during or after the pandemic [110]. However, the condition in Iraq and the way people deal with the pandemic are very unfortunate. The rapid spread of the disease, a huge number of afflicted people, death toll rise, distrust of the health system, unawareness, and false information may aid the fact that auditors and managers of Iraqi SME firms are afraid of the situation. Panic is determined as a factor that affects depression, and this factor, along with anxiety a reduced life expectancy, can exacerbate the ethical principles. This study's conclusion, related to ethical principles, life expectancy, anxiety, depression, and stress during COVID-19, can be used to reference other researchers developing ideas in response to patterns.
The COVID-19 pandemic has been a global economy and a health shock. Policy makers have had to balance strict public health measures to slow the spread of the virus against the adverse health, educational, and economic consequences of these choices. Attention has, therefore, rightly focused on the immediate mental health consequences of the pandemic, both for the general population and for people with mental illness [111]. According to this argument, the auditors and managers of SME firms, when they feel that they are more vulnerable and exposed to more risk, feel more unease since most of them have to leave their homes to continue with their living and be exposed to unfavourable conditions, so protecting their health is not an easy task. This study offers a timely and relevant contribution to the academic research about prevention and panic from COVID-19, ethical principles, life expectancy, anxiety, depression, and stress.
This study is subject to certain limitations. For example, access to research samples was difficult due to the prevalence of COVID-19 disease. It was also impossible to control other factors that affect auditors and financial managers' anxiety, life expectancy, depression and stress. Furthermore, in this study, life expectancy is attributable to COVID-19, not total life expectancy.