COVID-19 and stress of Indian youth: An association with background, on-line mode of teaching, resilience and hope

Background COVID-19 pandemic causes serious threats to physical health and triggers wide varieties of psychological problems, including anxiety and depression. Youth exhibit a greater risk of developing psychological distress, especially during epidemics influencing their wellbeing. Objectives To identify the relevant dimensions of psychological stress, mental health, hope and resilience and to examine the prevalence of stress in Indian youth and its relationship with socio-demographic information, online-mode of teaching, hope and resilience. Method A cross-sectional online survey obtained information on socio-demographic background, online-mode of teaching, psychological stress, hope and resilience from the Indian youth. A Factor Analysis is also conducted on the recompenses of the Indian youth on psychological stress, mental health, hope and resilience separately to identify the major factors associated with parameters. The sample size in this study was 317, which is more than the required sample size (Tabachnik et al., 2001). Results About 87% of the Indian youth perceived moderate to a high levels of psychological stress during the current COVID-19 pandemic. Different demographic, sociographic and psychographic segments were found to have high stress levels due to the pandemic, while psychological stress was found to be negatively correlated with resilience as well as hope. The findings identified significant dimensions of the stress caused by the pandemic and also identified the dimensions of mental health, resilience and hope among the study subjects. Conclusion As stress has a long-term impact on human psychology and can disrupt the lives of people and as the findings suggest that the young population of the country have faced the greatest amount of stress during the pandemic, a greater need for mental health support is required to the young population, especially in post pandemic situations. The integration of online counselling and stress management programs could assist in mitigating the stress of youth involved in distance learning.


Introduction
Viral pneumonia among patients in Wuhan, China, that appeared in late December 2019, which was temporarily named a new coronavirus (2019-nCOV) and subsequently renamed COVID-19, was declared a global pandemic by the World Health Organization in March 2020 (Zhou et al., 2020). Like any disease outbreaks in the past, COVID-19 pandemic causes serious threats to physical health and triggers wide varieties of psychological problems, including anxiety and depression (Qiu et al., 2020). Psychological distress and depressive symptoms are generally observed in the face of disease(s) (Duggal et al., 2016), with a differential level of psychological distress revealed among patients who experience COVID-19 infection, those under quarantine, and in the general public . Globally, with more than 200 countries and territories reporting confirmed cases of COVID-19, India is recorded to possess the third highest number of COVID-19 cases (Worldometer, 2020), and this severe outbreak has affected the mental health of Indians (Verma and Mishra, 2020) as is the case everywhere. Again, a study on the psychological impact of COVID-19 during lockdown among Indians revealed nearly three-fourth (74.1%) perceived moderate levels of stress and about one-fourth reported feelings of pessimism and hopelessness (Grover et al., 2020).
Various factors, including gender, marital status, social support, specific experience with COVID-19 infection, and length of isolation, have contributed to the vulnerability to psychological distress across populations during the COVID-19 pandemic (Brooks et al., 2020;Islam and Mamun, 2020). Studies have shown that females are at greater risk of developing depression and anxiety (Qiu et al., 2020;Zhou et al., 2020). Poor mental health is also identified to have a significant correlation with lower income and younger age (especially between 16 and 24 years), who exhibited a greater risk of developing psychological distress Taylor et al., 2008). Youth, especially students, are severely anxious during the COVID-19 outbreak (Dangi and George, 2020).
Stress, anxiety, and depression positively correlate with each other are found to negatively influence the wellbeing of a person (Grover et al., 2020). Therefore, mental health conditions are crucial for a good quality of life and wellbeing (Zhou et al., 2020). A Bangladesh-based study during COVID-19 revealed that disruption in the scheduled study plan and future career and financial difficulties predominantly caused stress (Islam and Mamun, 2020). With the closure of schools and colleges, the accessibility to resources, including teaching and learning, is greatly affected, and these predicaments provoke stress (Lee, 2020) and impact hope and resilience. Studies have shown that the online method of teaching and learning is not as good as interactive classroom lectures, which are negatively affected by multiple factors, including poor internet connectivity, economic conditions of the institute and students, less interaction and sometimes poor attitude of students and faculty members (Keskin and Yurdugül, 2019;Thongsri et al., 2019).
Hope and resilience are routinely described as important to coping with adversity and are predictors of psychological flourishing (Munoz et al., 2020). Possessing hope and resilience positively affects mood and functioning and is linked with better physical and mental health (Duggal et al., 2016). High resilience helps individuals cope positively with uncertainty, conflict, and failure (Avey et al., 2008). The ability to cope well with adverse events allows resilient individuals to adapt to significant life changes and consequently be better by becoming stronger, wiser, and more powerful (Duggal et al., 2016). However, to our best knowledge, there needs to be more paucity of how hope and resilience, especially during the COVID-19 pandemic, influence youth psychological stress.
Considering the detrimental effect the COVID-19 pandemic can have on mental health, this study was conducted to assess the prevalence of psychological stress among Indian youth and its relationship with the socio-demographic variables, online mode of classes and its efficacy, hope and resilience of the participants. The main motivation of the researchers for conducting this study was the lack of information about the impact of prolonged lockdown and continuous attendance of online classes on the mental health of Indian youth. Besides, the study attempted to identify relevant dimensions of stress, resilience and hope. Findings of this study would inform academic administrators, public health policymakers and healthcare professionals, particularly mental healthcare providers and psychologists, to attend to the urgent mental health needs of the Indian youth. Identification of the relevant factors of stress, resilience and hope will also contribute to further exploration in the field of psychology.

Study design
The data for this study was obtained using a cross-sectional online survey from Indian youth during the different phases of COVID-19.

Study population
A group of 317 Indian youth aged between 16 to 29 years participated in the online survey.

Study Tools
The study tools which were used for data collection from the Indian Youth are as follows:

Background Information Schedule (BIS)
This schedule is developed to gather information about the background of the youth. There are two broad sections in the BIS. The first section comprises 15 questions. Nine questions are related to demographic and socio-economic background, including one on the family environment, while the remaining six questions are related to personal feelings because of the Covid-19 outbreak. Section II has five questions related to online classes.

The Perceived Stress Scale (PSS)
The Perceived Stress Scale of Sheldon Cohen et al. (1983) is the most widely used psychological instrument to measure stress and this scale is adopted for the present study. It is a measure of the degree to which situations in one's life are appraised as stressful. Items were designed to tap into respondents' perceptions about how unpredictable, uncontrollable, and overloaded their lives are. The scale also includes a number of direct queries about levels of experienced stress. The questions in the PSS ask about feelings and thoughts during the pandemic phases. Respondents are asked how often they felt a certain way in each case. Psychometric properties of PSS have been ascertained by a number of previous studies (Lengacher et al., 2011;Mimura and Griffiths, 2008;Sellers et al., 2003). The response was collected by using a five-point scale that captures responses ranging from never (0) to very often (4). A high score indicates high stress. Reliability analysis of the PSS, using Cronbach alpha, was done for the present study subjects and it was found to be 0.772.
A factor analysis was conducted to identify the dimensions of the PSS and suitable dimensions were identified.

Adult Hope Scale (AHS)
Measuring the amount of hope and identification of the dimensions of hope is another objective of the study, and the Adult Hope Scale of Snyder et al. (2007) was adopted for the purpose. A five-point scale was used for the collection of data. The Cronbach's alpha of AHS was ascertained by another Indian Study, and it was 0.75 (Dar, 2020). The Cronbach's alpha of the present sample was 0.707.

Mental Health Inventory
Mental Health Inventory items, designed by Viet and Ware (1983) was adopted to identify the dimensions of mental health and suitable dimensions were identified. Smith et al. (2008): There are 6 items in BRS, of these 6 items, 3 are reverse scored. The BRS has a five-point Likert scale that ranges from 1 (strongly disagree) to 5 (strongly agree). The responses of the items are added/summed for all six items that gives a total score ranging from 6-30 which is divided by the total number of items answered that gives the resilience score. The higher the total means score on BRS the greater will be the resilience. The Chronbach alpha of the BRS with the present sample was 0.626. Although the resilience scale score was slightly low, it was regarded as suitable in this social science research (DeVellis, 1991;Mohamad et al., 2015).

Brief Resilience Scale (BRS)
Identification of the dimensions of resilience was also done through a factor analysis and suitable dimensions were identified.

Adult hope scale
Identification of dimensions of Hope was another important aspect of the study and such was done by collecting data from the respondents by adopting the Adult Hope Scale designed by Snyder et al. (1991).

Procedure for data collection
An online survey was conducted using a non-probability convenient sampling method. Indian youth (n=317) aged between 16-29 years from different parts of the country participated in the study through online mode as per their convenience.

Ethical issues
The present study was subjected to ethical approval and obtained the clearance (Ref.No.RGNIYD/ADMIN/20-21/SEC/001). All procedures performed in collecting data from the participants were per the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration. Participation in the study was voluntary and participants were ensured about the confidentiality of information.

Data analysis
Statistical analysis was performed using the Statistical Package for the Social Sciences (SPSS) version 22.0 software. Both descriptive and inferential statistics were employed to assess the relationship between the variables. Four different factor analysis was conducted on the data received from PSS, Mental Health Inventory Items, and the Brief Resilience Scale items to identify their dimensions. Table 1, the sample included both male and female almost in equal proportion except for one participant (0.3%) who identified as the third gender. Nearly half (49.8%) of the participants were in the age range of 19-22 years and more than half (61.9%) were postgraduate and above qualification. About 56.5% of the father's education and 65.3% of the mother's education were reported less than or equal to grade 12 level of education. About three quarters (74.8%) of the participants admitted single-family type with nearly half (47.9%) reported family income less than 25000 per month. A little over three fourth (77.9%) confirmed living in a congenial family environment. Table 1 also demonstrates the relationship between background information and their relationship with psychological stress. Psychological stress scores were significantly higher among the female gender (p<0.01), the ones with mother's higher level of education (p<0.05), single children (p<0.05) and those from non-congenial family environments (p<0.001). A significant higher psychological stress score was found among participants who felt restless due to lockdown (p<0.001), those who worried about exam and future career (p<0.001), and those who felt like consulting a psychologist or a counsellor for mental health support (p<0.001). Furthermore, participants who admitted having no one to share personal feelings or emotions significantly had high scores on psychological stress (p<0.001).

As shown in
A little over three quarters (77.3%) attended an online class during the lockdown. Nearly 62.4% of the participants shared the opinion that the online mode of classes during lockdown is a good option. About a quarter (25.8%) reported having faced a problem with internet connectivity all the time (Table 2). Table 2 also illustrates the relationship between online teaching, efficacy, and psychological stress. Participants who did not find the online mode of classes as a good option during lockdown (p<0.01) and not able to clarify queries through online mode of classes (p<0.001) significantly reported higher psychological stress. In addition, while attending online classes, those participants who faced internet connectivity problems were significantly associated with psychological stress (p<0.05). Tukey Post hoc test revealed participants who faced internet connectivity problems at all time significantly reported higher psychological stress compared to those who reported the problems sometimes. More than half of the youth (52.4%) stated that on-line classes should not be conducted for more than an hour at a stretch while 27.2% stated it could be continued for two hours.
A relationship between psychological stress, resilience and hope was also assessed for the participants of this study. All the items under perceived stress were rated at least 'sometimes' to 'very often' by not less than 50%. About 87% of the participants perceived moderate to a high level of psychological stress during this current COVID-19 pandemic. A strong negative correlation was found between psychological stress and resilience (p<0.001) and as well as between psychological stress and hope (p<0.001). Compared to participants who rated moderate to a high level of perceived stress, higher resilience (M=21.78±3.86) and great hope score (M=55.37±6.21) were shown by those who had low perceived stress (Table 3).

Findings related to the dimensions of stress elements
To identify the dimensions related to stress, a factor analysis was conducted on the data collected on variables of Cohen's Perceived Stress Scale (PSS). The findings are as follows:

Findings of KMO and Bartlett's test of sphericity
Finding out the sample adequacy and the existence of relationship among the variables are the primary requisites of factor analysis. To test the adequacy of the samples for each variable and for the complete model, Kaiser-Meyer-Olkin test (KMO test) was conducted. The statistic measures the proportion of variance among the variables in the model. A lower proportion of variance signifies the suitability of data for factor analysis. The value of KMO lies between 0 and 1. If the value of the KMO test is > 0.6, we can continue with the factor analysis. Here in this case, the findings show that the value of KMO is 0.836 > 0.6 (Table 4). This means that the samples are adequate for conducting factor analysis.
Bartlett's test of sphericity is conducted to find out whether the correlation matrix of the identified variables is an identity matrix. The Null Hypothesis states that the correlation matrix of the variables in the model is an identity matrix. If the correlation matrix turns out to be an identity matrix, then the variables are unrelated and thus they are not ideal for conducting factor analysis. If the p value of Bartlett's test of sphericity is less than 0.05, the correlation matrix is not an identity matrix, and the null hypothesis is rejected. Thus p-value < 0.05 allows us to continue with the factor analysis. Here in this case, the p value of Bartlett's test of sphericity is 0.000 < 0.05. This means that the correlation matrix is not an identity matrix and the null hypothesis is rejected and we can carry on with the factor analysis.

Findings related to communality
In Factor Analysis, communality explains the amount of variance of each variable explained by the factors. The Variable's communality ranges from 0 to 1. If a variable does not have any unique variance, i.e., 100% of its variance is explained, it has a communality of 1. If the variable's variance is completely unexplained by the factors, the communality will be 0. According to Child (2006), if the score of the extraction value is < 0.2, the item should be removed from the factor analysis. Here, in this case, none of the items is found to have an extraction value < 0.2, and thus, none of the items was dropped from the model (Table 5). Besides this, the extraction value associated with the item, 'In the last month, how rarely have you felt difficulties piling up so high that you could not overcome them?' is 0.702, which indicates that the factors have explained 70.2% of the variability of this item. This is also important to mention that this is the highest variance of an item explained by a factor in this factor analysis. On the other hand, 0.44 is the extraction value associated with the item, "In the last month, how often have you been able to control irritations in your life?". This means that the factor has explained 44% variability of this item. This is the lowest variance which is explained by the factor, in this factor analysis.

Findings related to total variance explained
The 'Total Variance Explained' table shows the number of factors extracted from the Factor analysis and the amount of variance explained by the factors. The number of factors is generated from the initial eigenvalues. According to Field (2013), the variance of each component is explained by the initial eigenvalues. The number of components with initial eigenvalues ≥ 1, decides the number of factors.
In this factor analysis conducted on the data received from Perceived Stress Scale, the 'Total Variance Explained' table (Table 6) identifies two components with initial eigenvalues ≥ 1, i.e., 3.646 and 2.103. Thus, the factor analysis has identified two factors. The first component explains 36.457% variance and the second component explains 21.027% variance. Cumulatively they explain 57.483% variance. Thus, we can conclude that the factor analysis identifies two factors and these two factors explain 57.483% variance of the total variance.

Findings related to scree plot
The Scree Plot is a graphical representation of the eigenvalues where eigenvalues are plotted on the y-axis and factors are plotted on the xaxis. It is a line plot where eigenvalues of the factors of PCA are plotted. It helps in determining the number of factors to be retained in an EFA or number of principal components to be retained in PCA. This graphical 0.000*** representation shows the 'elbow', the point beyond which all the other points level off with the x-axis. This helps in the visual identification of the factors emerging from the components.
Here, in this case, the visual representation of the scree plot shows beyond the second point, the rest of the items are levelling-off. Thus, we can state that among the ten components, two factors are distinctively identified by the scree plot, which also confirms the finding of the total variance explained table.

Findings related to rotated component matrix
Factor analysis identifies the presence of different items across different components. Items with high correlation are expected to be found within one component or factor. Information about Pearson correlation between items and the components is gathered in the Rotated Component Matrix table. The nature of the items also helps us to identify the nature of the component. In this case, we used the Varimax rotation, an orthogonal rotation. Unlike Oblique rotation, the correlation between the identified factors is not allowed in Orthogonal rotation, and thus Varimax rotation is used in this case. The findings of the Rotated Component Matrix (Table 7) are as follows: The findings show that factor loading of the items under component 1 ranges between 0.832 to 0.673, while items under component 2 contains the factor loading ranging from 0.767 to 0.664. While component 1 contains six items, component 2 contains 4 items.

Component 1 -perceived distress
The items under component 1 are related to respondents' perception of their distress. Researchers like Folkman, & Gruen, (1985) termed this component as distress components. Thus, we can name the component as Perceived Distress components. The component explains 36.457% of variance of the total variance on all the components with acceptable factor loading ranging from 0.832 to 0.673.

Component 2 -perceived coping
The items under component 2 are related to respondents' perception of their ability to cope with the stress elements. The items indicate the respondent's perception about their success to cope with the stress factors. Thus, we can name the component as Perceived Coping. The component explains 21.027% of variance of the total variance of all components with acceptable factor loading ranging from 0.767 to 0.664.
Thus, it could be concluded that the Perceived Stress Scale (PSS) consists of items which belong to two factors. The first factor contains items related to Perceived Distress and the second factor contains items related to Perceived Coping.

Findings related to the dimensions of elements of mental health
Identification of the dimensions of the Mental Health Elements is another important part of the present research and to find their dimensions, a factor analysis was conducted on the data collected by using Mental Health Inventory items, designed by Viet and Ware (1983). The findings are as follows:

Findings related to KMO and Bartlett's test
The sample adequacy of each variable as well as the entire model is tested by KMO test. Bartlett's test is conducted to identify whether the variables are unrelated. The findings show (Table 8) that the value of KMO is 0.903 which is greater than 0.6. This means that the sample size is adequate to conduct the Factor analysis. Bartlett's test shows p-value 0.000 < 0.05, which means that the variables are related and the correlation matrix is not an identity matrix. Thus, we can continue with the factor analysis.

Findings related to communality
The findings of the Communality show that all the extraction values are > 0.2, and thus, we can continue with the factor analysis without dropping any item (Table 9). Among the different items, the extraction value of the item named, "Have you been anxious or worried?" is 0.968, which means that 96.8% of the variability of this item is explained by the factor. This is the highest variance of an item explained by a factor in this factor analysis. On the other hand, an item named "Have you felt loved and wanted?" has an extraction value of 0.348 in the communality table and thus it could be stated that 34.8% of the variability of this item is explained by the factor. This is the lowest variance which is explained by the factor, in this factor analysis.

Findings related to total variance explained
The findings of the "Total Variance Explained" table (Table 10) show that three components have the eigenvalue ≥ 1. The eigenvalues associated with these components are 7.307, 1.721 and 1.278 respectively. Thus, it could be stated that the factor analysis identified three factors. Among these three factors, the first one explains 40.595% variance of the total variance and the second factor explains 9.562% of the variance of the total variance and the third one explains 7.101% of variance of the total variance. Cumulatively they explain 57.258% of variance of the total variance. Thus, this could be stated that the findings of the Total Variance Explained table identified three factors which explain 57.258% variance of the total variance.

Findings related to scree plot
The scree plot associated with the components of the Mental Health Inventory demonstrates the presence of three factors. The visual representation shows that from the fourth component onwards, the rest of the items are levelling-off and clearly giving an elbow shape. Thus, among the eighteen components, three factors are distinctively identified from the scree plot, which also confirms the finding of the total variance explained table.

Findings related to rotated component matrix
The findings of the Rotated Component Matrix help us to identify the items within a component. The findings (Table 11) are as follows: The findings show that component 1 has eight items and the factor loading ranges from 0.731 to 0.603. Component 2 has eight items and the factor loading ranges from 0.715 to 0.589. Component 3 has two items with factor loading ranging from 0.926 to 0.918.

Component 1 -perceived psychological distress dimension
The eight items under component 1 are related to respondents' perception of their psychological distress. Researchers like Meybodi et al. (2011) termed this component as psychological distress components. Thus, we can name the component as Perceived Psychological Distress Dimension. The component explains 40.595% of variance of the total variance on all the components with acceptable factor loading which ranges from 0.731 to 0.603.

Component 2 -perceived psychological well-being dimension
The eight items under component 2 represent respondent's perception of their psychological well-being. Based on the nature of the items, the component is named as Psychological Well-Being dimension. The component 9.562% variance of the total variance of all the components with an acceptable factor loading which ranges from 0.715 to 0.589.

Component 3 -perceived sense of self dimension
Only two items were found in the third component which represents the perception of the samples about their sense of their own self. Thus, the component is named as Perceived Sense of Self dimension. The component explains 7.101% variance of the total variance of all the components with an acceptable factor loading which ranges from 0.926 to 0.918.

Findings related to the dimensions of brief resilience scale
The next part of the research deals with the identification of the dimensions of the resilience of the samples by using the Brief Resilience scale. The findings are as follows:

Findings related to KMO and Bartlett's test
The findings of the KMO and Bartlett's Test (Table 12) show that the value of KMO is 0.690 which is greater than 0.6, the acceptable level of the KMO test. Thus, it could be concluded that the sample size is adequate to conduct the Factor analysis. Bartlett's test shows p-value 0.000 < 0.05, which means that the variables are related and the correlation matrix is not an identity matrix. Thus, we can continue with the factor analysis.

Findings related to communality
The findings of the Communality Table (Table 13) show that all the extraction values are > 0.2, and thus, we can continue with the factor analysis without dropping any item. Among the different items, the extraction value of the item named, "tend to take a long time to get over set-backs in my life" is 0.667, which means that 66.7% of the variability of this item is explained by the factor. Contrary to this, an item named "have a hard time making it through stressful events" has an extraction value of 0.584 and thus it could be stated that 58.4% of the variability of this item is explained by the factor.

Findings related to total variance explained
Findings of the Total Variance Explained table (Table 14) show that two components have the eigenvalue ≥ 1. The eigenvalues associated with these components are 2.252, and 1.494 respectively. Thus, it could be stated that two factors have been identified by this factor analysis. Among these two factors, the first factor explains 37.533% variance of the total variance and the second factor explains 24.902% variance of the total variance. Cumulatively they explain 62.435% of variance of the total variance. Thus, this could be stated that the findings of the Total Variance Explained table identified two factors which explain 62.435% variance of the total variance.

Findings related to scree plot
The scree plot associated with the Resilience scale shows that after the second component, the rest of the items are levelling off from the third component onwards. Thus, among the six components, two factors are distinctively identified by the scree plot, which also confirms the finding of the total variance explained table.

Findings related to rotated component matrix
The findings of the Rotated Component Matrix (Table 15) received from the factor analysis conducted on the data collected by using the 'Brief Resilience Scale' help us to identify that both the components have three items each. The detailed findings are as follows: The findings show that component 1 has three items where the factor loading ranges between 0.793 to 0.764 and component 2 also has three items where the factor loading ranges from 0.781 to 0.706.

Component 1 -negative priority to resilience
The items under component 1 are related to respondents' perception about the negative priority related to resilience and thus the factor is termed as Negative Priority to Resilience. The component explains 37.533% of variance of the total variance on all the components with acceptable factor loading which ranges from 0.793 to 0.764.

Component 2 -positive priority to resilience
The three items under component 2 are related to the items which indicate the respondents' perception about the positive priority of resilience. Based on the nature of the items, the factor is also named as Positive Priority to Resilience. The component 24.902% variance of the total variance of all the components with an acceptable factor loading ranges from 0.781 to 0.706.

Findings related to adult hope scale
The final part of the present research deals with the identification of the dimensions of the hopes of the samples by using the Adult Hope scale. The findings are as follows:

Findings related to KMO and Bartlett's test
The findings of the KMO and Bartlett's Test (Table 16) show that the value of KMO is 0.813 which is greater than 0.6, the acceptable level of the KMO test. Thus, it could be concluded that the sample size is adequate to conduct the Factor analysis. Bartlett's test shows p-value is 0.000 < 0.05, which means that the variables are related and the correlation matrix is not an identity matrix. Thus, we can continue with the factor analysis.

Findings related to communality
The findings of the Communality table (Table 17) show that all the extraction values are > 0.2, and thus, we can continue with the factor analysis. Among the different items, the extraction value of the item named, "I have been pretty successful in life." is 0.742, which means that 74.2% of the variability of this item is explained by the factor. On the other hand, an item named "I am not easily downed in an argument" has an extraction value of 0.325 and thus it could be stated that 32.5% of the variability of this item is explained by the factor.

Findings related to total variance explained
Findings of the Total Variance Explained table (Table 18) show that three components have the eigenvalue ≥ 1. The eigenvalues associated with these components are 3.804, 1.900 and 1.206 respectively. Thus, it could be stated that three factors have been identified by this factor analysis. Among these three factors, the first factor explains 31.702% variance of the total variance. The second factor explains 15.829% variance of the total variance and the third factor explains 10.052% variance of the total variance. Cumulatively they explain 57.583% variance of the total variance. Thus, this could be stated that the findings of the Total Variance Explained table identified three factors which cumulatively explain 57.583% variance of the total variance.

Findings related to scree plot
The scree plot associated with the Adult Hope Scale distinctively points out the presence of three factors. The visual representation shows that beyond the third component, from the fourth component onwards, the rest of the items are levelling-off. Thus, we can state that among the twelve components, three factors are distinctively identified by the scree plot, which also confirms the finding of the total variance explained table. .000

Findings related to rotated component matrix
The findings of the Rotated Component Matrix (Table 19) received from the factor analysis conducted on the data collected by using the 'Brief Resilience Scale' help us to identify that both the components have three items each. The detailed findings are as follows: The findings show that all the three components have four items each. Component 1 has four items with factor loading ranging from 0.853 to 0.573, component 2 also has four items with factor loading ranging from 0.774 to 0.542 and component 3 has four items with factor loading ranging from 0.801 to 0.563.

Component 1 -success oriented drive
The four items under component 1 are related to the success-oriented attitude of the respondents and thus the factor is termed as Success Oriented Drive. The component explains 31.702% of variance of the total variance on all the components with acceptable factor loading which ranges from 0.853 to 0.573.

Component 2 -problem solving drive
The four items under component 2 are related to the items which indicate the problem-solving attitude of the respondents. Based on the nature of the items, the factor is also named as Problem Solving Drive. The component explains 15.829% variance of the total variance of all the components with an acceptable factor loading which ranges from 0.774 to 0.542.

Component 3 -self-confidence oriented drive
The four items under component 3 are related to the self-confidence related attitude of the respondents. Based on the nature of the items, the factor is also named as Self-Confidence Oriented Drive. The component explains 10.052% variance of the total variance of all the components with an acceptable factor loading which ranges from 0.801 to 0.563.

Discussion
Identification of the dimensions of psychological stress, mental health, hope and resilience is a major outcome of this study. The Table 5 Analysis of Communalities of the items of the Stress Elements.

Communalities
Initial Extraction s1In the last month, how rarely have you been upset because of something that happened unexpectedly?    .000 understanding of these dimensions will facilitate the understanding about the impact of these dimensions in developing different types and levels of psychological stress as well as mental health, hope and resilience. This study was undertaken to estimate the prevalence of psychological stress and examine its socio-demographic correlates, hope and resilience among Indian youth during this current COVID-19 pandemic. Besides this, identification of the dimensions of the stress elements, mental health and that of resilience and hope were also important objectives of the study. The study identified a large number of respondents suffered moderate to a high level of psychological stress during this current COVID-19 pandemic and is found to be consistent with the findings from the previous studies (Grover et al., 2020;Islam and Mamun, 2020). Similar to the findings of Islam and Mamun (2020), peak of mortality rate during pandemic period and disruption of the normal flow of education, future plans and careers are found to be significant and main reasons for perceived stress. Findings related to the distribution of psychological stress among genders identified females as the gender segment to have higher stress than their male counterparts. Similar findings were received by Qiu et al. (2020), Zhou et al. (2020) in their study on Chinese population, and also in the study of AlAteeq et al. (2020), Islam and Mamun (2020) conducted on the Saudi Arabia and Bangladesh population. But, as mentioned by Smith et al. (2020), to identify pandemic situations as the main responsible factor of mental distress, further longitudinal epidemiological studies are required to be conducted. The study also identified that a single child significantly    showed higher psychological stress compared to one or more siblings echoed the findings of Taylor et al. (2008) conducted in Australia. Besides this, the study also identified that mothers, especially with some form of formal qualification and who possess knowledge about the academic progress of their children and wish better performance of their children, were found to be affected by the closure of schools and institutes which lead to increase in their psychological stress. However, since psychological stress positively correlates with other forms of mental health morbidities, appropriate psychological support and counselling for both the youth and their parents may be merited. Findings of the study also identify a significant association between psychological stress of the respondents and their negative attitude and inability of comprehension or clarification in the context of online teaching and learning. Similar findings have been observed by Dangi and George (2020), Keskin and Yurdugül (2019); and Thongsri et al. (2019). Even though the online mode of teaching and learning was a new challenge to most educators and students, the high correlation between technical issues such as poor internet connectivity and perceived stress was expected.
The present study also identified that higher hope and resilience were significantly correlated with lower psychological stress. This finding echoes the unison of the previous studies conducted by Munoz et al. (2020), where the author identified hope and resilience as important predictors of psychological flourishing and also reflects the finding of Duggal et al. (2016) where hope and resilience were identified as predictors of better physical and mental health.

Limitations of the study
As is usual with most cross-sectional studies, findings from this study cannot assign causal relationships. Collection of online responses, although save time, cannot guarantee the precision of the information. Hence, findings from this study cannot be generalized to the entire population.

Conclusion and recommendations
The study attempts to identify the dimensions of psychological stress, mental health, hope and resilience and uncover their impact on the youth during the pandemic. Both objectives are successfully achieved. Further study could be conducted to identify the existence of these factors in the post-pandemic world. If the factors remain unchanged and so are their impact, mental health professionals have to develop mechanisms to confront and redress. There is a need to carry out a retrospective study on challenges faced by parents in dealing with the emotion and stress of their youth during the COVID-19 pandemic. The findings of this type of study would help the administrators of educational institutions to extend online counselling to parents to deal with the emotion of the children and youth comfortably during any crisis. There is also a need to study the stress of teachers in conducting    .000

Table 17
Analysis of Communalities of the items of the Adult Hope Scale. continuous online classes and other challenges faced by them, especially for teachers working in private educational institutions.

Declaration of Competing Interest
Authors declare no conflict of interest in the publication of this research.

Funding
This was a syndicated study.