Resilience and Psychological Well-Being of Higher Education Students During COVID-19: The Mediating Role of Perceived Distress

Abstract

The novel coronavirus disease (COVID-19) pandemic has created a crisis with a severe effect on the masses, particularly the young students across the world. Framed by the transactional theory of stress and coping, this study investigates the factors influencing psychological well-being (PWB) of the students in higher education during the peri-traumatic phase of COVID-19. A cross-sectional survey using a questionnaire was employed. The study aimed at prediction and theory building and was carried out in India. Data were collected online from 173 higher education students. Partial least squares structural equation modelling (PLS-SEM) was used to test the hypothesised relationships among the constructs. Results indicated resilience significantly predicts PWB both directly and indirectly. Further, the study revealed perceived distress and PWB are not on the same continuum. This study has a contribution to theory and health promotion. Furthermore, the findings have several practical implications for counselling psychologists, academicians, and mental health workers associated with the higher education sector. These findings will put them in a better position to design interventions to enhance resilience in students in the backdrop of the relevance of both positive and negative mental health during the outbreak of diseases. Based on the findings, future directions were discussed.

Keywords

Crisis pandemic peri-traumatic phase stressors trauma

Introduction

Spread and the rippling effect of coronavirus is evident worldwide. The pandemic has shown the world in different ways (Gupta, 2020; Pan, 2020). The World Health Organization declared the novel coronavirus disease (COVID-19) as a global health emergency in March 2020 (Ducharme, 2020). Galvanised into action, the government of India took stringent, pre-emptive and mitigating action against the pestilential coronavirus (Thakur & Jain, 2020). People were hunkered down to accept the tsunami of lockdowns to save their lives and containment. However, lockdown had an overwhelming effect of social disruption (Kumar & Nayar, 2020) on mental health (Das, 2020; Hiremath et al., 2020; Kochhar et al., 2020).

A whopping number of 1.54 billion students worldwide, including those in higher education, are reported by UNESCO to be jeopardised by the closing of the institutions (Giannini & Albrectsen, 2020). Hence, the lockdown has profound repercussions for the students (Sahu, 2020). Conventional methods unsuited to the current situation being set aside, the mass transition to the online medium of learning have exerted an unprecedented effect on the students (Baloran, 2020). Pushed by this ordeal, students have embraced the change for continual and unhampered academic growth (Corbera et al., 2020). Undeniably, students are wrestling with unwanted changes.

Extant literature on students in higher education reflects the prevalence of psychological distress and mental health problems (Bruffaerts et al., 2018; Ovuga et al., 2006). Besides, incremental piling up of distress during the pandemic could be consequential. This calls for an emergent need to explore factors responsible for the management of mental health problems, (Boyraz & Legros, 2020) and promotion of psychological well-being (PWB) (Yang & Ma, 2020).

Despite excessive stress, experiencing a high level of well-being is possible (Grant et al., 2003). Hence, as explained by Diener (2000) the subjective feeling of the positive affective state is experienced. In traumatic events, failure to manage the situation efficiently results in perceived distress. Transactional theory of stress and coping (Lazarus & Folkman, 1984) explains the effect of personality traits on self-appraisal in dealing with the environmental stressors. Traumatic events build trait resilience that protects PWB and keeps from developing mental health problems is backed up by the evidence (Mowbray, 2020; Peng et al., 2012). Earlier conceptual and empirical research papers have revealed that resilience has adaptive qualities (Calhoun & Tedeschi, 2004; Luthar et al., 1993), and becomes functional in adverse conditions of life (Carver, 1998; Goodman et al., 2020). It is confirmed in studies carried out on students (e.g., Bacchi & Licino, 2017; Bore et al., 2016). In adverse life conditions, the resilience develops which enables control of distress and enhances PWB.

This stream of research has established the importance of understanding the mechanism through which PWB could be promoted. Nevertheless, it remains a controversial territory for inadequate evidence of whether perceived distress and PWB are similar or independent (Winefield et al., 2012; Zautra et al., 2005). Thus, its understanding would uncover the situations in which we should measure both.

Research on resilience, distress, and PWB is extensive but is primarily concerned with the patients or the students in medical settings. Studies on students in higher education are under-represented (e.g., Holdsworth et al., 2018). Exploration of perceived distress as a mediator between resilience and PWB is important for the students in higher education. Thus, this work is mainly conducted in response to the need to explore factors that would enhance the PWB of the students during the prolonged crisis (Yang & Ma, 2020).

Lack of certainty (Corbera et al., 2020; Mowbray, 2020), concern for future (Bacchi & Licino, 2017; Das, 2020) creates psychological distress. Owing to lockdown, symptoms of distress were reported in students (Odriozola-González et al., 2020). The negative relationship between perceived distress and PWB has been established (Meng & D’Arcy, 2016). Hence, the following hypothesis:

H₁: Perceived distress would have a significant negative and direct effect on PWB.

The negative relationship between resilience and psychological distress has been reported earlier (Bacchi & Licinio, 2017). Resilience has potential in lowering distress (Tecson et al., 2019). Albeit in the face of current catastrophe, one has to draw from the stores of undiminished resources. Failure to adjust to the traumatic stress creates psychological casualties. Hence,

H₂: Resilience would have a significant negative and direct effect on perceived distress.

Resilience enables one to withstand perilous situations relentlessly and persevere through life’s inevitable stressors (Polizzi et al., 2020). Srivastava (2011) emphasised that resilience improves well-being. On the contrary, lower resilience can result in mental health problems (Mowbray, 2020). Previous researchers have revealed a positive impact of resilience on PWB (Smith & Yang, 2017; Souri & Hasanirad, 2011). Hence, the following hypothesis:

H₃: Resilience would have a significant positive and direct effect on positive PWB.

Additionally, previous researches suggest that the impact of resilience on PWB is indirect, and various factors mediate it (Liu et al., 2012; Malkoç & Yalcin, 2015). Although studies on the mediating relationship between resilience and PWB have been carried out, to the best of our knowledge there is only one such study on students considering distress as a mediator (Malkoç & Yalcin, 2015). Hence, the following hypothesis:

H₄: Resilience would have an indirect effect on PWB through perceived distress.

Therefore, taking a clue from the empirical studies, and a priori theoretical support, the present research focuses on the evaluation of the mediating role of perceived distress between resilience and PWB in students in higher education. The theoretical model is presented in Figure 1.

Figure 1.

Source: Authors’ own.

This study, therefore, aims to contribute to both theory and health promotion. Our exploration of factors in promoting PWB of students during the peri-traumatic phase of the COVID-19 crisis will be a notable addition to the field. This might assist mental health service providers to determine focus on designing interventions to enhance resilience in students, so they are prepared to fight challenges. From a theoretical perspective, it shall provide knowledge about issues in the measurement of perceived distress and PWB.

Method

Sample and Data Collection

This study is a cross-sectional online survey based. A-priori sample size was calculated using ‘G*Power 3.1.9.2’ (Faul et al., 2007). Based on effect size (f² = 0.15), and statistical power (95% significance) (Cohen, 1988) minimum sample size determined was 89. Initially, the survey link was sent through social media to 200 students in Jammu (India), pursuing non-professional degree programs through regular mode. One hundred and eighty students filled the survey, of which seven were dropped as they gave unengaged responses. Thus, the final sample size of 173 students fulfilled the minimum criteria. The sample comprised of 127 females (73.4%) and 46 males (26.6%). The age ranged between 18 and 31 years (M = 22.79, SD = 2.51).

The survey comprised of demographic information and the battery of scales. Assurance regarding no threat, no hidden cost of participation, and confidentiality was given. Electronically, informed consent was obtained for their participation in this study. Voluntary participation was sought, and in case participants wanted, they could withdraw any time.

Measures

Perceived Distress

Six negatively worded items (1, 2, 3, 6, 9, 10) measuring perceived distress were borrowed from Perceived Stress Scale (Cohen et al., 1983) are sensitive to ongoing life circumstances (Cohen & Williamson, 1988). The items pertain to one’s feelings and thoughts in the last one month. Items are scored on a 5-point Likert scale, ranging from 0 to 5.

Resilience

The Brief Resilience Scale (BRS) comprises of six items to assess the ability to bounce back or to recover from stress (Smith et al, 2008). Items 1, 3 and 5 are positively worded, and items 2, 4 and 6 are negatively worded. Items are scored on a 5-point Likert scale, ranging from 1 to 5.

Psychological Well-Being

WHO-5 Well-being Index (World Health Organization [WHO], 1998) measures positive PWB. Positively worded five items in this scale cover mood, vitality and general interest. The items are rated on a 6-point Likert scale ranging from 0 to 5.

Data Analysis

The presence of environmental stressors creates crisis and trauma. On being asked, all the participants confirmed hampered academic activities and continuous exposure to pandemic related stressful information. Hence, meets the inclusion criteria of the study.

Since the current study was aimed at prediction and theory building, partial least squares structural equation modelling (PLS-SEM) was applied. The steps for analysis (Hair et al., 2018) were followed. Data were analysed using ‘Smart PLS software version 3.3.2’ (Ringle et al., 2015).

Results

Common Method Bias

The issue of common method bias (CMB) was assessed. As per Kock (2015), CMB from the perspective of PLS-SEM results from the measurement method used for the study. Variance inflation factor (VIF) obtained by applying full collinearity test for all latent variables in the model (Table 1) were below threshold 3.3 (Kock, 2015). Hence, the model is free from CMB.

Table 1.

Measurement Model Results.

Construct	Items	Loadings	ρA	CR	AVE	Full Collinearity VIFs
Perceived distress	PD1	0.770	0.812	0.858	0.505	1.489
	PD2	0.692
	PD3	0.790
	PD4	0.574
	PD5	0.633
	PD6	0.777
Psychological well-being	PWB1	0.696	0.827	0.876	0.586	1.443
	PWB2	0.821
	PWB3	0.783
	PWB4	0.755
	PWB5	0.767
Resilience	R1	0.807	0.811	0.857	0.505	1.367
	R2	0.655
	R3	0.830
	R4	0.701
	R5	0.530
	R6	0.700

Source: Authors’ calculation.

Notes: ρA, Dillion–Goldstein’s rho; CR, composite reliability; AVE, average variance extracted.

Measurement Model

For assessing reflective constructs, the convergent validity was tested. Convergent validity was achieved with average variance extracted (AVE) more than the threshold value 0.50 (Hair et al., 2014) for all the constructs. We retained all the items with loadings within the acceptable range as AVE was above 0.50 for associated constructs (Hair et al., 2014). Composite reliability (CR) (Hair et al., 2018), and Dillion–Goldstein’s rho (ρA) (Dijkstra & Henseler, 2015) were higher than 0.7 for the constructs. These results (Table 1) indicate that measurement model is internally consistent and reliable. The discriminant validity in PLS-SEM is tested by Heterotrait–Monotrait ratio of correlations (HTMT) (Hair et al., 2019). As demonstrated in Table 2, all the values are below the conservative criterion 0.85 (Henseler et al., 2015) exhibiting discriminant validity.

Table 2.

Heterotrait–Monotrait (HTMT) Ratio for the Constructs.

	Psychological Distress	Psychological Well-being	Resilience
Psychological distress	–	–	–
Psychological well-being	0.618	–	–
Resilience	0.568	0.531	–

Source: Authors’ calculation.

Structural Model

Assessment of structural model results requires testing the structural relationships between constructs, model’s in-sample predictive accuracy and model’s out-of-sample predictive power (Hair et al., 2019).

The significance of proposed hypotheses was assessed using bias-corrected and accelerated (BCa) bootstrap with 5,000 re-samples. Based on the results for the structural model, all the hypotheses for the study were supported (Table 3). We used the product coefficients approach to assess the mediation effect. Perceived distress was found to mediate the relationship between resilience and PWB (H4) (Table 3). Further, this indirect effect was positive. However, the results demonstrate a stronger direct effect than the indirect effect. The VIF of 1.273 of our model was below threshold 3.33 (Diamantopoulos & Siguaw, 2006). Hence, our model is free of collinearity issues.

Table 3.

Results Structural Model.

Hypothesis	Direct/Indirect Effect	Path Coefficient	t-Value	Confidence Interval (95%) BCa	Effect Size (f²)	Decision
H1	PD↓PWB	−0.388	5.235	[−0.496, −0.249]	0.170	Accepted
H2	R↓PD	−0.463	7.426	[−0.554, −0.346]	0.273	Accepted
H3	R↓PWB	0.256	3.375	[0.119, 0.367]	0.074	Accepted
H4	R↓PD↓PWB	0.179	4.412	[0.116, 0.247]	NA	Accepted

Source: Authors’ calculation.

Notes: PD, perceived distress; PWB, psychological well-being; R, resilience.

Next, structural paths and R² values were examined to determine the model’s in-sample predictive accuracy (Hair et al., 2014). Coefficient of determination (R²) 0.214 and 0.308 was obtained for perceived distress and PWB, respectively. 30.8% variance of PWB was jointly explained by the resilience and perceived distress. R² value of 0.2 is high in behavioural sciences research (Rasoolimanesh et al., 2017). Results in Table 3 show effect size (f²) for the significant path coefficients are substantial. f² 0.35, 0.15 and 0.02 reflects large, medium and small effect size, respectively (Cohen, 1988). Finally, the Q² values for perceived distress (0.102) and PWB (0.171) were greater than zero (Geisser, 1974; Stone, 1974). This establishes predictive relevance for endogenous constructs.

For evaluating the model’s out-of-sample predictive power (Shmueli et al., 2019) cross-validation by randomly splitting the sample into a model training sample and a holdout sample was done. We obtained 0.920 as root mean squared error (RMSE) and 0.745 as the mean absolute error (MAE). Q²_predict value of 0.169 for the model is positive. This reflects PLS-SEM model has desirable predictive relevance. Further, indicator wise analysis shows that in comparison to the linear regression model (LM) benchmark, RMSE and MAE values of indicators of PWB for the PLS model are lower, except PD2 (see Table 4). Q²_predict values for the PLS model are larger than for LM for most of the indicators. This establishes a model’s medium out of sample predictive power.

Table 4.

PLS Indicator Prediction Summary.

PLS			LM			PLS-LM
Item	RMSE	MAE	Q ² _predict	RMSE	MAE	Q ² _predict	RMSE	MAE	Q ² _predict
PD1	0.914	0.693	0.147	0.917	0.710	0.142	−0.003	−0.017	0.005
PD2	0.991	0.792	0.079	0.988	0.773	0.085	0.003	0.019	−0.006
PD3	1.075	0.828	0.075	1.095	0.845	0.040	−0.02	−0.017	0.035
PD4	0.920	0.723	0.112	0.934	0.743	0.086	−0.014	−0.02	0.026
PD5	1.089	0.840	0.048	1.121	0.868	−0.009	−0.032	−0.028	0.057
PD6	1.009	0.798	0.136	1.020	0.814	0.116	−0.011	−0.016	0.020
WB1	1.230	0.976	0.027	1.248	0.998	−0.003	−0.018	−0.022	0.003
WB2	1.151	0.934	0.114	1.190	0.956	0.053	−0.039	−0.022	0.061
WB3	1.136	0.914	0.109	1.172	0.944	0.051	−0.036	−0.03	0.058
WB4	1.370	1.142	0.087	1.386	1.156	0.065	−0.016	−0.014	0.022
WB5	1.251	1.016	0.158	1.284	1.024	0.112	−0.033	−0.008	0.046

Source: Authors’ calculation.

Notes: RMSE, root mean squared error; MAE, mean absolute error; PLS, partial least squares; LM, linear regression model.

Discussion and Conclusion

The main objective of this study was to assess the mediating role of perceived distress in the relationship between resilience and PWB, and to compare the direct and indirect effects. All the proposed hypotheses in this research were supported. The results showed the significant and stronger direct effect of resilience on PWB in comparison to the indirect effect. Hence, the mediating role of perceived distress between resilience and PWB is complementary (Nitzl et al., 2016). Building trait resilience might enhance PWB directly and indirectly as well by reducing students’ perceived distress in the peri-traumatic period of COVID-19. Next, resilience significantly influenced perceived distress. This is consistent with Bacchi and Licino (2017), where students with higher levels of resilience had reported lower levels of psychological distress. Zhang et al. (2018) also found that resilience is critical to the level of psychological distress. The results of the present study revealed the enhancing effect of resilience on PWB. These findings illustrate that even during the adverse life conditions; students with trait resilience are likely to display positive PWB. These findings agree well with existing studies, many of which have been conducted on students in other settings (e.g., Malkoç & Yalçın, 2015; Smith & Yang, 2017; Souri & Hasanirad, 2011).

Resilience has shown to decrease students’ perceived distress, which might then increase their PWB. This demonstrates the adaptive capacity of trait resilience in traumatic situations. This argument is consistent with the findings of (Bacchi & Licino, 2017; Mowbray, 2020). This finding may lead to a better understanding of how, in the presence of resilience, perceived distress is diminished which further promotes PWB. Resilience is already known to work in adverse life situations (Bonanno et al., 2008; Goodman et al., 2020). It helps in returning to one’s normal state and has an adaptive and protective function (Calhoun &Tedeschi, 2004; Luthar et al., 1993). That is to say, although in crisis, students might experience enhanced PWB. However, the individual higher resilience might not perceive heightened distress. This might help them further have enhanced effect and feelings Diener (2000), hence, higher subjective or PWB.

In practical terms, this study has implications for counselling psychologists, academicians, and mental health workers. The results indicate the relevance of enhancing inner psychological capacities on which students can capitalise. It could be inferred from the results that interventions should be designed to build resilience in young students. The findings of this study reinforce the proposition of the transactional theory of stress. The transactional theory of stress suggests trait resilience as a potential resource that might influence an individual’s perception of distress during adverse situations. Resilience empowers an individual to deal with the stressors. Distress emanating from indirect exposure to several environmental stressors during the acute peri-traumatic phase of COVID-19 could be overcome in case resilience is enhanced. With the outbreak of COVID-19, students were exposed to several stressors ranging from threat to survival to academic challenges. Instead of giving support to the students once the signs of distress appear and negative mental health outcomes develop, the main focus should be on helping students realise their inner potential so that they may adjust well to the challenging times. Therefore, emphasis should be on working on the individual while they are exposed to the stressors. As such, during the peri-traumatic phase, it is possible to maintain and promote the PWB of the students. Providing timely tele-counselling during this challenging period might prove to be beneficial in minimising the perceived distress. Students experiencing crisis might restore their equilibrium by remaining resilient and keep from developing negative mental health outcomes.

This study contributes to the resilience and trauma literature. Previous studies have explored the effect of resilience on PWB with various other mediators. There are hardly any studies trying to understand the effect of perceived distress on PWB. To the best of our knowledge, the mediating role of perceived distress remains under-explored. This study is novel in its investigation of the mediating role of perceived distress between resilience and PWB. Therefore, this serves as the core theoretical contribution of this study. It adds to the prior body of literature in trauma research. We have empirically proved the relevance of building on resilience to enhance PWB by lowering perceived distress.

Finally, we have also attempted to understand concerns in the measurement of perceived distress and PWB. Perceived distress and PWB were hardly taken together in the same study due to misconceptions about its measurement. The significant effect of perceived distress on PWB indicates proves they are different. This finding is similar to Winefield et al. (2012). The results of this work have unravelled and shed light on the need for measurement of perceived distress and PWB in the same study, especially in trauma research. However, there is still a great deal of work to be done in this area.

In this study, several potential limitations exist that guide the future direction of research. First, we only examined the mediating effect of perceived distress between resilience and PWB. The proposed relationships in the model might differ across gender. Second, future studies should adopt a longitudinal approach to explore the hypothesised direct and indirect paths as there is a possibility of the development of negative outcomes after the traumatic situation is over. Third, future research is suggested to examine the role of exposure to negative information and isolation in the model. Finally, future research should be conducted on school students to better understand its relevance across the student community at different levels.

This article examined the mediating role of perceived distress between resilience and PWB in higher education students during COVID-19. The findings revealed that the direct effect of resilience on PWB is stronger when compared to its indirect effects, that is, when perceived distress is included as a mediator. Since the indirect effect also exists, it challenges overemphasis in previous studies on just the direct effect of resilience on PWB. Protracted COVID-19 pandemic appears to be a challenge to the students. However, perceived distress could be minimised to promote PWB in students if the students are made resilient. Resilience is pivotal in determining positive and negative mental health. Moreover, understanding both perceived distress and PWB are important for the students so that they are better able to overcome adversity in traumatic situations.

Footnotes

Declaration of Conflicting Interests

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

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