Investigating the Relationship Among Fear of COVID-19, Student Engagements in Online Learning Environment and Psychological Well-Being: The Mediating Role of Intolerance of Uncertainty

Investigating the Relationship Among Fear of COVID-19, Student Engagements in Online Learning Environment and Psychological Well-Being: The Mediating Role of Intolerance of Uncertainty | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Investigating the Relationship Among Fear of COVID-19, Student Engagements in Online Learning Environment and Psychological Well-Being: The Mediating Role of Intolerance of Uncertainty Gazanfer ANLI This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5968746/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Due to the coronavirus disease pandemic, most countries have made rapid decisions on various topics. Regarding education, to prevent the spread of coronavirus, it was decided to replace face-to-face learning. In Türkiye, universities started offering online classes in March 2019. This transition has raised concerns regarding student engagement in online learning environments (SEOLE) and psychological well-being (PWB) due to the fear of COVID-19 (FC-19) as a psychological construct. Furthermore, intolerance of uncertainty (IU) has emerged as a critical mediator influencing these dynamics. Four models were proposed to examine these relationships. In Models 1 and 2, intolerance of uncertainty mediates the relationship between fear of COVID-19 and student engagement in online learning environments, with age, gender, class level, and perceived success included as control variables. Models 3 and 4 investigate how intolerance of uncertainty mediates the relationship between fear of COVID-19 and psychological well-being, using the same control variables. By synthesizing empirical studies and theoretical frameworks, we elucidate mechanisms that may mitigate the adverse psychological effects of the pandemic among students. Fear of COVID-19 intolerance of uncertainty student engagement psychological well-being Figures Figure 1 Introduction The COVID-19 pandemic has led to unprecedented changes in educational systems worldwide, necessitating a rapid transition to online learning. This shift has not only reshaped pedagogical practices but has also dramatically shaped students’ psychological states (Crawford et al., 2020 ). In the midst of this transition, fear of COVID-19 (FC-19) emerged as a major psychological burden. As an emotional reaction to the threat of the virus, this fear has consequences not only for public health but also for individual mental health and educational outcomes (Ahorsu, et al., 2020 ). FC-19 is particularly pertinent in academic contexts where students must adjust to new learning modalities while handling increased levels of stress and uncertainty. Research has revealed that FC-19 can lead to negative psychological consequences such as increased anxiety, depression, and decreased engagement in academic tasks (Aristovnik et al., 2020 ). FC-19 was characterized by constant worry about infection and its outcomes, further complicating these dynamics, potentially exacerbating intolerance of uncertainty (IU) and negatively impacting students’ PWB (Zarić et al., 2022 ). Intolerance of uncertainty (IU), characterized by an inability to withstand the unknown, has been associated with increased emotional distress and maladaptive coping behaviors (Carleton et al., 2007 ). IU has been recognized as a key factor influencing students’ engagement in the online learning environment (SEOLE) and their mental well-being during this period (Doğanülkü et al., 2021 ). Previous studies have emphasized that individuals with higher IU levels are more likely to experience deteriorated routines and reduced mental health during crises (Satıcı et al., 2020 ). Therefore, an examination of its role as a mediator can provide valuable insights into how FC-19 translates into specific student outcomes. Students’ engagement, defined as the level of attention, enthusiasm and participation in academic activities, is a vital indicator of academic achievement (Fredricks et al., 2004 ). The sharp transition to online learning has posed challenges in sustained engagement, with students reporting diminished motivation and distraction (Gonzalez-Ramirez et al., 2021 ). Understanding how FC-19 and IU affect engagement is pivotal to designing effective educational interventions. PWB, which encompasses emotional stability, positive relationships, and sense of purpose, is another critical area of concern during the pandemic. The widespread fear and uncertainty associated with COVID-19 has been shown to weaken well-being, leading to heightened rates of anxiety, loneliness, and fatigue among students (Shi et al., 2024 ). By investigating the mediating role of IU, this study illuminates the pathways through which FC-19 affects psychological health and draw implications for mental health support interventions. To ensure a rigorous analysis, the study incorporates age, gender, class level, and perceived success as control variables. These factors influence both educational and psychological outcomes, adding contextual nuance to the findings. Theoretical Framework FC-19 and PWB FC-19 has emerged as an important psychological construct that affects multiple aspects of individuals’ lives, including their mental health and engagement in supportive behaviors. Research has demonstrated that FC-19 is correlated with elevated levels of anxiety and stress among students, which may aggravate IU (Pakpour et al., 2021 ). Fear of being contaminated with the virus or infecting loved ones can increase IU by creating a widespread sense of uncertainty, which negatively impacts students’ ability to fully engage in their studies (Yang & Wang, 2023 ). This relationship emphasizes the need to address FC-19 as a critical determinant in the context of student engagement and PWB. SEOLE will be represented in the following section. SEOLE Engagement in online learning includes active participation in educational activities, emotional investment, and cognitive engagement (Capone & Lepore, 2022 ). According to Self-Determination Theory (SDT), meeting basic psychological needs for competence, relatedness, and autonomy increases motivation and well-being (Deci & Ryan, 2013 ). By promoting these factors, SEOLE can act as a buffer against the negative effects of pandemic-related fear. The shift to online learning has necessitated a reevaluation of student engagement strategies. SEOLE is crucial for academic success and PWB. Nevertheless, students with high IU may struggle with engagement due to anxiety and fear of the unknown, which can lead to diminished motivation and engagement (Oral & Karakurt, 2022 ). Besides, the absence of face-to-face interaction in online environments may intensify feelings of isolation and disengagement. Research has revealed that meeting basic psychological needs is essential for increasing SEOLE, when these needs are not satisfied, students may seek maladaptive coping mechanisms (An et al., 2024 ). Consequently, the promotion of an engaging online learning environment that addresses psychological needs is essential for enhancing students’ wellbeing. The following section will describe the interplay between IU and SEOLE. IU and SEOLE IU has been associated with numerous maladaptive outcomes, such as anxiety and procrastination, especially among university students (Doğanülkü et al., 2021 ). Research suggests that higher IU levels are associated with decreased SEOLE because students may struggle to adapt to the unpredictability of online classrooms (Huda, 2023 ). This is especially evident in the context of the COVID-19 pandemic, where uncertainties about health, academic performance, and future employment prospects have intensified students’ IU (Mok et al., 2021 ). As a result, students with high IU may experience difficulties in maintaining their motivation and engagement in their studies, which may lead to poorer academic outcomes. We next examine the relations of IU and PWB in the following section. IU and PWB IU is a psychological construct that refers to an individual's difficulty in accepting the unknown or ambiguous situations. Research has demonstrated that higher IU levels are related to increased anxiety, depression, and stress, especially in unpredictable environments such as COVID-19 (Oral & Karakurt, 2022 ). Individuals with high IU often perceive uncertain situations as more threatening, which may intensify psychological distress and inhibit effective coping strategies (Satıcı et al., 2020 ). This maladaptive response can result in a cycle of negative feelings and actions, further reducing PWB. Furthermore, the pandemic has intensified feelings of uncertainty, resulting in increased psychological distress between students (Padrón et al., 2021 ). FC-19 has been shown to mediate the associations between IU and mental health outcomes, indicating that as fear increases, so does the impact of IU on PWB (Satıcı et al., 2020 ). This interplay highlights the importance of addressing IU in therapeutic settings, especially for students navigating the challenges of online learning. In the next section, PWB in online learning contexts will be addressed. PWB in Online Learning Contexts The PWB of students is fundamental for effective learning and engagement. Students’ mental health has degraded during the pandemic, with heightened reports of anxiety, depression, and stress, thus, the transition to online learning has been associated with feelings of isolation and disconnection, further exacerbating psychological distress (Ahorsu et al., 2022 ). Importantly, IU has been found to be a predictor of psychological distress, suggesting that students with higher IU may be more sensitive to negative mental health outcomes during this period (Huda, 2023 ). This relationship underscores the importance of incorporating IU into interventions that promote student well-being in online learning settings. Rationale of the Study The relationship between FC-19, IU, student engagement, and PWB is supported by empirical evidence. Studies have revealed that students with high levels of FC-19 experience greater difficulties in navigating uncertainty, which in turn affects their engagement in online learning (Moussa et al., 2022 ). This suggests that FC-19 may generate a feedback cycle that further reduces PWB by increasing the negative effects of IU on student engagement contributing to heightened stress, anxiety, and depression (Salari et al., 2020 ). This fear can result in avoidant behaviors, social isolation, and decreased resilience, specifically in vulnerable populations such as students struggling with academic and social challenges (Seçer & Ulaş, 2020 ). IU exasperates the impact of fear on psychological outcomes by increasing perceived threats and weakening coping strategies. Individuals with high IU levels are more prone to anxiety in uncertain circumstances, such as during the COVID-19 pandemic. (Akbari et al., 2024 ). For these reasons, we predicted that IU would both mediate the relationships between FC-19 and SEOLE; FC-19 and PWB. Control variables are used to determine whether the relationship between two variables is real or due to a chance association with other related variables and employed to eliminate “nuisance” variance in research (Carlson & Wu, 2012 ). Gender, age, class level, and perceived success were selected as control variables in this study. The change in the relationship between FC-19 and PWB and FC-19 and SEOLE is examined when IU is the mediator and gender, age, class level, and perceived success were controlled. The models and hypotheses are listed below. Maxwell et al. ( 2011 ) advise against overreliance on cross-sectional mediation studies but note that well-structured mediation models can still provide valuable insights when grounded in robust theoretical foundations (Maxwell et al., 2011 ). The proposed pathways (FC-19 → IU → SEOLE/PWB) are consistent with existing theories, notably Carleton's (2016) Uncertainty Distress Model, which suggests that fear-causing events amplify IU, resulting in negative psychological and behavioral consequences (Carleton, 2016 ). Studies based on empirical research during the COVID-19 pandemic, such as those conducted by Satıcı et al. ( 2020 ) and Doğanülkü et al. ( 2021 ), have also shown similar directional connections, which provide a basis for our proposed mediation model. Models 1 and 2: FC-19, IU, and SEOLE FC-19 is a pervasive emotional response that can impair students’ ability to focus and engage in online learning. IU, a dispositional characteristic reflecting discomfort with ambiguity, may exacerbate this relationship by increasing stress and reducing cognitive resources needed for engagement. Student engagement, which is defined as the degree of attention, curiosity and involvement in educational activities, is a vital indicator of learning outcomes in online contexts. Hypotheses for Models 1 and 2 : FC-19 is associated with SEOLE. IU mediates the relationship between FC-19 and SEOLE. The mediating role of IU is still significant when age, gender, class level, and perceived success are controlled. Models 3 and 4: FC-19, IU, and PWB FC-19 can substantially deteriorate PWB, which is characterized as a domain of mental health comprising emotional equilibrium, positive relationships, and a sense of purpose. As a cognitive-emotional component, IU may serve as a mediating factor by increasing negative emotional reactions and diminishing adaptive coping skills. Hypotheses for Models 3 and 4: FC-19 is associated with PWB. IU mediates the relationship between FC-19 and PWB. The mediating role of IU is still significant when age, gender, class level, and perceived success are controlled. Method Participants and Procedures The research sample was composed of 316 Turkish university students (251 females, 65 males) from undergraduate (Freshman = 93, sophomore = 85, senior = 45, junior = 60), graduate (n = 30) and PhD degrees (n = 3). Their ages ranged from 18 to 57 years, and the mean age of the participants was 22.08 (SD = 4.58). The perceived success level of the students was also asked (very successful = 16, successful = 148, medium successful = 133, unsuccessful = 14, very unsuccessful = 5). A widely accepted benchmark in the social sciences is the goal of achieving a minimum effect size of 0.80 (Cohen, 2013 ). With the help of G*Power software, it can be determined that to achieve sufficient power, a sample size of approximately 90 participants per group or condition would be required when considering medium effect sizes (Cohen’s d = 0.5) and an alpha of 0.05 (Lakens, 2022 ). Having a total sample size (n = 316) that exceeds this threshold ensures sufficient power to identify substantial effects in mediation studies. Ethical and organizational permissions were obtained from the participants before the study. Prior to participating in the study, informed verbal consent was obtained from each participant. A detailed information sheet was provided to participants, detailing the study's objectives, methods, possible risks, confidentiality policies, and their option to withdraw at any point during the study. Following this, participants were instructed to click an 'I agree to participate' button to formally indicate their consent. This methodology was endorsed by the Institutional Review Board and adheres to the ethical standards for low-risk online studies. The Snowball sampling method was used to administer the online survey. The scale forms were administered through online Google Forms. Individuals were eligible to participate in the study if they were 18 years of age or older, university students, and able to read and complete the online consent form and scales in Turkish. Measures SEOLE Scale The Student Engagement Scale, developed by Sun and Rueda ( 2012 ) and adapted into Turkish by Ergün and Koçak Usluel (2015), was used in this study (Ergün & Usluel, 2015 ). The scale comprises 19 items. It is a 5-point Likert scale, ranging from strongly disagree (1) to strongly agree (5). A minimum score of 19 and a maximum score of 95 can be obtained using this scale. High scores can be interpreted as high student engagement in the online learning environment, whereas low scores can be interpreted as low student engagement. The Cronbach's alpha was calculated as 0.84 (Sun & Rueda, 2012 ). PWB Scale The PWB Scale was developed by Diener et al. ( 2009 ), and Telef ( 2013 ) conducted a Turkish validity and reliability study (Telef, 2013 ). The scale consists of eight items and is used to describe important elements of human functioning, from positive relationships to feelings of competence to having a meaningful and purposeful life. The items of this 7-point Likert-type scale are answered on a 1–7 scale ranging from strongly disagree (1) to strongly agree (7). There are no reverse scored items in the scale. Scores range from a minimum of 8 to a maximum of 56. A high score indicates that the individual to whom the scale is applied has many psychological resources and strengths. In the original scale, Cronbach’s alpha coefficient was calculated as .87 (Diener et al., 2009 ). IU Scale The Intolerance for Uncertainty Scale (ITS-12) was developed by Carleton et al. ( 2007 ) to measure reactions to uncertain situations, and Sarıçam et al. (2014) conducted a Turkish validity and reliability study (Sariçam, 2014 ). The self-reporting scale comprises 12 items. The scale has a 5-point Likert-type structure and ranges from 1 to 5 points from “Not at all suitable for me” to “Completely suitable for me.” The total score that can be obtained from the scale, which does not have a cut-off score, varies between 12 and 60. There are no reverse-coded items in the scale. Higher scores indicate a high level of IU. Cronbach's alpha internal consistency coefficient was .88 (Carleton et al., 2007 ). FC-19 Scale The scale was developed by Ahorsu et al. ( 2020 ) and Bakioğlu et al. ( 2020 ) conducted its Turkish validity and reliability study (Bakioğlu et al., 2020 ). The scale is a 5-point Likert scale consisting of 7 items. There are no reverse items in the scale. The scale is scored from strongly disagree (1) to strongly agree (5). The lowest score was 7, and the highest score was 35. As the scale scores increased, the fear level of the coronavirus increased. The Cronbach’s alpha value of the scale was 0.82 (Ahorsu, et al., 2020 ). Data Analysis The hypothesized mediation model (see Fig. 1 ) was tested in a single model using a bootstrapping approach to assess the significance of the indirect effects at different levels of the moderator (Hayes, 2018 ). In the first and second models, FC-19 was the predictor variable, SEOLE was the outcome variable, and IU was the mediator. In the third and fourth models, FC-19 was the predictor variable, PWB was the outcome variable, and IU was the mediator variable. Demographic variables such as gender, age, class level, and perceived success were controlled in the analysis. The “PROCESS" macro, model 4, v4.2 (Hayes, 2018 ) in SPSS 25 with bias-corrected 95% confidence intervals (n = 5000) was used to assess the significance of the indirect effects. This model implicitly tests the mediation role on the pathway from predictor to outcome. Significant effects are underpinned by the absence of zeros in the confidence intervals, as no zeros should be present in the interval (Preacher & Hayes, 2008 ). Results Testing Common method bias and multicollinearity Exploratory factor analysis (EFA) was performed to test for common method bias. Harman’s single factor test allowed SEOLE, FC-19, PWB, and IU to be entered into the exploratory factor analysis (EFA) procedure, and the results showed that 19.93% variance could be explained by the first factor, which is below the 40% standard (Baumgartner et al., 2021 ). This result indicated that common method bias was not significant. In the next phase, the potential multicollinearity problem was checked using the Variance Inflation Factor (VIF) and Condition Index (CI) values for the independent variables. The VIF values for all independent variables are below 1.6, suggesting a moderate correlation between the variables. In addition, the CI values for all independent variables are below 30. These results reveal that there is no concern regarding multicollinearity in the data (Shrestha, 2020 ). Descriptive statistics and correlation analyses Skewness (ranged − .64 to .49) and kurtosis (ranged − .56 to .59) of all variables showed normal distribution. After this procedure, parametric tests were conducted. Table 1 Information about the variables and relationships among them Variables 1 2 3 4 1. FC-19 ― 2. PWB − .18** ― 3. SEOLE − .15** .36*** ― 4. IU .33*** − .32*** − .19** ― Mean 16.22 41.25 62.61 38.29 Sd 5.41 8.09 11.68 7.80 Skewness .42 − .73 − .18 − .10 Kurtosis − .20 .58 .60 − .50 Cronbach's α .87 .86 .91 .91 * p < .05, ** p < .01, ***p < 0.001 Note. SEOLE = student engagement in online learning environments, PWB = psychological well-being, FC-19 = fear of COVID-19, IU = intolerance of uncertainty. As shown in Table 1, The means of all variables were moderate. According to the correlation analysis results, FC-19 had a significant, negative correlation with PWB and a significant, positive correlation with IU and SEOLE. In addition, PWB had a significant, positive correlation with SEOLE and a significant, negative correlation with IU. Finally, SEOLE and IU were significantly and negatively correlated. Mediation analysis was conducted after this step. Tests of the mediation model M1 and M2: IU mediates the relationship between FC-19 and SEOLE The hypothesized mediation model was tested using the PROCESS macro model number 4, which tests a model in which IU mediates the relationship shown in Figure 1 (Hayes, 2018 ). First, the relationship between FC-19 and SEOLE (b = -0.21, p > 0.5) was not significant; therefore, H1 was not supported. Second, the mediating role of IU on the relationship between FC-19 and SEOLE was tested, and the results revealed a significant indirect effect of FC-19 on SEOLE (b= -0.11, BootLLCI=-.22; BootULCI=-.02), supporting H2. Hence, IU significantly mediated the relationship between FC-19 and SEOLE. Due to the contemporary approach, there is no need for the direct effect; the total effect; the effect of predictor on mediator; or the effect of mediator on outcome variable need to be statistically significant, and bootstrapping should be used to test the significance of the indirect effect (MacKinnon et al., 2002 ; Preacher & Hayes, 2008 ). The bootstrapping results clarified that zero was not within the confidence intervals, indicating that the mediating effect of IU was significant (Hayes, 2018 ). After this step, gender, age, class level, and perceived success entered this model as control variables to check the possible variation in the direct role of FC-19 on SEOLE and the mediating role of IU. Gender, age, class level, and perceived success jointly accounted for significant variation in SEOLE, R-square = .15, F (6,308) = 9.308, p < .001. The direct roles of age (b = .525, s.e.=.168, p < .01), class level (b=-1.37, s.e.=.55, p < .05) and perceived success (b = 4.37, s.e.=.86, p .05). In this model, the relationship between FC-19 and SEOLE was still insignificant (b=-.16, s.e.=.12, p < .05) and the mediating role of IU was still significant (b= -0.09, BootSE = .04, BootLLCI=-.19; BootULCI=-.02), supporting H3 (see Table 2 ). M3 and M4: IU mediates the relationship between FC-19 and PWB In this model, the mediating role of IU in the relationship between FC-19 and PWB was examined. First, the relationship between FC-19 and PWB (b = -0.12, p > 0.5) was not significant, so H4 was not supported. Second, the results revealed a significant indirect role of FC-19 on PWB (b= -0.14, BootLLCI=-.23; BootULCI=-.07). Hence, IU mediated the relationship between FC-19 and PWB (MacKinnon et al., 2002 ; Preacher & Hayes, 2008 ). Since zero was not within the BootCI, this indicated that the mediating role of IU was significant (Hayes, 2018 ), supporting H5. Again, gender, age, class level, and perceived success entered this model as control variables to check the possible change in the direct effect of FC-19 on PWB. Gender, age, class level, and perceived success jointly accounted for significant variation in PWB, R-square = .18, F(6,308) = 11.31, p .05), class level (b = 0.31, s.e.=.37, p > .05), and gender (b=-0.01, s.e.=1.08, p > .05) was not significant. The direct role of perceived success on PWB was the only significant variable (b = 2.58, s.e.=.59, p .05) and the mediating role of IU was also significant (b= -0.12, BootSE = .04, BootLLCI=-.19; BootULCI=-.06). Thus, H6 was supported (see Table 2 ).. Table 2 Models tested in the mediation analysis IV DV: SEOLE DV: PWB Model 1 Model 2 Model 3 Model 4 β (SE) B (SE) B (SE) B(SE) FC-19 -0.21 (0.13) -0.16 (0.12) -0.12 (0.08) -0.09 (0.08) IU -0.18 (0.07*) -0.17 (0.07*) -0.24 (0.47***) -0.21 (0.05***) Age 0.52 (0.17**) 0.12 (0.11) Gender 0.15 (1.58) 0.01 (1.08) Class Levels -1.37 (0.54*) 0.31 (0.37) Perceived Success 4.37 (0.86***) 2.58 (0.59***) R 2 0.04 0.15 0.11 0.18 F 7.04 9.31 19.05 11.31 The mediating effects of IU (before control variables entered the model) DV: SEOLE Indirect Effect Boot SE Boot LLCI Boot ULCI Standardized -0.05 0.02 -0.10 -0.01 Unstandardized -0.11 0.05 -0.22 -0.02 DV: PWB Indirect Effect Boot SE Boot LLCI Boot ULCI Standardized -0.10 0.03 -0.15 -0.05 Unstandardized -0.14 0.04 -0.23 -0.07 The mediating effects of IU (after control variables entered the model) DV: SEOLE Indirect Effect Boot SE Boot LLCI Boot ULCI Standardized -0.04 0.02 -0.09 -0.01 Unstandardized -0.09 0.04 -0.19 -0.02 DV: PWB Indirect Effect Boot SE Boot LLCI Boot ULCI Standardized -0.08 0.02 -0.13 -0.04 Unstandardized -0.12 0.04 -0.19 -0.06 Note: β = unstandardized regression coefficient, IV = independent variables, DV = dependent variables, SE = standard error, LLCI = lower confidence interval, UCLI = lower confidence interval. SEOLE= student engagement in online learning environments, PWB= psychological well-being, FC-19= fear of COVID-19, IU= intolerance of uncertainty. *** p < 0.001, ** p < 0.01, * p < 0.05 Discussion The findings of this study contribute to a better understanding of how FC-19 shapes educational and psychological outcomes. Models 1 and 2 emphasize that FC-19 had a negative relationship with SEOLE and serves as an important mediator of IU. This suggests that students struggling with uncertainty and unpredictable circumstances may find it more difficult to stick to their studies. Some studies support these findings. The transition to online learning exacerbated feelings of IU, leading to decreased SEOLE and increased burnout. Additionally, IU is linked to decreased academic adjustment and engagement in online learning, as students struggle with the unpredictability of the educational environment (Daşcı et al., 2023 ). These findings indicate that complex situations with severe uncertainties, such as COVID-19, make it difficult for students to focus on life, coursework, and assignments in online environments. Therefore, the findings of this study are noteworthy. SEOLE plays a vital role in vague times like COVID-19. One study reports that SEOLE mediates the path of academic grit and well-being, and the relationship between intolerance to uncertainty and well‐being (Kareem et al., 2023 ), while switching to distance learning negatively affects their learning. Student engagement is both a protective factor against uncertainty and a positive contributor to students’ psychological well-being. Increased workload and lack of motivation can cause students to feel stressed and frustrated, which affects the online learning environment, further negatively impacting students’ mental health and engagement levels (Mihai et al., 2022 ). Moreover, learning motivation and school well-being were affected by online learning during the COVID-19 pandemic. There are different challenges for different student populations, depending on technology issues, academic integration, social integration, motivation, and support (Cena et al., 2023 ). For this reason, it is important to understand the difficulties and issues in the online course period and to pay attention to them in future studies. Our findings showed that FC-19 negatively affected SEOLE, and IU mediated this relationship. The above studies support our findings. Models 3 and 4 reveal a parallel pattern for PWB. FC-19 directly undermines well-being, and this relationship is strengthened through IU. Many students have suffered psychological consequences as a result of the COVID-19 pandemic and students with higher IU levels are likely to experience high emotional distress, potentially manifesting as anxiety, depression, emotional burnout, and decreased life satisfaction, which negatively impacts their PWB (Irak et al., 2024 ). A large number of studies support our findings. IU is a significant vulnerability factor linked to various mental health issues, especially during the pandemic (Akbari et al., 2024 ). The findings in the literature clearly demonstrate that IU severely affects PWB. Because it is difficult to cope with states of uncertainty, the aforementioned symptoms arise. These studies provide supporting evidence of our findings. Clarification on the nature of mediation is crucial when examining the relationships detailed in this manuscript, notably the mediation of intolerance of uncertainty (IU) in models relating to FC-19, SEOLE and PWB. The FC-19's direct influence on SEOLE and PWB did not prove to be statistically significant, however mediation via IU was found to be substantial and is in line with the existing mediation analysis frameworks proposed by Hayes (Putri & Etikariena, 2020 ). This pattern of results is consistent with the concept of full mediation as defined by Hayes ( 2018 ), which occurs when the effect of an independent variable on a dependent variable is completely mediated by a third variable. In essence, the fear of COVID-19 does not have a substantial impact on student engagement or psychological well-being by itself; however, its effects become apparent when it exacerbates intolerance of uncertainty. IU acts as a key intermediary, facilitating the impact of the fear of COVID-19 on both educational and psychological outcomes. The significance of addressing intolerance of uncertainty as a focal point for intervention is underscored by abovementioned results. Given that students with higher levels of intolerance of uncertainty may experience greater difficulty in adjusting to online learning and preserving their psychological well-being, interventions should target developing resilience, adaptability, and the ability to cope with uncertainty. Structured uncertainty tolerance training and stress-management programs may alleviate the negative consequences of COVID-19-related anxiety by diminishing instances of learned helplessness. Reducing the fear of COVID-19 alone is unlikely to be sufficient to enhance engagement and well-being when the underlying issue of IU is not being addressed. Educational establishments and mental health practitioners should take into account holistic strategies that concentrate on both minimizing excessive anxiety and assisting students in coping with uncertainty. Future studies could investigate whether certain coping techniques, including mindfulness or cognitive-behavioral methods, can efficiently influence the pathway involving IU. Study Limitations Despite its contributions, this study has several limitations that need to be considered. First, this study's application of cross-sectional mediation analysis is consistent with previous research, but also illustrates a wider pattern of over-reliance on this method in psychological studies (Maxwell et al., 2011 ). The hypothesized mediation model has a theoretical basis, but its, the cross-sectional design limits the ability to deduce causal relationships between FC-19, IU, and dependent variables. Future research should focus on employing either longitudinal or experimental methodologies to corroborate these results. Second, reliance on self-reported measures may lead to biases such as social desirability or recall errors. While validated scales were used, future research could include objective behavioral assessments or qualitative methods to triangulate findings. Third, although the sample is diverse, it is limited to students at specific academic institutions, potentially limiting the generalizability of the results to broader populations or groups of students in other countries. Expanding the study to include different geographical and cultural contexts may provide a more holistic understanding of these dynamics. Moreover, the study's reliance on snowball sampling and the disproportionate representation of female participants (79%) may compromise the applicability of the results to a wider range of populations, including male students and individuals from diverse cultural backgrounds. The use of a convenience sample of Turkish students limits the generalizability of the study's findings. Future research should strive to recruit samples that are more diverse and representative, encompassing equal gender representations, multiple educational levels, and individuals from a range of cultural and geographical backgrounds. Utilizing probability-based sampling techniques, such as stratified random sampling, combined with a priori power analyses will increase the robustness and broad applicability of research results. Fourth, unmeasured variables such as pre-existing mental health, socioeconomic status, and access to technology may have influenced the results. In future research, controlling these factors could improve the robustness of the findings. Finally, this study focused only on the mediating role of IU. Investigating additional mediators, such as social support and coping mechanisms, may provide a more holistic view of the factors influencing student engagement and PWB during crises. Last, The PROCESS macro is most suitable for straightforward mediation models, but it was not created to assess model fit or include hidden variables. The drawback of this research lies in its dependence on the PROCESS macro for mediation analysis, which fails to consider latent constructs, measurement errors, or model fit indices. Future studies should utilize structural equation modelling to rigorously examine the proposed relationships, incorporating latent variables and examining model fit metrics. These approaches will increase the reliability of outcomes and offer more in-depth understanding of the mechanisms relating FC-19, IU, PWB, and SEOLE. Implications and Recommendations Given the complex relationships among IU, SEOLE, PWB, and FC-19, educational institutions should take a holistic approach to supporting students during these challenging times. Interventions focused on reducing IU, such as cognitive-behavioral strategies, can improve students’ coping mechanisms and encourage engagement in online learning (Andrade et al., 2020 ). Furthermore, fostering a supportive online community can reduce feelings of isolation and improve students’ PWB (Hilton et al., 2023 ). Additionally, addressing students’ fears related to COVID-19 through transparent communication and mental health resources can help alleviate IU and promote a more conducive learning environment. From another perspective, exploring other potential mediators or moderators may further enrich the findings. Internet and social media addiction (Ismail et al., 2020 ), and the level of doomscrolling (Anlı, 2023 ) all increased during the pandemic period. The potential impacts of these concepts directly related to the online period on SEOLE and PWB are important to investigate. Future research should continue to explore the nuanced relationships between these variables, with an emphasis on longitudinal studies that can capture the changing nature of students' experiences in online learning contexts. Interventions that enhance coping mechanisms, promote engagement, and address psychological needs may reduce the negative effects of these factors and eventually promote better PWB among students. For example, psychological and cognitive resilience as well as coping strategies can prevent the negative effects of IU and increase students’ PWB. Psychological resilience promotes better mental health outcomes by reducing the negative effects of IU, promoting better mental health outcomes (Daşcı et al., 2023 ). Some studies have suggested that promoting psychological resilience and mindfulness can improve SEOLE (Irak et al., 2024 ); thus, individual and group therapy or psychoeducational intervention studies can be conducted to increase the PWB and coping skills of students during drastic times like COVID-19. The effects of these variables on SEOLE and PWB can be examined to understand these concepts in depth. The concrete and detailed intervention suggestions are listed below. Mindfulness Training Evidence from numerous studies suggests that mindfulness practices like meditation and focused breathing can greatly enhance psychological flexibility and emotional regulation, skills that are particularly important in navigating uncertain situations (Khatami et al., 2023 ). Practicing mindfulness not only boosts self-awareness but also minimizes anxiety linked to uncertain circumstances, thereby promoting increased involvement and overall well-being. Structured programs for students that incorporate regular mindfulness exercises can offer a structured framework for students to learn and integrate these techniques into their daily lives (Edmundson & Jenson, 2022 ). Structured Uncertainty Tolerance Workshops By focusing educational modules on enhancing Individuals' Understanding, these workshops can effectively provide students with strategies to manage ambiguity and unpredictability. These workshops may use real-life scenario training to mimic uncertain situations, enabling students to hone their coping skills in a secure setting. Case discussions and role-playing scenarios about health uncertainties during the Covid-19 pandemic can aid students in developing resilience by simulating decision-making under uncertainty, thereby enhancing their participation in learning activities and overall mental well-being (Reis-Dennis et al., 2021 ). Coping Strategies and Communication Training Training programs that emphasize communication skills create a setting in which students feel more at ease articulating their doubts and worries. This process may entail peer-to-peer conversations, where students are encouraged to exchange their personal experiences and proposed solutions related to uncertainty in academic environments. Normalizing uncertainties through open conversation can help reduce associated stigma (Fei et al., 2024 ). Parent and Community Involvement Strategies Incorporating conversations with families about uncertainty and resilience can enhance educational programs. Workshops aimed at families would provide them with information to help their children manage IU and distress, thereby establishing a comprehensive support network for students, which would improve their ability to cope with uncertain situations (Karamushka et al., 2021 ). Peer Support Programs Establishing peer support groups for students to discuss their experiences with Covid-19 and academic concerns may foster resilience. Through peer mentoring, students not only provide mutual support but also acquire varied viewpoints on tackling uncertain circumstances. Research has demonstrated that this approach can help alleviate distress related to uncertainty, ultimately leading to improved well-being and engagement (Leisterer et al., 2021 ). Conclusion The COVID-19 pandemic has highlighted the pivotal interplay between psychological constructs such as IU and fear and their impact on student engagement and wellbeing in online learning environments. Although FC-19 poses significant threats to students’ PWB, engagement in online learning can serve as a crucial buffer. IU may promote academic achievement and psychological resilience by reducing the negative effects of pandemic-related fear by developing coping skills. Understanding these dynamics is fundamental for designing impactful strategies to support students during and after the pandemic. Declarations Compliance with Ethical Standards Funding The study has its’ own funding. Authors’ contributions The author created the idea for this study and developed the protocol, methods and the data analysis and wrote the manuscript by himself. Conflict of interest The contents of this manuscript have not been copyrighted or published previously and not now under consideration for publication elsewhere. The author declares that he has no conflict of interest. Ethical approval All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This study was reviewed and approved by the Ethics Committee of Bursa Technical University (Protocol no: 19.02.2021-E.847). Clinical trial number: not applicable. Informed consent Informed consent was obtained from all individual participants included in the study. Data availability The datasets generated by the survey research during and/or analyzed during the current study are available in the Mendeley repository, https://doi.org/ 10.17632/c3rrw96d7h.1” Clinical trial number Not applicable. Consent to publish declaration Not applicable Consent to participate declaration Not applicable References Ahorsu, D. K., Imani, V., Lin, C.-Y., Timpka, T., Broström, A., Updegraff, J. A., Årestedt, K., Griffiths, M. D., & Pakpour, A. H. (2020). Associations between fear of COVID-19, mental health, and preventive behaviours across pregnant women and husbands: An actor-partner interdependence modelling. International Journal of Mental Health and Addiction , 1–15. Ahorsu, D. K., Lin, C., Alimoradi, Z., Griffiths, M. D., Chen, H.-P., Broström, A., Timpka, T., & Pakpour, A. H. (2022). Cyberchondria, Fear of COVID-19, and Risk Perception Mediate the Association Between Problematic Social Media Use and Intention to Get a COVID-19 Vaccine. Vaccines , 10 (1), 122. https://doi.org/10.3390/vaccines10010122 Akbari, M., Seydavi, M., Zamani, E., Jamshidi, S., & Freeston, M. H. (2024). Intolerance of Uncertainty as a Situational Vulnerability Factor in the Context of the Pandemic: A Systematic Review and Meta‐Analysis of COVID‐19‐Related Psychological Impacts. Clinical Psychology & Psychotherapy , 31 (5), e3046. An, R., Addas, A., Rehman, N., & Rehman, S. (2024). Predicting online learning experiences in sports education during the COVID-19 pandemic: Implications for pedagogical strategies. Heliyon , 10 (17). Andrade, E. F., Pereira, L. J., Ana Paula Luiz de Oliveira, Orlando, D. R., Alves, D. A. G., Guilarducci, J. d. S., & Castelo, P. M. (2020). Perceived Fear of COVID-19 Infection According to Sex, Age and Occupational Risk Using the Brazilian Version of the Fear of COVID-19 Scale. Death Studies , 46 (3), 533–542. https://doi.org/10.1080/07481187.2020.1809786 Anlı, G. (2023). Felaket Haberlerini Kaydırma Ölçeği Türkçe formunun geçerlik ve güvenirlik çalışması. Dokuz Eylül Üniversitesi Sosyal Bilimler Enstitüsü Dergisi , 25 (1), 301–316. Aristovnik, A., Keržič, D., Ravšelj, D., Tomaževič, N., & Umek, L. (2020). Impacts of the COVID-19 pandemic on life of higher education students: A global perspective. Sustainability , 12 (20), 8438. Bakioğlu, F., Korkmaz, O., & Ercan, H. (2020). Fear of COVID-19 and Positivity: Mediating Role of Intolerance of Uncertainty, Depression, Anxiety, and Stress. International Journal of Mental Health and Addiction , 19 (6), 2369–2382. https://doi.org/10.1007/s11469-020-00331-y Baumgartner, H., Weijters, B., & Pieters, R. (2021). The biasing effect of common method variance: Some clarifications. Journal of the Academy of Marketing Science , 49 , 221–235. Capone, R., & Lepore, M. (2022). From distance learning to integrated digital learning: A fuzzy cognitive analysis focused on engagement, motivation, and participation during COVID-19 pandemic. Technology, Knowledge and Learning , 27 (4), 1259–1289. Carleton, R. N. (2016). Into the unknown: A review and synthesis of contemporary models involving uncertainty. Journal of Anxiety Disorders , 39 , 30–43. Carleton, R. N., Norton, M. P. J., & Asmundson, G. J. (2007). Fearing the unknown: A short version of the Intolerance of Uncertainty Scale. Journal of Anxiety Disorders , 21 (1), 105–117. Carlson, K. D., & Wu, J. (2012). The ıllusion of statistical control: Control variable practice in management research. Organizational Research Methods , 15 (3), 413–435. https://doi.org/10.1177/1094428111428817 Cena, E., Toner, P., McParland, A., Burns, S., & Dudgeon, K. (2023). Studying and learning psychology during the COVID-19 pandemic: A mixed-methods approach on students’ perspectives of psychological well-being and adjustment to studying online. Psychology Learning & Teaching , 22 (2), 137–158. Cohen, J. (2013). Statistical Power Analysis for the Behavioral Sciences (0 ed.). Routledge. https://doi.org/10.4324/9780203771587 Crawford, J., Butler-Henderson, K., Rudolph, J., Malkawi, B., Glowatz, M., Burton, R., Magni, P., & Lam, S. (2020). COVID-19: 20 countries’ higher education intra-period digital pedagogy responses . Daşcı, E., Salihoğlu, K., & Daşcı, E. (2023). The relationship between tolerance for uncertainty and academic adjustment: The mediating role of students’ psychological flexibility during COVID-19. Frontiers in Psychology , 14 , 1272205. Deci, E. L., & Ryan, R. M. (2013). Intrinsic motivation and self-determination in human behavior . Springer Science & Business Media. Diener, E., Napa Scollon, C., & Lucas, R. E. (2009). The evolving concept of subjective well-being: The multifaceted nature of happiness. Assessing Well-Being: The Collected Works of Ed Diener , 67–100. Doğanülkü, H. A., Korkmaz, O., Griffiths, M. D., & Pakpour, A. H. (2021). Fear of COVID-19 Lead to Procrastination Among Turkish University Students: The Mediating Role of Intolerance of Uncertainty. BMC Psychology , 9 (1). https://doi.org/10.1186/s40359-021-00681-9 Edmundson, J., & Jenson, J. (2022). MakerMinds: An Exploratory Study of Making and Mindfulness Pedagogies. Canadian Journal of Education / Revue Canadienne De L Éducation , 45 (2), 481–511. https://doi.org/10.53967/cje-rce.v45i2.4959 Ergün, E., & Usluel, Y. K. (2015). Çevrimiçi öğrenme ortamlarında öğrenci bağlılık ölçeği’nin türkçe uyarlaması: Geçerlik ve güvenirlik çalışması. EĞİTİM TEKNOLOJİSİ Kuram ve Uygulama , 5 (1), 20–33. https://doi.org/10.17943/etku.64661 Fei, D., Liu, Y., & Zhang, W. (2024). The Paradox of Uncertainty: How Tolerance for Uncertainty Modulates Approach Motivation—Evidence From ERPs Experiments . https://doi.org/10.21203/rs.3.rs-4229168/v1 Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research , 74 (1), 59–109. Gonzalez-Ramirez, J., Mulqueen, K., Zealand, R., Silverstein, S., Mulqueen, C., & BuShell, S. (2021). Emergency online learning: College students’ perceptions during the COVID-19 pandemic. College Student Journal , 55 (1), 29–46. Hayes, A. F. (2018). Partial, conditional, and moderated moderated mediation: Quantification, inference, and interpretation. Communication Monographs , 85 (1), 4–40. Hilton, B. T., Wiskur, B., Brand, M., Rojas, J. I., LCSW, R. D. L., Trapp, M., & Kuhn, K. G. (2023). Fear of COVID-19 Among Nonsmokers and Smokers/Former Smokers: Implications for Health Promotion Practice. Journal of Emergency Management , 21 (7), 307–313. https://doi.org/10.5055/jem.0764 Huda, M. (2023). Towards digital access during pandemic age: Better learning service or adaptation struggling? Foresight , 25 (1), 82–107. Hyland, P., Shevlin, M., McBride, O., Murphy, J., Karatzias, T., Bentall, R. P., Martinez, A., & Vallières, F. (2020). Anxiety and depression in the Republic of Ireland during the COVID‐19 pandemic. Acta Psychiatrica Scandinavica , 142 (3), 249–256. Irak, D. U., Dede, B., & Demir, N. (2024). Mindfulness during the COVID-19 pandemic lockdowns: Intolerance uncertainty and psychological well-being among employees. Sage Open , 14 (2), 21582440241251478. https://doi.org/10.1177/21582440241251478 Ismail, S. J. I., Kusnandar, T., Sanovia, Y., Mayasari, R., Negara, R. M., & Mahayana, D. (2020). Study of internet and social media addiction in Indonesia during COVID-19. IJAIT (International Journal of Applied Information Technology) , 69–80. Karamushka, L., Терещенко, К., Креденцер, О., Ivkin, V., Lagodzinska, V., & Kovalchuk, O. (2021). Tolerance of Uncertainty as a Determinant of Educators’ Psychological Health in Conditions of Social Tension. Організаційна Психологія Економічна Психологія , 4 (24), 30–38. https://doi.org/10.31108/2.2021.4.24.4 Kareem, J., Thomas, S., Kumar P., A., & Neelakantan, M. (2023). The role of classroom engagement on academic grit, intolerance to uncertainty and well‐being among school students during the second wave of the COVID‐19 pandemic in India. Psychology in the Schools , 60 (5), 1594–1608. https://doi.org/10.1002/pits.22758 Khatami, M., Shayerey, A. J., Behera, S. K., He, D., & Haji-Othman, N. A. (2023). Exploring Bangladeshi EFL Learners’ Mindfulness and Engagement in Online Learning Context . https://doi.org/10.21203/rs.3.rs-3356992/v1 Lai, A. Y., Lee, L., Wang, M., Feng, Y., Lai, T. T., Ho, L., Lam, V. S., Ip, M. S., & Lam, T. (2020). Mental health impacts of the COVID-19 pandemic on international university students, related stressors, and coping strategies. Frontiers in Psychiatry , 11 , 584240. Lakens, D. (2022). Sample Size Justification. Collabra: Psychology , 8 (1), 33267. https://doi.org/10.1525/collabra.33267 Leisterer, S., Lautenbach, F., Walter, N., Kronenberg, L., & Elbe, A. (2021). Development of a Salutogenesis Workshop for SPPs to Help Them, Their Athletes, and the Athlete’s Entourage Better Cope with Uncertainty During the COVID-19 Pandemic. Frontiers in Psychology , 12 . https://doi.org/10.3389/fpsyg.2021.612264 MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., & Sheets, V. (2002). A comparison of methods to test mediation and other intervening variable effects. Psychological Methods , 7 (1), 83–104. https://doi.org/10.1037/1082-989X.7.1.83 Malakcioglu, C. (2024). Emotional loneliness, perceived stress, and academic burnout of medical students after the COVID-19 pandemic. Frontiers in Psychology , 15 , 1370845. Maxwell, S. E., Cole, D. A., & Mitchell, M. A. (2011). Bias in cross-sectional analyses of longitudinal mediation: Partial and complete mediation under an autoregressive model. Multivariate Behavioral Research , 46 (5), 816–841. Mazza, M., Marano, G., Lai, C., Janiri, L., & Sani, G. (2020). Danger in danger: Interpersonal violence during COVID-19 quarantine. Psychiatry Research , 289 , 113046. Mihai, M., Albert, C. N., Mihai, V. C., & Dumitras, D. E. (2022). Emotional and social engagement in the English language classroom for higher education students in the COVID-19 online context. Sustainability , 14 (8), 4527. Mok, K. H., Xiong, W., & Ye, H. (2021). COVID-19 crisis and challenges for graduate employment in Taiwan, Mainland China and East Asia: A critical review of skills preparing students for uncertain futures. Journal of Education and Work , 34 (3), 247–261. Moussa, M., Moussa, F. L., Alharbi, H., Omer, T., Sofiany, H. A., Almuzaini, T. M., Alsaady, E. S., & Alrashede, S. M. (2022). Association of Fear of COVID-19 and Health Literacy Among the General Public in Saudi Arabia: A Cross-Sectional Assessment. Frontiers in Public Health , 9 . https://doi.org/10.3389/fpubh.2021.740625 Oral, M., & Karakurt, N. (2022). The Impact of Psychological Hardiness on Intolerance of Uncertainty in University Students During the COVID‐19 Pandemic. Journal of Community Psychology , 50 (8), 3574–3589. https://doi.org/10.1002/jcop.22856 Padrón, I., Fraga, I., Vieitez, L., Montes, C., & Romero, E. (2021). A study on the psychological wound of COVID-19 in university students. Frontiers in Psychology , 12 , 589927. Pakpour, A. H., Liu, C. H., Hou, W.-L., Chen, Y. P., Li, Y. P., Kuo, Y. J., Lin, C. Y., & Scarf, D. (2021). Comparing Fear of COVID-19 and Preventive COVID-19 Infection Behaviors Between Iranian and Taiwanese Older People: Early Reaction May Be a Key. Frontiers in Public Health , 9 . https://doi.org/10.3389/fpubh.2021.740333 Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods , 40 (3), 879–891. Putri, H. E., & Etikariena, A. (2020). The Role of the Work-Family Conflict in the Relationship Between Work-Family Culture and Employee Well-Being. Jurnal Psikologi , 19 (3), 257–268. https://doi.org/10.14710/jp.19.3.257-268 Radanović, A., Micić, I., Pavlović, S., & Krstić, K. (2021). Don’t think that kids aren’t noticing: Indirect pathways to children’s fear of COVID-19. Frontiers in Psychology , 12 , 635952. https://doi.org/10.3389/fpsyg.2021.635952 Reis‐Dennis, S., Gerrity, M., & Geller, G. (2021). Tolerance for Uncertainty and Professional Development: A Normative Analysis. Journal of General Internal Medicine , 36 (8), 2408–2413. https://doi.org/10.1007/s11606-020-06538-y Salari, N., Khazaie, H., Hosseinian-Far, A., Khaledi-Paveh, B., Kazeminia, M., Mohammadi, M., Shohaimi, S., Daneshkhah, A., & Eskandari, S. (2020). The prevalence of stress, anxiety and depression within front-line healthcare workers caring for COVID-19 patients: A systematic review and meta-regression. Human Resources for Health , 18 , 1–14. Sariçam, H. (2014). Belirsizliğe tahammülsüzlük ölçeği (BTÖ-12) Türkçe formu: Geçerlik ve güvenirlik çalişmasi. Route Educational and Social Science Journal , 1 (3), 148–148. https://doi.org/10.17121/ressjournal.109 Satıcı, S. A., Kayiş, A. R., Satıcı, B., Griffiths, M. D., & Can, G. (2020). Resilience, Hope, and Subjective Happiness Among the Turkish Population: Fear of COVID-19 as a Mediator. International Journal of Mental Health and Addiction , 21 (2), 803–818. https://doi.org/10.1007/s11469-020-00443-5 Seçer, İ., & Ulaş, S. (2020). The mediator role of academic resilience in the relationship of anxiety sensitivity, social and adaptive functioning, and school refusal with school attachment in high school students. Frontiers in Psychology , 11 , 557. Shi, J., Ma, G., Liu, X., & Jin, Y. (2024). Social isolation and adolescents’ psychological wellbeing during the COVID-19 epidemic: The mediating effect of loneliness and the moderating effect of resilience. Current Psychology , 43 (19), 17163–17174. https://doi.org/10.1007/s12144-024-05637-x Shrestha, N. (2020). Detecting multicollinearity in regression analysis. American Journal of Applied Mathematics and Statistics , 8 (2), 39–42. Son, C., Hegde, S., Smith, A., Wang, X., & Sasangohar, F. (2020). Effects of COVID-19 on college students’ mental health in the United States: Interview survey study. Journal of Medical Internet Research , 22 (9), e21279. Sun, J. C., & Rueda, R. (2012). Situational interest, computer self‐efficacy and self‐regulation: Their impact on student engagement in distance education. British Journal of Educational Technology , 43 (2), 191–204. Telef, B. B. (2013). Psikolojik iyi oluş ölçeği: Türkçeye uyarlama, geçerlik ve güvenirlik çalışması. Hacettepe Üniversitesi Eğitim Fakültesi Dergisi , 28 (28–3), 374–384. Wan, P., Hu, J., Su, C., & Li, Q. (2024). Impact of intolerance of uncertainty on depression in college students during the COVID-19 pandemic: Coping strategies as mediators. INQUIRY: The Journal of Health Care Organization, Provision, and Financing , 61 , 00469580241273119. Yang, Y., & Wang, H. (2023). Fear of COVID-19 and Anxiety: Serial mediation by trust in the government and hope. Psychology Research and Behavior Management , Volume 16 , 963–970. https://doi.org/10.2147/prbm.s399466 Zarić, R. Ž., Zarić, M., Čanović, P., Janković, S. M., Stojadinovic, M., Zornić, N., Nešić, J., Spasić, M., Jovanović, D., Jug, M., Jakovljevic, S., & Pejčić, A. V. (2022). Validation of the Fear of COVID-19 Scale in a Central Balkan Country—Serbia. Frontiers in Public Health , 10 . https://doi.org/10.3389/fpubh.2022.972668 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5968746","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":440324619,"identity":"7cdaaa2e-840e-4164-99dd-167ae939cfad","order_by":0,"name":"Gazanfer ANLI","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIie2OMWsCMRSAI0JcolkjB/UvvEMoHIqrf+OVA7tIdXQQCQhdXRXEn+H85EAXuxd0CYWb3ApFqKDxFLdYxw75huTxwscXxjyef0s9O3OEPcom9bfSulyEa1JXBR5Ucu8PKJXmynx3sd5hhQ8iM9sOgPKLjWCHjksJ1xgHY2xFWrwhvcxTBcTjmmAQaZeikYLiPrH/aINVEquI58Aqzp+FIzP8FXgEJndWmZ4V+XNXqaiYBwIJmDpXdFbhdxVQKa8JjIErW8FlWp4kvBpNoequjF6/NgIbIGU7NPv+VpZWQ/O56z25K3Qd+G2Vz/YuwVa0+83j8Xg8F04PKFM342w6LgAAAABJRU5ErkJggg==","orcid":"","institution":"Bursa Technical University","correspondingAuthor":true,"prefix":"","firstName":"Gazanfer","middleName":"","lastName":"ANLI","suffix":""}],"badges":[],"createdAt":"2025-02-05 22:38:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5968746/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5968746/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80323535,"identity":"79e3940d-1405-4c3c-92be-abb18320de24","added_by":"auto","created_at":"2025-04-10 13:53:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":48746,"visible":true,"origin":"","legend":"\u003cp\u003eTheoretical models for investigating the mediating role of IU in the relationship between FC-19 and SEOLE, FC-19 and PWB.\u003c/p\u003e\n\u003cp\u003eNote. SEOLE= student engagement in online learning environments, PWB= psychological well-being, FC-19= fear of COVID-19, IU= intolerance of uncertainty.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5968746/v1/35ade0ecc6917e63f8e65ad3.png"},{"id":94043600,"identity":"0a50d300-0587-4c4d-9f2d-0ae3fe59eac0","added_by":"auto","created_at":"2025-10-21 19:46:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1162817,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5968746/v1/b32e15d6-24cd-439b-87e8-86b342848d76.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigating the Relationship Among Fear of COVID-19, Student Engagements in Online Learning Environment and Psychological Well-Being: The Mediating Role of Intolerance of Uncertainty","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe COVID-19 pandemic has led to unprecedented changes in educational systems worldwide, necessitating a rapid transition to online learning. This shift has not only reshaped pedagogical practices but has also dramatically shaped students\u0026rsquo; psychological states (Crawford et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In the midst of this transition, fear of COVID-19 (FC-19) emerged as a major psychological burden. As an emotional reaction to the threat of the virus, this fear has consequences not only for public health but also for individual mental health and educational outcomes (Ahorsu, et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFC-19 is particularly pertinent in academic contexts where students must adjust to new learning modalities while handling increased levels of stress and uncertainty. Research has revealed that FC-19 can lead to negative psychological consequences such as increased anxiety, depression, and decreased engagement in academic tasks (Aristovnik et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). FC-19 was characterized by constant worry about infection and its outcomes, further complicating these dynamics, potentially exacerbating intolerance of uncertainty (IU) and negatively impacting students\u0026rsquo; PWB (Zarić et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIntolerance of uncertainty (IU), characterized by an inability to withstand the unknown, has been associated with increased emotional distress and maladaptive coping behaviors (Carleton et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). IU has been recognized as a key factor influencing students\u0026rsquo; engagement in the online learning environment (SEOLE) and their mental well-being during this period (Doğan\u0026uuml;lk\u0026uuml; et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Previous studies have emphasized that individuals with higher IU levels are more likely to experience deteriorated routines and reduced mental health during crises (Satıcı et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, an examination of its role as a mediator can provide valuable insights into how FC-19 translates into specific student outcomes.\u003c/p\u003e \u003cp\u003eStudents\u0026rsquo; engagement, defined as the level of attention, enthusiasm and participation in academic activities, is a vital indicator of academic achievement (Fredricks et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The sharp transition to online learning has posed challenges in sustained engagement, with students reporting diminished motivation and distraction (Gonzalez-Ramirez et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Understanding how FC-19 and IU affect engagement is pivotal to designing effective educational interventions.\u003c/p\u003e \u003cp\u003ePWB, which encompasses emotional stability, positive relationships, and sense of purpose, is another critical area of concern during the pandemic. The widespread fear and uncertainty associated with COVID-19 has been shown to weaken well-being, leading to heightened rates of anxiety, loneliness, and fatigue among students (Shi et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). By investigating the mediating role of IU, this study illuminates the pathways through which FC-19 affects psychological health and draw implications for mental health support interventions. To ensure a rigorous analysis, the study incorporates age, gender, class level, and perceived success as control variables. These factors influence both educational and psychological outcomes, adding contextual nuance to the findings.\u003c/p\u003e"},{"header":"Theoretical Framework","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eFC-19 and PWB\u003c/h2\u003e \u003cp\u003eFC-19 has emerged as an important psychological construct that affects multiple aspects of individuals\u0026rsquo; lives, including their mental health and engagement in supportive behaviors. Research has demonstrated that FC-19 is correlated with elevated levels of anxiety and stress among students, which may aggravate IU (Pakpour et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Fear of being contaminated with the virus or infecting loved ones can increase IU by creating a widespread sense of uncertainty, which negatively impacts students\u0026rsquo; ability to fully engage in their studies (Yang \u0026amp; Wang, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This relationship emphasizes the need to address FC-19 as a critical determinant in the context of student engagement and PWB. SEOLE will be represented in the following section.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSEOLE\u003c/h3\u003e\n\u003cp\u003eEngagement in online learning includes active participation in educational activities, emotional investment, and cognitive engagement (Capone \u0026amp; Lepore, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). According to Self-Determination Theory (SDT), meeting basic psychological needs for competence, relatedness, and autonomy increases motivation and well-being (Deci \u0026amp; Ryan, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). By promoting these factors, SEOLE can act as a buffer against the negative effects of pandemic-related fear.\u003c/p\u003e \u003cp\u003eThe shift to online learning has necessitated a reevaluation of student engagement strategies. SEOLE is crucial for academic success and PWB. Nevertheless, students with high IU may struggle with engagement due to anxiety and fear of the unknown, which can lead to diminished motivation and engagement (Oral \u0026amp; Karakurt, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Besides, the absence of face-to-face interaction in online environments may intensify feelings of isolation and disengagement. Research has revealed that meeting basic psychological needs is essential for increasing SEOLE, when these needs are not satisfied, students may seek maladaptive coping mechanisms (An et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Consequently, the promotion of an engaging online learning environment that addresses psychological needs is essential for enhancing students\u0026rsquo; wellbeing. The following section will describe the interplay between IU and SEOLE.\u003c/p\u003e\n\u003ch3\u003eIU and SEOLE\u003c/h3\u003e\n\u003cp\u003eIU has been associated with numerous maladaptive outcomes, such as anxiety and procrastination, especially among university students (Doğan\u0026uuml;lk\u0026uuml; et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Research suggests that higher IU levels are associated with decreased SEOLE because students may struggle to adapt to the unpredictability of online classrooms (Huda, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This is especially evident in the context of the COVID-19 pandemic, where uncertainties about health, academic performance, and future employment prospects have intensified students\u0026rsquo; IU (Mok et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As a result, students with high IU may experience difficulties in maintaining their motivation and engagement in their studies, which may lead to poorer academic outcomes. We next examine the relations of IU and PWB in the following section.\u003c/p\u003e\n\u003ch3\u003eIU and PWB\u003c/h3\u003e\n\u003cp\u003eIU is a psychological construct that refers to an individual's difficulty in accepting the unknown or ambiguous situations. Research has demonstrated that higher IU levels are related to increased anxiety, depression, and stress, especially in unpredictable environments such as COVID-19 (Oral \u0026amp; Karakurt, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Individuals with high IU often perceive uncertain situations as more threatening, which may intensify psychological distress and inhibit effective coping strategies (Satıcı et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This maladaptive response can result in a cycle of negative feelings and actions, further reducing PWB. Furthermore, the pandemic has intensified feelings of uncertainty, resulting in increased psychological distress between students (Padr\u0026oacute;n et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). FC-19 has been shown to mediate the associations between IU and mental health outcomes, indicating that as fear increases, so does the impact of IU on PWB (Satıcı et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This interplay highlights the importance of addressing IU in therapeutic settings, especially for students navigating the challenges of online learning. In the next section, PWB in online learning contexts will be addressed.\u003c/p\u003e\n\u003ch3\u003ePWB in Online Learning Contexts\u003c/h3\u003e\n\u003cp\u003eThe PWB of students is fundamental for effective learning and engagement. Students\u0026rsquo; mental health has degraded during the pandemic, with heightened reports of anxiety, depression, and stress, thus, the transition to online learning has been associated with feelings of isolation and disconnection, further exacerbating psychological distress (Ahorsu et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Importantly, IU has been found to be a predictor of psychological distress, suggesting that students with higher IU may be more sensitive to negative mental health outcomes during this period (Huda, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This relationship underscores the importance of incorporating IU into interventions that promote student well-being in online learning settings.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRationale of the Study\u003c/h2\u003e \u003cp\u003eThe relationship between FC-19, IU, student engagement, and PWB is supported by empirical evidence. Studies have revealed that students with high levels of FC-19 experience greater difficulties in navigating uncertainty, which in turn affects their engagement in online learning (Moussa et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This suggests that FC-19 may generate a feedback cycle that further reduces PWB by increasing the negative effects of IU on student engagement contributing to heightened stress, anxiety, and depression (Salari et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This fear can result in avoidant behaviors, social isolation, and decreased resilience, specifically in vulnerable populations such as students struggling with academic and social challenges (Se\u0026ccedil;er \u0026amp; Ulaş, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). IU exasperates the impact of fear on psychological outcomes by increasing perceived threats and weakening coping strategies. Individuals with high IU levels are more prone to anxiety in uncertain circumstances, such as during the COVID-19 pandemic. (Akbari et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For these reasons, we predicted that IU would both mediate the relationships between FC-19 and SEOLE; FC-19 and PWB.\u003c/p\u003e \u003cp\u003eControl variables are used to determine whether the relationship between two variables is real or due to a chance association with other related variables and employed to eliminate \u0026ldquo;nuisance\u0026rdquo; variance in research (Carlson \u0026amp; Wu, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Gender, age, class level, and perceived success were selected as control variables in this study. The change in the relationship between FC-19 and PWB and FC-19 and SEOLE is examined when IU is the mediator and gender, age, class level, and perceived success were controlled. The models and hypotheses are listed below.\u003c/p\u003e \u003cp\u003eMaxwell et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) advise against overreliance on cross-sectional mediation studies but note that well-structured mediation models can still provide valuable insights when grounded in robust theoretical foundations (Maxwell et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The proposed pathways (FC-19 \u0026rarr; IU \u0026rarr; SEOLE/PWB) are consistent with existing theories, notably Carleton's (2016) Uncertainty Distress Model, which suggests that fear-causing events amplify IU, resulting in negative psychological and behavioral consequences (Carleton, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Studies based on empirical research during the COVID-19 pandemic, such as those conducted by Satıcı et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Doğan\u0026uuml;lk\u0026uuml; et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), have also shown similar directional connections, which provide a basis for our proposed mediation model.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eModels 1 and 2: FC-19, IU, and SEOLE\u003c/h3\u003e\n\u003cp\u003eFC-19 is a pervasive emotional response that can impair students\u0026rsquo; ability to focus and engage in online learning. IU, a dispositional characteristic reflecting discomfort with ambiguity, may exacerbate this relationship by increasing stress and reducing cognitive resources needed for engagement. Student engagement, which is defined as the degree of attention, curiosity and involvement in educational activities, is a vital indicator of learning outcomes in online contexts.\u003c/p\u003e \u003cp\u003e \u003cem\u003eHypotheses for Models 1 and 2\u003c/em\u003e:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eFC-19 is associated with SEOLE.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIU mediates the relationship between FC-19 and SEOLE.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe mediating role of IU is still significant when age, gender, class level, and perceived success are controlled.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e\n\u003ch3\u003eModels 3 and 4: FC-19, IU, and PWB\u003c/h3\u003e\n\u003cp\u003eFC-19 can substantially deteriorate PWB, which is characterized as a domain of mental health comprising emotional equilibrium, positive relationships, and a sense of purpose. As a cognitive-emotional component, IU may serve as a mediating factor by increasing negative emotional reactions and diminishing adaptive coping skills.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eHypotheses for Models 3 and 4:\u003c/h2\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eFC-19 is associated with PWB.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIU mediates the relationship between FC-19 and PWB.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe mediating role of IU is still significant when age, gender, class level, and perceived success are controlled.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e "},{"header":"Method","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003eParticipants and Procedures\u003c/h2\u003e \u003cp\u003eThe research sample was composed of 316 Turkish university students (251 females, 65 males) from undergraduate (Freshman\u0026thinsp;=\u0026thinsp;93, sophomore\u0026thinsp;=\u0026thinsp;85, senior\u0026thinsp;=\u0026thinsp;45, junior\u0026thinsp;=\u0026thinsp;60), graduate (n\u0026thinsp;=\u0026thinsp;30) and PhD degrees (n\u0026thinsp;=\u0026thinsp;3). Their ages ranged from 18 to 57 years, and the mean age of the participants was 22.08 (SD\u0026thinsp;=\u0026thinsp;4.58). The perceived success level of the students was also asked (very successful\u0026thinsp;=\u0026thinsp;16, successful\u0026thinsp;=\u0026thinsp;148, medium successful\u0026thinsp;=\u0026thinsp;133, unsuccessful\u0026thinsp;=\u0026thinsp;14, very unsuccessful\u0026thinsp;=\u0026thinsp;5). A widely accepted benchmark in the social sciences is the goal of achieving a minimum effect size of 0.80 (Cohen, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). With the help of G*Power software, it can be determined that to achieve sufficient power, a sample size of approximately 90 participants per group or condition would be required when considering medium effect sizes (Cohen\u0026rsquo;s d\u0026thinsp;=\u0026thinsp;0.5) and an alpha of 0.05 (Lakens, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Having a total sample size (n\u0026thinsp;=\u0026thinsp;316) that exceeds this threshold ensures sufficient power to identify substantial effects in mediation studies.\u003c/p\u003e \u003cp\u003eEthical and organizational permissions were obtained from the participants before the study. Prior to participating in the study, informed verbal consent was obtained from each participant. A detailed information sheet was provided to participants, detailing the study's objectives, methods, possible risks, confidentiality policies, and their option to withdraw at any point during the study. Following this, participants were instructed to click an 'I agree to participate' button to formally indicate their consent. This methodology was endorsed by the Institutional Review Board and adheres to the ethical standards for low-risk online studies.\u003c/p\u003e \u003cp\u003eThe Snowball sampling method was used to administer the online survey. The scale forms were administered through online Google Forms. Individuals were eligible to participate in the study if they were 18 years of age or older, university students, and able to read and complete the online consent form and scales in Turkish.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003eSEOLE Scale\u003c/h2\u003e \u003cp\u003eThe Student Engagement Scale, developed by Sun and Rueda (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and adapted into Turkish by Erg\u0026uuml;n and Ko\u0026ccedil;ak Usluel (2015), was used in this study (Erg\u0026uuml;n \u0026amp; Usluel, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The scale comprises 19 items. It is a 5-point Likert scale, ranging from strongly disagree (1) to strongly agree (5). A minimum score of 19 and a maximum score of 95 can be obtained using this scale. High scores can be interpreted as high student engagement in the online learning environment, whereas low scores can be interpreted as low student engagement. The Cronbach's alpha was calculated as 0.84 (Sun \u0026amp; Rueda, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePWB Scale\u003c/h2\u003e \u003cp\u003eThe PWB Scale was developed by Diener et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and Telef (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) conducted a Turkish validity and reliability study (Telef, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The scale consists of eight items and is used to describe important elements of human functioning, from positive relationships to feelings of competence to having a meaningful and purposeful life. The items of this 7-point Likert-type scale are answered on a 1\u0026ndash;7 scale ranging from strongly disagree (1) to strongly agree (7). There are no reverse scored items in the scale. Scores range from a minimum of 8 to a maximum of 56. A high score indicates that the individual to whom the scale is applied has many psychological resources and strengths. In the original scale, Cronbach\u0026rsquo;s alpha coefficient was calculated as .87 (Diener et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eIU Scale\u003c/h2\u003e \u003cp\u003eThe Intolerance for Uncertainty Scale (ITS-12) was developed by Carleton et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) to measure reactions to uncertain situations, and Sarı\u0026ccedil;am et al. (2014) conducted a Turkish validity and reliability study (Sari\u0026ccedil;am, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The self-reporting scale comprises 12 items. The scale has a 5-point Likert-type structure and ranges from 1 to 5 points from \u0026ldquo;Not at all suitable for me\u0026rdquo; to \u0026ldquo;Completely suitable for me.\u0026rdquo; The total score that can be obtained from the scale, which does not have a cut-off score, varies between 12 and 60. There are no reverse-coded items in the scale. Higher scores indicate a high level of IU. Cronbach's alpha internal consistency coefficient was .88 (Carleton et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eFC-19 Scale\u003c/h2\u003e \u003cp\u003eThe scale was developed by Ahorsu et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Bakioğlu et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) conducted its Turkish validity and reliability study (Bakioğlu et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The scale is a 5-point Likert scale consisting of 7 items. There are no reverse items in the scale. The scale is scored from strongly disagree (1) to strongly agree (5). The lowest score was 7, and the highest score was 35. As the scale scores increased, the fear level of the coronavirus increased. The Cronbach\u0026rsquo;s alpha value of the scale was 0.82 (Ahorsu, et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eThe hypothesized mediation model (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) was tested in a single model using a bootstrapping approach to assess the significance of the indirect effects at different levels of the moderator (Hayes, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In the first and second models, FC-19 was the predictor variable, SEOLE was the outcome variable, and IU was the mediator. In the third and fourth models, FC-19 was the predictor variable, PWB was the outcome variable, and IU was the mediator variable. Demographic variables such as gender, age, class level, and perceived success were controlled in the analysis.\u003c/p\u003e \u003cp\u003eThe \u0026ldquo;PROCESS\" macro, model 4, v4.2 (Hayes, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) in SPSS 25 with bias-corrected 95% confidence intervals (n\u0026thinsp;=\u0026thinsp;5000) was used to assess the significance of the indirect effects. This model implicitly tests the mediation role on the pathway from predictor to outcome. Significant effects are underpinned by the absence of zeros in the confidence intervals, as no zeros should be present in the interval (Preacher \u0026amp; Hayes, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eTesting Common method bias and multicollinearity\u003c/h2\u003e \u003cp\u003eExploratory factor analysis (EFA) was performed to test for common method bias. Harman\u0026rsquo;s single factor test allowed SEOLE, FC-19, PWB, and IU to be entered into the exploratory factor analysis (EFA) procedure, and the results showed that 19.93% variance could be explained by the first factor, which is below the 40% standard (Baumgartner et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This result indicated that common method bias was not significant.\u003c/p\u003e \u003cp\u003eIn the next phase, the potential multicollinearity problem was checked using the Variance Inflation Factor (VIF) and Condition Index (CI) values for the independent variables. The VIF values for all independent variables are below 1.6, suggesting a moderate correlation between the variables. In addition, the CI values for all independent variables are below 30. These results reveal that there is no concern regarding multicollinearity in the data (Shrestha, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive statistics and correlation analyses\u003c/h2\u003e \u003cp\u003eSkewness (ranged \u0026minus;\u0026thinsp;.64 to .49) and kurtosis (ranged \u0026minus;\u0026thinsp;.56 to .59) of all variables showed normal distribution. After this procedure, parametric tests were conducted.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eTable\u0026nbsp;1 Information about the variables and relationships among them\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. FC-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e―\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. PWB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.18**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e―\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. SEOLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.15**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.36***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e―\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. IU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.33***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.32***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.19**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e―\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCronbach's α\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05, **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote. SEOLE\u0026thinsp;=\u0026thinsp;student engagement in online learning environments, PWB\u0026thinsp;=\u0026thinsp;psychological well-being, FC-19\u0026thinsp;=\u0026thinsp;fear of COVID-19, IU\u0026thinsp;=\u0026thinsp;intolerance of uncertainty.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;1, The means of all variables were moderate. According to the correlation analysis results, FC-19 had a significant, negative correlation with PWB and a significant, positive correlation with IU and SEOLE. In addition, PWB had a significant, positive correlation with SEOLE and a significant, negative correlation with IU. Finally, SEOLE and IU were significantly and negatively correlated. Mediation analysis was conducted after this step.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eTests of the mediation model\u003c/h2\u003e \u003cdiv id=\"Sec24\" class=\"Section4\"\u003e \u003ch2\u003eM1 and M2: IU mediates the relationship between FC-19 and SEOLE\u003c/h2\u003e \u003cp\u003eThe hypothesized mediation model was tested using the PROCESS macro model number 4, which tests a model in which IU mediates the relationship shown in\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (Hayes, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). First, the relationship between FC-19 and SEOLE (b = -0.21, p\u0026thinsp;\u0026gt;\u0026thinsp;0.5) was not significant; therefore, H1 was not supported. Second, the mediating role of IU on the relationship between FC-19 and SEOLE was tested, and the results revealed a significant indirect effect of FC-19 on SEOLE (b= -0.11, BootLLCI=-.22; BootULCI=-.02), supporting H2. Hence, IU significantly mediated the relationship between FC-19 and SEOLE. Due to the contemporary approach, there is no need for the direct effect; the total effect; the effect of predictor on mediator; or the effect of mediator on outcome variable need to be statistically significant, and bootstrapping should be used to test the significance of the indirect effect (MacKinnon et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Preacher \u0026amp; Hayes, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The bootstrapping results clarified that zero was not within the confidence intervals, indicating that the mediating effect of IU was significant (Hayes, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAfter this step, gender, age, class level, and perceived success entered this model as control variables to check the possible variation in the direct role of FC-19 on SEOLE and the mediating role of IU. Gender, age, class level, and perceived success jointly accounted for significant variation in SEOLE, R-square\u0026thinsp;=\u0026thinsp;.15, F (6,308)\u0026thinsp;=\u0026thinsp;9.308, p\u0026thinsp;\u0026lt;\u0026thinsp;.001. The direct roles of age (b\u0026thinsp;=\u0026thinsp;.525, s.e.=.168, p\u0026thinsp;\u0026lt;\u0026thinsp;.01), class level (b=-1.37, s.e.=.55, p\u0026thinsp;\u0026lt;\u0026thinsp;.05) and perceived success (b\u0026thinsp;=\u0026thinsp;4.37, s.e.=.86, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) were positive and significant. The direct role of gender on SEOLE was not significant (b\u0026thinsp;=\u0026thinsp;.15, s.e.=1.58, p\u0026thinsp;\u0026gt;\u0026thinsp;.05). In this model, the relationship between FC-19 and SEOLE was still insignificant (b=-.16, s.e.=.12, p\u0026thinsp;\u0026lt;\u0026thinsp;.05) and the mediating role of IU was still significant (b= -0.09, BootSE\u0026thinsp;=\u0026thinsp;.04, BootLLCI=-.19; BootULCI=-.02), supporting H3 (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eM3 and M4: IU mediates the relationship between FC-19 and PWB\u003c/h2\u003e \u003cp\u003eIn this model, the mediating role of IU in the relationship between FC-19 and PWB was examined. First, the relationship between FC-19 and PWB (b = -0.12, p\u0026thinsp;\u0026gt;\u0026thinsp;0.5) was not significant, so H4 was not supported. Second, the results revealed a significant indirect role of FC-19 on PWB (b= -0.14, BootLLCI=-.23; BootULCI=-.07). Hence, IU mediated the relationship between FC-19 and PWB (MacKinnon et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Preacher \u0026amp; Hayes, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Since zero was not within the BootCI, this indicated that the mediating role of IU was significant (Hayes, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), supporting H5.\u003c/p\u003e \u003cp\u003eAgain, gender, age, class level, and perceived success entered this model as control variables to check the possible change in the direct effect of FC-19 on PWB. Gender, age, class level, and perceived success jointly accounted for significant variation in PWB, R-square\u0026thinsp;=\u0026thinsp;.18, F(6,308)\u0026thinsp;=\u0026thinsp;11.31, p\u0026thinsp;\u0026lt;\u0026thinsp;.001. The direct role of age (b\u0026thinsp;=\u0026thinsp;.12, s.e.=.11, p\u0026thinsp;\u0026gt;\u0026thinsp;.05), class level (b\u0026thinsp;=\u0026thinsp;0.31, s.e.=.37, p\u0026thinsp;\u0026gt;\u0026thinsp;.05), and gender (b=-0.01, s.e.=1.08, p\u0026thinsp;\u0026gt;\u0026thinsp;.05) was not significant. The direct role of perceived success on PWB was the only significant variable (b\u0026thinsp;=\u0026thinsp;2.58, s.e.=.59, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). In this model, the relationship between FC-19 and PWB was not significant (b=-.01, s.e.=.08, p\u0026thinsp;\u0026gt;\u0026thinsp;.05) and the mediating role of IU was also significant (b= -0.12, BootSE\u0026thinsp;=\u0026thinsp;.04, BootLLCI=-.19; BootULCI=-.06). Thus, H6 was supported (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e)..\u003c/p\u003e \n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eModels tested in the mediation analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5199%;\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21.8674%;\"\u003e\n \u003cp\u003eDV: SEOLE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 17.2838%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; DV: PWB\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5199%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5995%;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2679%;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4589%;\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8408%;\"\u003e\n \u003cp\u003eModel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5199%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5995%;\"\u003e\n \u003cp\u003e\u0026beta; (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2679%;\"\u003e\n \u003cp\u003eB (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4589%;\"\u003e\n \u003cp\u003eB (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8408%;\"\u003e\n \u003cp\u003eB(SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5199%;\"\u003e\n \u003cp\u003eFC-19\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5995%;\"\u003e\n \u003cp\u003e-0.21\u003c/p\u003e\n \u003cp\u003e(0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2679%;\"\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003cp\u003e(0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4589%;\"\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003cp\u003e(0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8408%;\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003cp\u003e(0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5199%;\"\u003e\n \u003cp\u003eIU\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5995%;\"\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003cp\u003e(0.07*)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2679%;\"\u003e\n \u003cp\u003e-0.17\u003c/p\u003e\n \u003cp\u003e(0.07*)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4589%;\"\u003e\n \u003cp\u003e-0.24\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.47***)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8408%;\"\u003e\n \u003cp\u003e-0.21\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.05***)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5199%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5995%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2679%;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003cp\u003e(0.17**)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4589%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8408%;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003cp\u003e(0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5199%;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5995%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2679%;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003cp\u003e(1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4589%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8408%;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003cp\u003e(1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5199%;\"\u003e\n \u003cp\u003eClass Levels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5995%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2679%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; -1.37\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;(0.54*)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4589%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8408%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.31\u003c/p\u003e\n \u003cp\u003e(0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5199%;\"\u003e\n \u003cp\u003ePerceived Success\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5995%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2679%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;4.37\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;(0.86***)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4589%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8408%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 2.58\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (0.59***)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5199%;\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5995%;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2679%;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4589%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8408%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5199%;\"\u003e\n \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5995%;\"\u003e\n \u003cp\u003e7.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2679%;\"\u003e\n \u003cp\u003e9.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4589%;\"\u003e\n \u003cp\u003e19.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8408%;\"\u003e\n \u003cp\u003e11.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eThe mediating effects of IU (before control variables entered the model)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5199%;\"\u003e\n \u003cp\u003eDV: SEOLE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5995%;\"\u003e\n \u003cp\u003e\u003cem\u003eIndirect Effect\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2679%;\"\u003e\n \u003cp\u003e\u003cem\u003eBoot SE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4589%;\"\u003e\n \u003cp\u003e\u003cem\u003eBoot LLCI\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8408%;\"\u003e\n \u003cp\u003e\u003cem\u003eBoot ULCI\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5199%;\"\u003e\n \u003cp\u003e\u003cem\u003eStandardized\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5995%;\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2679%;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4589%;\"\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8408%;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5199%;\"\u003e\n \u003cp\u003e\u003cem\u003eUnstandardized\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5995%;\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2679%;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4589%;\"\u003e\n \u003cp\u003e-0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8408%;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5199%;\"\u003e\n \u003cp\u003eDV: PWB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5995%;\"\u003e\n \u003cp\u003e\u003cem\u003eIndirect Effect\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2679%;\"\u003e\n \u003cp\u003e\u003cem\u003eBoot SE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4589%;\"\u003e\n \u003cp\u003e\u003cem\u003eBoot LLCI\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8408%;\"\u003e\n \u003cp\u003e\u003cem\u003eBoot ULCI\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5199%;\"\u003e\n \u003cp\u003e\u003cem\u003eStandardized\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5995%;\"\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2679%;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4589%;\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8408%;\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5199%;\"\u003e\n \u003cp\u003e\u003cem\u003eUnstandardized\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5995%;\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2679%;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4589%;\"\u003e\n \u003cp\u003e-0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8408%;\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 61.687%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eThe mediating effects of IU (after control variables entered the model)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5199%;\"\u003e\n \u003cp\u003eDV: SEOLE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5995%;\"\u003e\n \u003cp\u003e\u003cem\u003eIndirect Effect\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2679%;\"\u003e\n \u003cp\u003e\u003cem\u003eBoot SE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4589%;\"\u003e\n \u003cp\u003e\u003cem\u003eBoot LLCI\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8408%;\"\u003e\n \u003cp\u003e\u003cem\u003eBoot ULCI\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5199%;\"\u003e\n \u003cp\u003e\u003cem\u003eStandardized\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5995%;\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2679%;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4589%;\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8408%;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5199%;\"\u003e\n \u003cp\u003e\u003cem\u003eUnstandardized\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5995%;\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2679%;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4589%;\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8408%;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5199%;\"\u003e\n \u003cp\u003eDV: PWB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5995%;\"\u003e\n \u003cp\u003e\u003cem\u003eIndirect Effect\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2679%;\"\u003e\n \u003cp\u003e\u003cem\u003eBoot SE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4589%;\"\u003e\n \u003cp\u003e\u003cem\u003eBoot LLCI\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8408%;\"\u003e\n \u003cp\u003e\u003cem\u003eBoot ULCI\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5199%;\"\u003e\n \u003cp\u003e\u003cem\u003eStandardized\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5995%;\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2679%;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4589%;\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8408%;\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5199%;\"\u003e\n \u003cp\u003e\u003cem\u003eUnstandardized\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5995%;\"\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2679%;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4589%;\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8408%;\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eNote: \u003cem\u003e\u0026beta;\u0026nbsp;\u003c/em\u003e= unstandardized regression coefficient, IV = independent variables, DV = dependent variables, SE = standard error, LLCI = lower confidence interval, UCLI = lower confidence interval. SEOLE= student engagement in online learning environments, PWB= psychological well-being, FC-19= fear of COVID-19, IU= intolerance of uncertainty.\u003c/p\u003e\n \u003cp\u003e***\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001, **\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01, *\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings of this study contribute to a better understanding of how FC-19 shapes educational and psychological outcomes. Models 1 and 2 emphasize that FC-19 had a negative relationship with SEOLE and serves as an important mediator of IU. This suggests that students struggling with uncertainty and unpredictable circumstances may find it more difficult to stick to their studies. Some studies support these findings. The transition to online learning exacerbated feelings of IU, leading to decreased SEOLE and increased burnout. Additionally, IU is linked to decreased academic adjustment and engagement in online learning, as students struggle with the unpredictability of the educational environment (Daşcı et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These findings indicate that complex situations with severe uncertainties, such as COVID-19, make it difficult for students to focus on life, coursework, and assignments in online environments. Therefore, the findings of this study are noteworthy.\u003c/p\u003e \u003cp\u003eSEOLE plays a vital role in vague times like COVID-19. One study reports that SEOLE mediates the path of academic grit and well-being, and the relationship between intolerance to uncertainty and well‐being (Kareem et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), while switching to distance learning negatively affects their learning. Student engagement is both a protective factor against uncertainty and a positive contributor to students\u0026rsquo; psychological well-being. Increased workload and lack of motivation can cause students to feel stressed and frustrated, which affects the online learning environment, further negatively impacting students\u0026rsquo; mental health and engagement levels (Mihai et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Moreover, learning motivation and school well-being were affected by online learning during the COVID-19 pandemic. There are different challenges for different student populations, depending on technology issues, academic integration, social integration, motivation, and support (Cena et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For this reason, it is important to understand the difficulties and issues in the online course period and to pay attention to them in future studies. Our findings showed that FC-19 negatively affected SEOLE, and IU mediated this relationship. The above studies support our findings.\u003c/p\u003e \u003cp\u003eModels 3 and 4 reveal a parallel pattern for PWB. FC-19 directly undermines well-being, and this relationship is strengthened through IU. Many students have suffered psychological consequences as a result of the COVID-19 pandemic and students with higher IU levels are likely to experience high emotional distress, potentially manifesting as anxiety, depression, emotional burnout, and decreased life satisfaction, which negatively impacts their PWB (Irak et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A large number of studies support our findings. IU is a significant vulnerability factor linked to various mental health issues, especially during the pandemic (Akbari et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The findings in the literature clearly demonstrate that IU severely affects PWB. Because it is difficult to cope with states of uncertainty, the aforementioned symptoms arise. These studies provide supporting evidence of our findings.\u003c/p\u003e \u003cp\u003eClarification on the nature of mediation is crucial when examining the relationships detailed in this manuscript, notably the mediation of intolerance of uncertainty (IU) in models relating to FC-19, SEOLE and PWB. The FC-19's direct influence on SEOLE and PWB did not prove to be statistically significant, however mediation via IU was found to be substantial and is in line with the existing mediation analysis frameworks proposed by Hayes (Putri \u0026amp; Etikariena, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This pattern of results is consistent with the concept of full mediation as defined by Hayes (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), which occurs when the effect of an independent variable on a dependent variable is completely mediated by a third variable. In essence, the fear of COVID-19 does not have a substantial impact on student engagement or psychological well-being by itself; however, its effects become apparent when it exacerbates intolerance of uncertainty. IU acts as a key intermediary, facilitating the impact of the fear of COVID-19 on both educational and psychological outcomes.\u003c/p\u003e \u003cp\u003eThe significance of addressing intolerance of uncertainty as a focal point for intervention is underscored by abovementioned results. Given that students with higher levels of intolerance of uncertainty may experience greater difficulty in adjusting to online learning and preserving their psychological well-being, interventions should target developing resilience, adaptability, and the ability to cope with uncertainty. Structured uncertainty tolerance training and stress-management programs may alleviate the negative consequences of COVID-19-related anxiety by diminishing instances of learned helplessness.\u003c/p\u003e \u003cp\u003eReducing the fear of COVID-19 alone is unlikely to be sufficient to enhance engagement and well-being when the underlying issue of IU is not being addressed. Educational establishments and mental health practitioners should take into account holistic strategies that concentrate on both minimizing excessive anxiety and assisting students in coping with uncertainty. Future studies could investigate whether certain coping techniques, including mindfulness or cognitive-behavioral methods, can efficiently influence the pathway involving IU.\u003c/p\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003eStudy Limitations\u003c/h2\u003e \u003cp\u003eDespite its contributions, this study has several limitations that need to be considered. First, this study's application of cross-sectional mediation analysis is consistent with previous research, but also illustrates a wider pattern of over-reliance on this method in psychological studies (Maxwell et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The hypothesized mediation model has a theoretical basis, but its, the cross-sectional design limits the ability to deduce causal relationships between FC-19, IU, and dependent variables. Future research should focus on employing either longitudinal or experimental methodologies to corroborate these results.\u003c/p\u003e \u003cp\u003eSecond, reliance on self-reported measures may lead to biases such as social desirability or recall errors. While validated scales were used, future research could include objective behavioral assessments or qualitative methods to triangulate findings.\u003c/p\u003e \u003cp\u003eThird, although the sample is diverse, it is limited to students at specific academic institutions, potentially limiting the generalizability of the results to broader populations or groups of students in other countries. Expanding the study to include different geographical and cultural contexts may provide a more holistic understanding of these dynamics. Moreover, the study's reliance on snowball sampling and the disproportionate representation of female participants (79%) may compromise the applicability of the results to a wider range of populations, including male students and individuals from diverse cultural backgrounds. The use of a convenience sample of Turkish students limits the generalizability of the study's findings. Future research should strive to recruit samples that are more diverse and representative, encompassing equal gender representations, multiple educational levels, and individuals from a range of cultural and geographical backgrounds. Utilizing probability-based sampling techniques, such as stratified random sampling, combined with a priori power analyses will increase the robustness and broad applicability of research results.\u003c/p\u003e \u003cp\u003eFourth, unmeasured variables such as pre-existing mental health, socioeconomic status, and access to technology may have influenced the results. In future research, controlling these factors could improve the robustness of the findings. Finally, this study focused only on the mediating role of IU. Investigating additional mediators, such as social support and coping mechanisms, may provide a more holistic view of the factors influencing student engagement and PWB during crises.\u003c/p\u003e \u003cp\u003eLast, The PROCESS macro is most suitable for straightforward mediation models, but it was not created to assess model fit or include hidden variables. The drawback of this research lies in its dependence on the PROCESS macro for mediation analysis, which fails to consider latent constructs, measurement errors, or model fit indices. Future studies should utilize structural equation modelling to rigorously examine the proposed relationships, incorporating latent variables and examining model fit metrics. These approaches will increase the reliability of outcomes and offer more in-depth understanding of the mechanisms relating FC-19, IU, PWB, and SEOLE.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eImplications and Recommendations\u003c/h2\u003e \u003cp\u003eGiven the complex relationships among IU, SEOLE, PWB, and FC-19, educational institutions should take a holistic approach to supporting students during these challenging times. Interventions focused on reducing IU, such as cognitive-behavioral strategies, can improve students\u0026rsquo; coping mechanisms and encourage engagement in online learning (Andrade et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore, fostering a supportive online community can reduce feelings of isolation and improve students\u0026rsquo; PWB (Hilton et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Additionally, addressing students\u0026rsquo; fears related to COVID-19 through transparent communication and mental health resources can help alleviate IU and promote a more conducive learning environment.\u003c/p\u003e \u003cp\u003eFrom another perspective, exploring other potential mediators or moderators may further enrich the findings. Internet and social media addiction (Ismail et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and the level of doomscrolling (Anlı, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) all increased during the pandemic period. The potential impacts of these concepts directly related to the online period on SEOLE and PWB are important to investigate.\u003c/p\u003e \u003cp\u003eFuture research should continue to explore the nuanced relationships between these variables, with an emphasis on longitudinal studies that can capture the changing nature of students' experiences in online learning contexts. Interventions that enhance coping mechanisms, promote engagement, and address psychological needs may reduce the negative effects of these factors and eventually promote better PWB among students. For example, psychological and cognitive resilience as well as coping strategies can prevent the negative effects of IU and increase students\u0026rsquo; PWB. Psychological resilience promotes better mental health outcomes by reducing the negative effects of IU, promoting better mental health outcomes (Daşcı et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Some studies have suggested that promoting psychological resilience and mindfulness can improve SEOLE (Irak et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); thus, individual and group therapy or psychoeducational intervention studies can be conducted to increase the PWB and coping skills of students during drastic times like COVID-19. The effects of these variables on SEOLE and PWB can be examined to understand these concepts in depth. The concrete and detailed intervention suggestions are listed below.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eMindfulness Training\u003c/strong\u003e \u003cp\u003eEvidence from numerous studies suggests that mindfulness practices like meditation and focused breathing can greatly enhance psychological flexibility and emotional regulation, skills that are particularly important in navigating uncertain situations (Khatami et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Practicing mindfulness not only boosts self-awareness but also minimizes anxiety linked to uncertain circumstances, thereby promoting increased involvement and overall well-being. Structured programs for students that incorporate regular mindfulness exercises can offer a structured framework for students to learn and integrate these techniques into their daily lives (Edmundson \u0026amp; Jenson, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStructured Uncertainty Tolerance Workshops\u003c/strong\u003e \u003cp\u003eBy focusing educational modules on enhancing Individuals' Understanding, these workshops can effectively provide students with strategies to manage ambiguity and unpredictability. These workshops may use real-life scenario training to mimic uncertain situations, enabling students to hone their coping skills in a secure setting. Case discussions and role-playing scenarios about health uncertainties during the Covid-19 pandemic can aid students in developing resilience by simulating decision-making under uncertainty, thereby enhancing their participation in learning activities and overall mental well-being (Reis-Dennis et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCoping Strategies and Communication Training\u003c/strong\u003e \u003cp\u003eTraining programs that emphasize communication skills create a setting in which students feel more at ease articulating their doubts and worries. This process may entail peer-to-peer conversations, where students are encouraged to exchange their personal experiences and proposed solutions related to uncertainty in academic environments. Normalizing uncertainties through open conversation can help reduce associated stigma (Fei et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eParent and Community Involvement Strategies\u003c/strong\u003e \u003cp\u003eIncorporating conversations with families about uncertainty and resilience can enhance educational programs. Workshops aimed at families would provide them with information to help their children manage IU and distress, thereby establishing a comprehensive support network for students, which would improve their ability to cope with uncertain situations (Karamushka et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePeer Support Programs\u003c/strong\u003e \u003cp\u003eEstablishing peer support groups for students to discuss their experiences with Covid-19 and academic concerns may foster resilience. Through peer mentoring, students not only provide mutual support but also acquire varied viewpoints on tackling uncertain circumstances. Research has demonstrated that this approach can help alleviate distress related to uncertainty, ultimately leading to improved well-being and engagement (Leisterer et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe COVID-19 pandemic has highlighted the pivotal interplay between psychological constructs such as IU and fear and their impact on student engagement and wellbeing in online learning environments. Although FC-19 poses significant threats to students\u0026rsquo; PWB, engagement in online learning can serve as a crucial buffer. IU may promote academic achievement and psychological resilience by reducing the negative effects of pandemic-related fear by developing coping skills. Understanding these dynamics is fundamental for designing impactful strategies to support students during and after the pandemic.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompliance with Ethical Standards\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThe study has its\u0026rsquo; own funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003eThe author created the idea for this study and developed the protocol, methods and the data analysis and wrote the manuscript by himself.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003eThe contents of this manuscript have not been copyrighted or published previously and not now under consideration for publication elsewhere. The author declares that he has no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This study was reviewed and approved by the Ethics Committee of Bursa Technical University (Protocol no: 19.02.2021-E.847). Clinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003eThe datasets generated by the survey research during and/or analyzed during the current study are available in the Mendeley repository, https://doi.org/\u0026nbsp;10.17632/c3rrw96d7h.1\u0026rdquo;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish declaration\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate declaration\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhorsu, D. K., Imani, V., Lin, C.-Y., Timpka, T., Brostr\u0026ouml;m, A., Updegraff, J. A., \u0026Aring;restedt, K., Griffiths, M. D., \u0026amp; Pakpour, A. H. (2020). Associations between fear of COVID-19, mental health, and preventive behaviours across pregnant women and husbands: An actor-partner interdependence modelling. \u003cem\u003eInternational Journal of Mental Health and Addiction\u003c/em\u003e, 1\u0026ndash;15.\u003c/li\u003e\n\u003cli\u003eAhorsu, D. K., Lin, C., Alimoradi, Z., Griffiths, M. D., Chen, H.-P., Brostr\u0026ouml;m, A., Timpka, T., \u0026amp; Pakpour, A. H. (2022). Cyberchondria, Fear of COVID-19, and Risk Perception Mediate the Association Between Problematic Social Media Use and Intention to Get a COVID-19 Vaccine. \u003cem\u003eVaccines\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(1), 122. https://doi.org/10.3390/vaccines10010122\u003c/li\u003e\n\u003cli\u003eAkbari, M., Seydavi, M., Zamani, E., Jamshidi, S., \u0026amp; Freeston, M. H. (2024). Intolerance of Uncertainty as a Situational Vulnerability Factor in the Context of the Pandemic: A Systematic Review and Meta‐Analysis of COVID‐19‐Related Psychological Impacts. \u003cem\u003eClinical Psychology \u0026amp; Psychotherapy\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e(5), e3046.\u003c/li\u003e\n\u003cli\u003eAn, R., Addas, A., Rehman, N., \u0026amp; Rehman, S. (2024). Predicting online learning experiences in sports education during the COVID-19 pandemic: Implications for pedagogical strategies. \u003cem\u003eHeliyon\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(17).\u003c/li\u003e\n\u003cli\u003eAndrade, E. F., Pereira, L. J., Ana Paula Luiz de Oliveira, Orlando, D. R., Alves, D. A. G., Guilarducci, J. d. S., \u0026amp; Castelo, P. M. (2020). Perceived Fear of COVID-19 Infection According to Sex, Age and Occupational Risk Using the Brazilian Version of the Fear of COVID-19 Scale. \u003cem\u003eDeath Studies\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(3), 533\u0026ndash;542. https://doi.org/10.1080/07481187.2020.1809786\u003c/li\u003e\n\u003cli\u003eAnlı, G. (2023). Felaket Haberlerini Kaydırma \u0026Ouml;l\u0026ccedil;eği T\u0026uuml;rk\u0026ccedil;e formunun ge\u0026ccedil;erlik ve g\u0026uuml;venirlik \u0026ccedil;alışması. \u003cem\u003eDokuz Eyl\u0026uuml;l \u0026Uuml;niversitesi Sosyal Bilimler Enstit\u0026uuml;s\u0026uuml; Dergisi\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(1), 301\u0026ndash;316.\u003c/li\u003e\n\u003cli\u003eAristovnik, A., Keržič, D., Rav\u0026scaron;elj, D., Tomaževič, N., \u0026amp; Umek, L. (2020). Impacts of the COVID-19 pandemic on life of higher education students: A global perspective. \u003cem\u003eSustainability\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(20), 8438.\u003c/li\u003e\n\u003cli\u003eBakioğlu, F., Korkmaz, O., \u0026amp; Ercan, H. (2020). Fear of COVID-19 and Positivity: Mediating Role of Intolerance of Uncertainty, Depression, Anxiety, and Stress. \u003cem\u003eInternational Journal of Mental Health and Addiction\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(6), 2369\u0026ndash;2382. https://doi.org/10.1007/s11469-020-00331-y\u003c/li\u003e\n\u003cli\u003eBaumgartner, H., Weijters, B., \u0026amp; Pieters, R. (2021). The biasing effect of common method variance: Some clarifications. \u003cem\u003eJournal of the Academy of Marketing Science\u003c/em\u003e, \u003cem\u003e49\u003c/em\u003e, 221\u0026ndash;235.\u003c/li\u003e\n\u003cli\u003eCapone, R., \u0026amp; Lepore, M. (2022). From distance learning to integrated digital learning: A fuzzy cognitive analysis focused on engagement, motivation, and participation during COVID-19 pandemic. \u003cem\u003eTechnology, Knowledge and Learning\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(4), 1259\u0026ndash;1289.\u003c/li\u003e\n\u003cli\u003eCarleton, R. N. (2016). Into the unknown: A review and synthesis of contemporary models involving uncertainty. \u003cem\u003eJournal of Anxiety Disorders\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e, 30\u0026ndash;43.\u003c/li\u003e\n\u003cli\u003eCarleton, R. N., Norton, M. P. J., \u0026amp; Asmundson, G. J. (2007). Fearing the unknown: A short version of the Intolerance of Uncertainty Scale. \u003cem\u003eJournal of Anxiety Disorders\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(1), 105\u0026ndash;117.\u003c/li\u003e\n\u003cli\u003eCarlson, K. D., \u0026amp; Wu, J. (2012). The ıllusion of statistical control: Control variable practice in management research. \u003cem\u003eOrganizational Research Methods\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(3), 413\u0026ndash;435. https://doi.org/10.1177/1094428111428817\u003c/li\u003e\n\u003cli\u003eCena, E., Toner, P., McParland, A., Burns, S., \u0026amp; Dudgeon, K. (2023). Studying and learning psychology during the COVID-19 pandemic: A mixed-methods approach on students\u0026rsquo; perspectives of psychological well-being and adjustment to studying online. \u003cem\u003ePsychology Learning \u0026amp; Teaching\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(2), 137\u0026ndash;158.\u003c/li\u003e\n\u003cli\u003eCohen, J. (2013). \u003cem\u003eStatistical Power Analysis for the Behavioral Sciences\u003c/em\u003e (0 ed.). Routledge. https://doi.org/10.4324/9780203771587\u003c/li\u003e\n\u003cli\u003eCrawford, J., Butler-Henderson, K., Rudolph, J., Malkawi, B., Glowatz, M., Burton, R., Magni, P., \u0026amp; Lam, S. (2020). \u003cem\u003eCOVID-19: 20 countries\u0026rsquo; higher education intra-period digital pedagogy responses\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eDaşcı, E., Salihoğlu, K., \u0026amp; Daşcı, E. (2023). The relationship between tolerance for uncertainty and academic adjustment: The mediating role of students\u0026rsquo; psychological flexibility during COVID-19. \u003cem\u003eFrontiers in Psychology\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e, 1272205.\u003c/li\u003e\n\u003cli\u003eDeci, E. L., \u0026amp; Ryan, R. M. (2013). \u003cem\u003eIntrinsic motivation and self-determination in human behavior\u003c/em\u003e. Springer Science \u0026amp; Business Media.\u003c/li\u003e\n\u003cli\u003eDiener, E., Napa Scollon, C., \u0026amp; Lucas, R. E. (2009). The evolving concept of subjective well-being: The multifaceted nature of happiness. \u003cem\u003eAssessing Well-Being: The Collected Works of Ed Diener\u003c/em\u003e, 67\u0026ndash;100.\u003c/li\u003e\n\u003cli\u003eDoğan\u0026uuml;lk\u0026uuml;, H. A., Korkmaz, O., Griffiths, M. D., \u0026amp; Pakpour, A. H. (2021). Fear of COVID-19 Lead to Procrastination Among Turkish University Students: The Mediating Role of Intolerance of Uncertainty. \u003cem\u003eBMC Psychology\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(1). https://doi.org/10.1186/s40359-021-00681-9\u003c/li\u003e\n\u003cli\u003eEdmundson, J., \u0026amp; Jenson, J. (2022). MakerMinds: An Exploratory Study of Making and Mindfulness Pedagogies. \u003cem\u003eCanadian Journal of Education / Revue Canadienne De L \u0026Eacute;ducation\u003c/em\u003e, \u003cem\u003e45\u003c/em\u003e(2), 481\u0026ndash;511. https://doi.org/10.53967/cje-rce.v45i2.4959\u003c/li\u003e\n\u003cli\u003eErg\u0026uuml;n, E., \u0026amp; Usluel, Y. K. (2015). \u0026Ccedil;evrimi\u0026ccedil;i \u0026ouml;ğrenme ortamlarında \u0026ouml;ğrenci bağlılık \u0026ouml;l\u0026ccedil;eği\u0026rsquo;nin t\u0026uuml;rk\u0026ccedil;e uyarlaması: Ge\u0026ccedil;erlik ve g\u0026uuml;venirlik \u0026ccedil;alışması. \u003cem\u003eEĞİTİM TEKNOLOJİSİ Kuram ve Uygulama\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(1), 20\u0026ndash;33. https://doi.org/10.17943/etku.64661\u003c/li\u003e\n\u003cli\u003eFei, D., Liu, Y., \u0026amp; Zhang, W. (2024). \u003cem\u003eThe Paradox of Uncertainty: How Tolerance for Uncertainty Modulates Approach Motivation\u0026mdash;Evidence From ERPs Experiments\u003c/em\u003e. https://doi.org/10.21203/rs.3.rs-4229168/v1\u003c/li\u003e\n\u003cli\u003eFredricks, J. A., Blumenfeld, P. C., \u0026amp; Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. \u003cem\u003eReview of Educational Research\u003c/em\u003e, \u003cem\u003e74\u003c/em\u003e(1), 59\u0026ndash;109.\u003c/li\u003e\n\u003cli\u003eGonzalez-Ramirez, J., Mulqueen, K., Zealand, R., Silverstein, S., Mulqueen, C., \u0026amp; BuShell, S. (2021). Emergency online learning: College students\u0026rsquo; perceptions during the COVID-19 pandemic. \u003cem\u003eCollege Student Journal\u003c/em\u003e, \u003cem\u003e55\u003c/em\u003e(1), 29\u0026ndash;46.\u003c/li\u003e\n\u003cli\u003eHayes, A. F. (2018). Partial, conditional, and moderated moderated mediation: Quantification, inference, and interpretation. \u003cem\u003eCommunication Monographs\u003c/em\u003e, \u003cem\u003e85\u003c/em\u003e(1), 4\u0026ndash;40.\u003c/li\u003e\n\u003cli\u003eHilton, B. T., Wiskur, B., Brand, M., Rojas, J. I., LCSW, R. D. L., Trapp, M., \u0026amp; Kuhn, K. G. (2023). Fear of COVID-19 Among Nonsmokers and Smokers/Former Smokers: Implications for Health Promotion Practice. \u003cem\u003eJournal of Emergency Management\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(7), 307\u0026ndash;313. https://doi.org/10.5055/jem.0764\u003c/li\u003e\n\u003cli\u003eHuda, M. (2023). Towards digital access during pandemic age: Better learning service or adaptation struggling? \u003cem\u003eForesight\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(1), 82\u0026ndash;107.\u003c/li\u003e\n\u003cli\u003eHyland, P., Shevlin, M., McBride, O., Murphy, J., Karatzias, T., Bentall, R. P., Martinez, A., \u0026amp; Valli\u0026egrave;res, F. (2020). Anxiety and depression in the Republic of Ireland during the COVID‐19 pandemic. \u003cem\u003eActa Psychiatrica Scandinavica\u003c/em\u003e, \u003cem\u003e142\u003c/em\u003e(3), 249\u0026ndash;256.\u003c/li\u003e\n\u003cli\u003eIrak, D. U., Dede, B., \u0026amp; Demir, N. (2024). Mindfulness during the COVID-19 pandemic lockdowns: Intolerance uncertainty and psychological well-being among employees. \u003cem\u003eSage Open\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(2), 21582440241251478. https://doi.org/10.1177/21582440241251478\u003c/li\u003e\n\u003cli\u003eIsmail, S. J. I., Kusnandar, T., Sanovia, Y., Mayasari, R., Negara, R. M., \u0026amp; Mahayana, D. (2020). Study of internet and social media addiction in Indonesia during COVID-19. \u003cem\u003eIJAIT (International Journal of Applied Information Technology)\u003c/em\u003e, 69\u0026ndash;80.\u003c/li\u003e\n\u003cli\u003eKaramushka, L., Терещенко, К., Креденцер, О., Ivkin, V., Lagodzinska, V., \u0026amp; Kovalchuk, O. (2021). Tolerance of Uncertainty as a Determinant of Educators\u0026rsquo; Psychological Health in Conditions of Social Tension. \u003cem\u003eОрганізаційна Психологія Економічна Психологія\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(24), 30\u0026ndash;38. https://doi.org/10.31108/2.2021.4.24.4\u003c/li\u003e\n\u003cli\u003eKareem, J., Thomas, S., Kumar P., A., \u0026amp; Neelakantan, M. (2023). The role of classroom engagement on academic grit, intolerance to uncertainty and well‐being among school students during the second wave of the COVID‐19 pandemic in India. \u003cem\u003ePsychology in the Schools\u003c/em\u003e, \u003cem\u003e60\u003c/em\u003e(5), 1594\u0026ndash;1608. https://doi.org/10.1002/pits.22758\u003c/li\u003e\n\u003cli\u003eKhatami, M., Shayerey, A. J., Behera, S. K., He, D., \u0026amp; Haji-Othman, N. A. (2023). \u003cem\u003eExploring Bangladeshi EFL Learners\u0026rsquo; Mindfulness and Engagement in Online Learning Context\u003c/em\u003e. https://doi.org/10.21203/rs.3.rs-3356992/v1\u003c/li\u003e\n\u003cli\u003eLai, A. Y., Lee, L., Wang, M., Feng, Y., Lai, T. T., Ho, L., Lam, V. S., Ip, M. S., \u0026amp; Lam, T. (2020). Mental health impacts of the COVID-19 pandemic on international university students, related stressors, and coping strategies. \u003cem\u003eFrontiers in Psychiatry\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e, 584240.\u003c/li\u003e\n\u003cli\u003eLakens, D. (2022). Sample Size Justification. \u003cem\u003eCollabra: Psychology\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(1), 33267. https://doi.org/10.1525/collabra.33267\u003c/li\u003e\n\u003cli\u003eLeisterer, S., Lautenbach, F., Walter, N., Kronenberg, L., \u0026amp; Elbe, A. (2021). Development of a Salutogenesis Workshop for SPPs to Help Them, Their Athletes, and the Athlete\u0026rsquo;s Entourage Better Cope with Uncertainty During the COVID-19 Pandemic. \u003cem\u003eFrontiers in Psychology\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e. https://doi.org/10.3389/fpsyg.2021.612264\u003c/li\u003e\n\u003cli\u003eMacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., \u0026amp; Sheets, V. (2002). A comparison of methods to test mediation and other intervening variable effects. \u003cem\u003ePsychological Methods\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(1), 83\u0026ndash;104. https://doi.org/10.1037/1082-989X.7.1.83\u003c/li\u003e\n\u003cli\u003eMalakcioglu, C. (2024). Emotional loneliness, perceived stress, and academic burnout of medical students after the COVID-19 pandemic. \u003cem\u003eFrontiers in Psychology\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e, 1370845.\u003c/li\u003e\n\u003cli\u003eMaxwell, S. E., Cole, D. A., \u0026amp; Mitchell, M. A. (2011). Bias in cross-sectional analyses of longitudinal mediation: Partial and complete mediation under an autoregressive model. \u003cem\u003eMultivariate Behavioral Research\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(5), 816\u0026ndash;841.\u003c/li\u003e\n\u003cli\u003eMazza, M., Marano, G., Lai, C., Janiri, L., \u0026amp; Sani, G. (2020). Danger in danger: Interpersonal violence during COVID-19 quarantine. \u003cem\u003ePsychiatry Research\u003c/em\u003e, \u003cem\u003e289\u003c/em\u003e, 113046.\u003c/li\u003e\n\u003cli\u003eMihai, M., Albert, C. N., Mihai, V. C., \u0026amp; Dumitras, D. E. (2022). Emotional and social engagement in the English language classroom for higher education students in the COVID-19 online context. \u003cem\u003eSustainability\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(8), 4527.\u003c/li\u003e\n\u003cli\u003eMok, K. H., Xiong, W., \u0026amp; Ye, H. (2021). COVID-19 crisis and challenges for graduate employment in Taiwan, Mainland China and East Asia: A critical review of skills preparing students for uncertain futures. \u003cem\u003eJournal of Education and Work\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(3), 247\u0026ndash;261.\u003c/li\u003e\n\u003cli\u003eMoussa, M., Moussa, F. L., Alharbi, H., Omer, T., Sofiany, H. A., Almuzaini, T. M., Alsaady, E. S., \u0026amp; Alrashede, S. M. (2022). Association of Fear of COVID-19 and Health Literacy Among the General Public in Saudi Arabia: A Cross-Sectional Assessment. \u003cem\u003eFrontiers in Public Health\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e. https://doi.org/10.3389/fpubh.2021.740625\u003c/li\u003e\n\u003cli\u003eOral, M., \u0026amp; Karakurt, N. (2022). The Impact of Psychological Hardiness on Intolerance of Uncertainty in University Students During the COVID‐19 Pandemic. \u003cem\u003eJournal of Community Psychology\u003c/em\u003e, \u003cem\u003e50\u003c/em\u003e(8), 3574\u0026ndash;3589. https://doi.org/10.1002/jcop.22856\u003c/li\u003e\n\u003cli\u003ePadr\u0026oacute;n, I., Fraga, I., Vieitez, L., Montes, C., \u0026amp; Romero, E. (2021). A study on the psychological wound of COVID-19 in university students. \u003cem\u003eFrontiers in Psychology\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e, 589927.\u003c/li\u003e\n\u003cli\u003ePakpour, A. H., Liu, C. H., Hou, W.-L., Chen, Y. P., Li, Y. P., Kuo, Y. J., Lin, C. Y., \u0026amp; Scarf, D. (2021). Comparing Fear of COVID-19 and Preventive COVID-19 Infection Behaviors Between Iranian and Taiwanese Older People: Early Reaction May Be a Key. \u003cem\u003eFrontiers in Public Health\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e. https://doi.org/10.3389/fpubh.2021.740333\u003c/li\u003e\n\u003cli\u003ePreacher, K. J., \u0026amp; Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. \u003cem\u003eBehavior Research Methods\u003c/em\u003e, \u003cem\u003e40\u003c/em\u003e(3), 879\u0026ndash;891.\u003c/li\u003e\n\u003cli\u003ePutri, H. E., \u0026amp; Etikariena, A. (2020). The Role of the Work-Family Conflict in the Relationship Between Work-Family Culture and Employee Well-Being. \u003cem\u003eJurnal Psikologi\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(3), 257\u0026ndash;268. https://doi.org/10.14710/jp.19.3.257-268\u003c/li\u003e\n\u003cli\u003eRadanović, A., Micić, I., Pavlović, S., \u0026amp; Krstić, K. (2021). Don\u0026rsquo;t think that kids aren\u0026rsquo;t noticing: Indirect pathways to children\u0026rsquo;s fear of COVID-19. \u003cem\u003eFrontiers in Psychology\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e, 635952. https://doi.org/10.3389/fpsyg.2021.635952\u003c/li\u003e\n\u003cli\u003eReis‐Dennis, S., Gerrity, M., \u0026amp; Geller, G. (2021). Tolerance for Uncertainty and Professional Development: A Normative Analysis. \u003cem\u003eJournal of General Internal Medicine\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e(8), 2408\u0026ndash;2413. https://doi.org/10.1007/s11606-020-06538-y\u003c/li\u003e\n\u003cli\u003eSalari, N., Khazaie, H., Hosseinian-Far, A., Khaledi-Paveh, B., Kazeminia, M., Mohammadi, M., Shohaimi, S., Daneshkhah, A., \u0026amp; Eskandari, S. (2020). The prevalence of stress, anxiety and depression within front-line healthcare workers caring for COVID-19 patients: A systematic review and meta-regression. \u003cem\u003eHuman Resources for Health\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e, 1\u0026ndash;14.\u003c/li\u003e\n\u003cli\u003eSari\u0026ccedil;am, H. (2014). Belirsizliğe tahamm\u0026uuml;ls\u0026uuml;zl\u0026uuml;k \u0026ouml;l\u0026ccedil;eği (BT\u0026Ouml;-12) T\u0026uuml;rk\u0026ccedil;e formu: Ge\u0026ccedil;erlik ve g\u0026uuml;venirlik \u0026ccedil;alişmasi. \u003cem\u003eRoute Educational and Social Science Journal\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(3), 148\u0026ndash;148. https://doi.org/10.17121/ressjournal.109\u003c/li\u003e\n\u003cli\u003eSatıcı, S. A., Kayiş, A. R., Satıcı, B., Griffiths, M. D., \u0026amp; Can, G. (2020). Resilience, Hope, and Subjective Happiness Among the Turkish Population: Fear of COVID-19 as a Mediator. \u003cem\u003eInternational Journal of Mental Health and Addiction\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(2), 803\u0026ndash;818. https://doi.org/10.1007/s11469-020-00443-5\u003c/li\u003e\n\u003cli\u003eSe\u0026ccedil;er, İ., \u0026amp; Ulaş, S. (2020). The mediator role of academic resilience in the relationship of anxiety sensitivity, social and adaptive functioning, and school refusal with school attachment in high school students. \u003cem\u003eFrontiers in Psychology\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e, 557.\u003c/li\u003e\n\u003cli\u003eShi, J., Ma, G., Liu, X., \u0026amp; Jin, Y. (2024). Social isolation and adolescents\u0026rsquo; psychological wellbeing during the COVID-19 epidemic: The mediating effect of loneliness and the moderating effect of resilience. \u003cem\u003eCurrent Psychology\u003c/em\u003e, \u003cem\u003e43\u003c/em\u003e(19), 17163\u0026ndash;17174. https://doi.org/10.1007/s12144-024-05637-x\u003c/li\u003e\n\u003cli\u003eShrestha, N. (2020). Detecting multicollinearity in regression analysis. \u003cem\u003eAmerican Journal of Applied Mathematics and Statistics\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(2), 39\u0026ndash;42.\u003c/li\u003e\n\u003cli\u003eSon, C., Hegde, S., Smith, A., Wang, X., \u0026amp; Sasangohar, F. (2020). Effects of COVID-19 on college students\u0026rsquo; mental health in the United States: Interview survey study. \u003cem\u003eJournal of Medical Internet Research\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(9), e21279.\u003c/li\u003e\n\u003cli\u003eSun, J. C., \u0026amp; Rueda, R. (2012). Situational interest, computer self‐efficacy and self‐regulation: Their impact on student engagement in distance education. \u003cem\u003eBritish Journal of Educational Technology\u003c/em\u003e, \u003cem\u003e43\u003c/em\u003e(2), 191\u0026ndash;204.\u003c/li\u003e\n\u003cli\u003eTelef, B. B. (2013). Psikolojik iyi oluş \u0026ouml;l\u0026ccedil;eği: T\u0026uuml;rk\u0026ccedil;eye uyarlama, ge\u0026ccedil;erlik ve g\u0026uuml;venirlik \u0026ccedil;alışması. \u003cem\u003eHacettepe \u0026Uuml;niversitesi Eğitim Fak\u0026uuml;ltesi Dergisi\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(28\u0026ndash;3), 374\u0026ndash;384.\u003c/li\u003e\n\u003cli\u003eWan, P., Hu, J., Su, C., \u0026amp; Li, Q. (2024). Impact of intolerance of uncertainty on depression in college students during the COVID-19 pandemic: Coping strategies as mediators. \u003cem\u003eINQUIRY: The Journal of Health Care Organization, Provision, and Financing\u003c/em\u003e, \u003cem\u003e61\u003c/em\u003e, 00469580241273119.\u003c/li\u003e\n\u003cli\u003eYang, Y., \u0026amp; Wang, H. (2023). Fear of COVID-19 and Anxiety: Serial mediation by trust in the government and hope. \u003cem\u003ePsychology Research and Behavior Management\u003c/em\u003e, \u003cem\u003eVolume 16\u003c/em\u003e, 963\u0026ndash;970. https://doi.org/10.2147/prbm.s399466\u003c/li\u003e\n\u003cli\u003eZarić, R. Ž., Zarić, M., Čanović, P., Janković, S. M., Stojadinovic, M., Zornić, N., Ne\u0026scaron;ić, J., Spasić, M., Jovanović, D., Jug, M., Jakovljevic, S., \u0026amp; Pejčić, A. V. (2022). Validation of the Fear of COVID-19 Scale in a Central Balkan Country\u0026mdash;Serbia. \u003cem\u003eFrontiers in Public Health\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e. https://doi.org/10.3389/fpubh.2022.972668\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Fear of COVID-19, intolerance of uncertainty, student engagement, psychological well-being","lastPublishedDoi":"10.21203/rs.3.rs-5968746/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5968746/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDue to the coronavirus disease pandemic, most countries have made rapid decisions on various topics. Regarding education, to prevent the spread of coronavirus, it was decided to replace face-to-face learning. In T\u0026uuml;rkiye, universities started offering online classes in March 2019. This transition has raised concerns regarding student engagement in online learning environments (SEOLE) and psychological well-being (PWB) due to the fear of COVID-19 (FC-19) as a psychological construct. Furthermore, intolerance of uncertainty (IU) has emerged as a critical mediator influencing these dynamics. Four models were proposed to examine these relationships. In Models 1 and 2, intolerance of uncertainty mediates the relationship between fear of COVID-19 and student engagement in online learning environments, with age, gender, class level, and perceived success included as control variables. Models 3 and 4 investigate how intolerance of uncertainty mediates the relationship between fear of COVID-19 and psychological well-being, using the same control variables. By synthesizing empirical studies and theoretical frameworks, we elucidate mechanisms that may mitigate the adverse psychological effects of the pandemic among students.\u003c/p\u003e","manuscriptTitle":"Investigating the Relationship Among Fear of COVID-19, Student Engagements in Online Learning Environment and Psychological Well-Being: The Mediating Role of Intolerance of Uncertainty","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-10 13:53:34","doi":"10.21203/rs.3.rs-5968746/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"37f2682f-a886-4bff-9d57-2c385a5fa11f","owner":[],"postedDate":"April 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-21T19:38:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-10 13:53:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5968746","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5968746","identity":"rs-5968746","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}