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The Association between Intolerance of Uncertainty and Mental Health Symptoms among 86,767 Chinese College Students during the COVID-19 Pandemic: A Latent Profile Analysis

Published online by Cambridge University Press:  27 October 2025

Zijie Ma ,
Yujing Chen ,
Yishuai Deng ,
Cong Luo ,
Lili Liu ,
Andi Huang ,
Xiayong Wang ,
Jiahong Li  and
Jingbo Zhao Open the ORCID record for Jingbo Zhao [Opens in a new window]
Zijie Ma
Affiliation:
Department of Psychology, School of Public Health, Southern Medical University , Guangzhou China Mental Health Education and Counseling Center, School of Public Health, Southern Medical University , Guangzhou, China
Yujing Chen
Affiliation:
Department of Psychology, School of Public Health, Southern Medical University , Guangzhou China
Yishuai Deng
Affiliation:
Department of Psychology, School of Public Health, Southern Medical University , Guangzhou China
Cong Luo
Affiliation:
Department of Psychology, School of Public Health, Southern Medical University , Guangzhou China
Lili Liu
Affiliation:
Department of Psychology, School of Public Health, Southern Medical University , Guangzhou China
Andi Huang
Affiliation:
Department of Psychology, School of Public Health, Southern Medical University , Guangzhou China
Xiayong Wang
Affiliation:
Department of Psychology, School of Public Health, Southern Medical University , Guangzhou China
Jiahong Li
Affiliation:
Department of Psychology, School of Public Health, Southern Medical University , Guangzhou China
Jingbo Zhao*
Affiliation:
Department of Psychology, School of Public Health, Southern Medical University , Guangzhou China Mental Health Education and Counseling Center, School of Public Health, Southern Medical University , Guangzhou, China
*
Corresponding author: Jingbo Zhao; Email: mingtian@smu.edu.cn
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Abstract

Objective

This study aimed to investigate the individual characteristics of intolerance of uncertainty (IU) and its association with mental health symptoms among Chinese college students during COVID-19.

Methods

In total, 86,767 students completed the online survey in Guangdong province in June 2021. Data collected including socio-demographic and COVID-19-related information, IU, and mental health symptoms (depression, anxiety, insomnia, and suicidal ideation). Latent profile analysis was used to classify IU subgroups. Logistic regression was used to identify IU risk factors.

Results

Four IU subgroups were identified, named low IU (n = 9,197, 10.6%), medium-low IU (n = 25,514, 29.4%), medium-high IU (n = 38,805, 44.7%), and high IU (n = 13,251, 15.3%). Scores of mental health symptoms varied from the degree of IU in the latent profiles. Mental health status was the worst in the high IU group. In addition, females, freshmen, and those perceiving more impacts from COVID-19 and spending longer time surfing COVID-19 information online were at risk of high IU.

Conclusions

Our findings showed that individuals differ in the total degree of intolerance of the uncertainties. Students with high IU were associated with worse mental health symptoms. Thus, taking actions to target individuals with high IU and developing their adaptive coping strategies are imperative during pandemics.

Keywords

Intolerance of uncertaintymental healthcollege studentsSARS-CoV-2

Information

Type
Original Research
Information
Disaster Medicine and Public Health Preparedness , Volume 19 , 2025 , e303
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of Society for Disaster Medicine and Public Health, Inc

Introduction

The long-lasting COVID-19 pandemic had a severe impact on mental health and triggered mental disorders with both short- and long-term effects.Reference Holmes, O’Connor and Perry1, Reference Li, Zhao and Ma2 Poor mental health status was found among college students during the pandemic. They encounter life events with tremendous uncertainty, such as sudden isolation, disruption of normal social life, and disruption of schooling, making them more vulnerable to mental health problems.Reference Auerbach, Mortier and Bruffaerts3–Reference Brooks, Webster and Smith5 Considering Chinese students have been through strict quarantine measures during the pandemic, the disruption of multi-aspect life has brought great uncertainty to students’ campus life, which could nudge students into mental health symptoms involving anxiety, fear, insomnia, depression, and suicidal ideation (SI).Reference Wang, Zhang, Zhao, Zhang and Jiang6–Reference Zhang, Wang and Zhao9

Studies indicated uncertainty about COVID-19 has heavily impacted the mental health of different groups of people.Reference Bavolar, Kacmar and Hricova10 Intolerance of uncertainty (IU) is the dispositional inability to tolerate negative uncertain situations, which was identified as a transdiagnostic factor underlying a range of mental disorders including anxiety disorders, obsessive-compulsive disorder, major depression disorder, etc.Reference Carleton11, Reference McEvoy, Hyett, Shihata, Price and Strachan12 Cognitive-behavioral model of IU proposed that high levels of IU increased the biased and threatening information processing of life events. Catastrophic interpretation of the uncertainties directly led to maladaptive emotion regulation strategies and impacted mental health.Reference Hebert and Dugas13 Therefore, IU was considered a significant trait in understanding individuals’ psychological reactions during COVID-19.Reference Bavolar, Kacmar and Hricova10 Studies have found that IU was associated with psychological symptoms during the pandemic like depression,Reference Di Blasi, Gullo and Mancinelli14 anxiety,Reference Bavolar, Kacmar and Hricova10, Reference Shabani, Mohsenabadi, Gharraee, Shayanfar, Corcoran and McKay15 suicidal ideation,Reference Allan, Volarov and Koscinski16 and sleep disturbance.Reference Drummond, Wiley, Boardman, Aidman, Kensinger and Cunningham17 Studies during COVID-19 mostly focused on the mental symptoms, which could lead to overlooking some subclinical populations. IU was the individuals’ dispositional reactions to uncertainty situations, which were stable across samples with or without mental disorders. Moreover, given the transdiagnostic nature of IU, focusing on the individual differences of IU would be more effective to target populations at risk of mental disorders.

Studies discussing individual characteristics of IU didn’t achieve a consensus, due to the number of IU subgroups varying from 2 to 4. However, their result indicated a common fact that the vulnerability to mental symptoms differed in IU subgroups.Reference Volarov, Saulnier, Allan, Shapiro and Mihić18–Reference Oglesby, Allan, Short, Raines and Schmidt20 Besides, mental symptoms in these studies were limited to anxiety- and depression-related symptoms. The association between insomnia, SI, and IU subgroups was rarely explored. Furthermore, individual characteristics of IU were mostly studied in Western countries. Subgroups of IU and its association with mental health symptoms remain unclear in China.

Therefore, we conducted this survey to investigate individual characteristics of IU among Chinese college students and its association with depression, anxiety, insomnia, and SI. Latent profile analysis (LPA) was used in the current study, which was conducive to exploring the characteristics of psychological symptoms in subgroups of IU.Reference Spurk, Hirschi, Wang, Valero and Kauffeld21 Furthermore, risk factors for IU subgroups were also explored. This study aimed to provide practical advice for authorities to monitor and maintain the mental health of certain populations that were intolerant of uncertainties during public health emergency outbreaks like COVID-19.

Materials and Methods

Participants

Our survey was conducted when Guangdong Province had new local outbreaks from June 10 to 18, 2021. At that time, new cases were reported in communities after almost a year without COVID-19 infection cases being reported in Guangdong. During the survey, all schools have implemented strict lockdown measures and students’ campus life was impacted. Data was collected through So-jump (an online survey instrument) using a web link or quick response (QR) code. A total of 93,413 students from 22 universities in Guangdong province participated in the survey. In summary, 86,767 (92.89%) students completed the questionnaires. The average age of our sample was 20.11 years (SD = 1.56).

All procedures were conducted in accordance with the ethical standards of the institutional research committee (Ethics_No._SCNU-PSY-2020-01-001). Electronic informed consents were obtained online, and all participants were assured that they could quit at any time.

Measures

Demographic and COVID-19 information

Demographic information included age, sex (male or female), grade (freshman, sophomore, junior, senior, fifth year of college and above), single child status (yes or no), physical health, and mental state (very poor, poor, moderate, good, very good). COVID-19-related items included the length of surfing pandemic-related information and perception of impacts from COVID-19.

Intolerance of uncertainty

The Chinese version of the intolerance of uncertainty scale (IUS) was used in this study.Reference Zhang, Song, Gao, Wu, Song and Miao22 It assessed reactions to ambiguous conditions, uncertainties, and forthcoming events, representing levels of IU. IUS was a 5-point scale comprising 12 items. The total score of IUS ranged from 12 to 60. Individuals with a higher total score on the scale indicated a higher level of IU. In this study, Cronbach’s ɑ was 0.90.

Mental health symptoms

The following mental health symptoms were collected: (1) depression, using the 8-item patient health questionnaire (PHQ-8) to reflect probable clinical depression within 2 weeks,Reference Wu, Levis and Riehm23 (2) anxiety, using the 7-item generalized anxiety disorder scale (GAD-7) to assess anxiety symptoms in the past 2 weeks,Reference Spitzer, Kroenke, Williams and Löwe24 (3) insomnia, using the 8-item youth self-rating insomnia scale (YSIS) to evaluated current insomnia symptoms,Reference Liu, Yang, Liu, Luo, Fan and Jia25 and (4) suicidal ideation, using the ninth of item of the 9-item patient health questionnaire (PHQ-9), a questions by asking the frequency of suicidal ideation during the past 2 weeks.Reference Schulberg, Lee and Bruce26

Positive symptoms were scores of the scales above the threshold identified (PHQ-8 ≥10 of 24; GAD-7 ≥10 of 21; YSIS ≥26 of 40) and the presence of SI.Reference Bauer, Chan, Huang, Vannoy and Unützer27, Reference Wang, Ross and Zhou28 The Cronbach’s ɑ of the 3 scales in the sample were all greater than 0.91.

Statistical analyses

LPA was performed on all items of the IUS-12 to identify subgroups of college students with similar response patterns, capturing both quantitative differences among individuals and qualitative distinctions between subgroups. The procedure objectively determines population subgroup proportions while minimizing within-group variance and maximizing between-group differences. LPA was conducted by Mplus 8.3 using the robust maximum likelihood (MLR) estimation method. The final model selection was guided by a combination of statistical fit indices and substantive considerations.Reference Ye, He and Chen29, Reference Ye, Chen and Chen30 We examined information criteria, including the Akaike information criterion (AIC), Bayesian information criterion (BIC), and sample-size adjusted BIC (aBIC), where lower values consistently indicated better model fit. Profile separation was assessed through entropy values, with values exceeding 0.80 demonstrating adequate classification accuracy and values approaching 1.0 reflecting optimal profile differentiation.Reference Celeux and Soromenho31 The Lo-Mendell-Rubin likelihood ratio test (LMRT) and bootstrapped likelihood ratio test (BLRT) provided further validation, where statistically significant improvements (P <.01) in model fit supported the selection of models with additional profiles.Reference Nylund, Asparouhov and Muthén32 These quantitative indicators were carefully balanced against essential qualitative considerations, including the psychological meaningfulness of each profile, clinically interpretable group sizes (maintaining at least 10% of the sample in each class), and adherence to the principle of parsimony, ensuring both methodological rigor and practical relevance in our latent profile solution.Reference Ye, Chen and Chen30

Differences in mental health symptoms between IU subgroups were examined by one-way ANOVA. The associations between IU subgroups and covariates were tested by chi-square test, and logistic regression models identified risk factors for IU subgroups. The estimates of the strengths of associations were presented as odds ratios (ORs) with 95% confidence intervals (CIs). Answers to the “poor” and “very poor” of “physical health state” were very few, which were summarized during bivariate analyses.

Data analyses were performed using SPSS 25.0. LPA was calculated by Mplus 8.3. The level of significance was set at 0.05, and all tests were 2-sided.

Results

Latent Profile Analysis

The model fits of LPA are presented in Table 1. According to the scree plot of aBIC (See Figure 1),Reference Petras and Masyn33 which was the most accurate information criterion,Reference Yang34 and other criteria used for model selection (entropy, AIC, BIC, simplicity and the practical significance of the classification, results from prior studies), model 4 was selected in the current study, which had good classification quality (see Figure S1, Table S1).

Table 1.

Model fit of latent profile analysis of IU

Figure 1.

The 4-profile model and mean values of 12 items of IUS.

Note: Inhibitory IU and prospective IU were the dimensions of IUS-12.

Four profile was labeled in model 4: (1) the high IU group (n = 13,251, 15.3%); (2) the medium-high IU (M-H) group (n = 38,805, 44.7%); (3) the medium-low IU (M-L) group (n = 25,514, 29.4%); (4) the low IU group (n = 9,197, 10.6%). Furthermore, results from the one-way ANOVA test showed statistically significant differences in the scores of IU between subgroups (see Table S2).

Mental Health Symptoms among IU Subgroups

In Figure 2, the results indicated that the high IU group scored the highest in depression (F = 7440.32, P <.001), anxiety (F = 6212.56, P <.001), insomnia (F = 5529.97, P <.001), and SI (F = 1259.98, P <.001). Other details of IU subgroups could be seen in Table S3.

Figure 2.

Differences in mental health symptoms among subgroups of Intolerance of Uncertainty.

Note: *** indicates that the mean difference is statistically significant (P <.001).

Logistic Regression Analysis

The results of multinomial logistic regression analysis are presented in Table 2. The low IU group was set as the reference group and compared with other IU subgroups. Depression, anxiety, insomnia, and SI were more prevalent in the high IU group. Freshmen tended to be more intolerant of uncertainty than students in other grade levels. Moreover, compared with a very good mental and physical health states, students with good and moderate mental and physical health states had an increased risk of high levels of IU. In addition, those who spend longer time surfing COVID-19 information online and reported more impact from COVID-19 were at higher risk of high IU.

Table 2.

Factors associated with the high IU group according to multinomial logistic regression

Note: * P <.05, ** P <.01, *** P <.001.

Discussion

In the shadow of COVID-19 uncertainty, students with high IU tended to obtain negative mental outcomes. Four subgroups of IU were classified based on LPA: the high IU group, the M-H IU group, the M-L IU group, and the low IU group. Furthermore, we found that mental health symptoms were worse in the groups with higher levels of IU. In addition, risk factors associated with IU subgroups were also discussed.

In this study, mental health symptoms worsened in the subgroups with higher levels of IU, which was similar to the results from Western countries.Reference Volarov, Saulnier, Allan, Shapiro and Mihić18, Reference Oglesby, Allan, Short, Raines and Schmidt20 These results aligned with taxometric studies, which supported conceptualizing IU as varying along a continuum.Reference Fergus and Carleton35 However, subgroups of IU in the current study differed from Boelen and Lenferink,Reference Boelen and Lenferink19 which was mainly because they have converted IU into a binary categorical variable. In addition, the association between IU and mental health symptoms emphasized IU as a transdiagnostic factor for different mental health symptoms.Reference McEvoy, Hyett, Shihata, Price and Strachan12, Reference Del Valle, Andrés, Urquijo, Yerro-Avincetto, López-Morales and Canet-Juric36, Reference Karnick, Fergerson and Caulfield37

IU is a dispositional trait that reflects an individual’s inability to tolerate ambiguous or uncertain situations, often leading to maladaptive cognitive and emotional responses.Reference Carleton11 Extensive research has demonstrated that individuals with higher IU tend to interpret and react to uncertainty negatively.Reference Morriss, Butler and Ellett38 Specifically, they are more likely to assume that uncertain situations will result in adverse outcomes, thereby amplifying their experience of negative affect.Reference Anderson, Carleton, Diefenbach and Han39 In the shadow of COVID-19 uncertainties, restrictions imposed during the pandemic brought unexpected disruptions to multi-aspects of Chinese students’ lives, which made students who are intolerant of uncertainties feel the disorder of their cognitive structure. They tended to notice more, to memorize more, and to catastrophically interpret uncertain situations, due to the biased information processing of IU.Reference Fergus and Carleton35, Reference Dugas, Hedayati, Karavidas, Buhr, Francis and Phillips40, Reference Fergus, Bardeen and Wu41 Moreover, studies indicate that those with high IU exhibit greater reliance on maladaptive emotion regulation strategies (e.g., cognitive and behavioral avoidance, Catastrophizing) and struggle more profoundly with emotional regulation.Reference Morriss42, Reference Sahib, Chen, Cárdenas and Calear43 These cognitive and emotional tendencies collectively heighten their susceptibility to a range of mental and psychological problems.

Besides, we found that IU was more prevalent in females, indicating the sex difference in IU.Reference Zhang, Song, Gao, Wu, Song and Miao22, Reference Ye, Chen and Chen30 This pattern may reflect culturally shaped emotional expression norms in both Western and East Asian cultures,Reference Gong, Wong and Wang44 where women are socialized to more readily acknowledge “powerless” emotions like fear and sadness when facing uncontrollable stressors.Reference Brody, Lovas and Hay45 The concept of power in emotion has been shown in the fact that if a negative event is appraised as caused by unknown factors, being out of one’s control, and beyond one’s coping resources, the experienced emotion is likely to be sadness or fear. This appraisal, and the resultant emotional expression, is perceived as displaying powerlessness and vulnerability.Reference Robichaud, Dugas and Conway46 If women do in fact express these emotions to a greater extent than men, they may be more likely to believe a problem caused by uncertainties is out of their control and beyond their ability to cope,Reference Timmers, Fischer and Manstead47 which could increase the risks of intolerance of uncertainties.

Freshmen were at risk of high IU. While IU was indeed conceptualized as a transdiagnostic trait-like factor,Reference Carleton11 extant research suggests that it also varies across developmental stages and in response to environmental demands. College students fall within the developmental phase of emerging adulthood (typically aged 18-26), which is a phase characterized by heightened instability, identity exploration, and exposure to novel uncertainties (e.g., academic transitions, independent living).Reference Arnett, Arnett and Tanner48 Studies investigated IU in emerging adulthood found that IU tended to decrease with age in young adults.Reference Iannattone, Spaggiari, Di Riso and Bottesi49, Reference Yeler, Berber, Özdoğan and Çok50 A lifespan study further confirmed that IU changed across development, declining from adolescence to old age.Reference Okayama, Minihan and Andrews51 During emerging adulthood, freshmen were lack of psychological flexibility,Reference Okayama, Minihan and Andrews51 but were especially required to work harder to adapt to the novel uncertainties, and to make considerable efforts in managing their mental health.Reference Wang, Jiang, Li, Zhang and Xiao52 They had to adapt to a lot of uncertainties when facing the new college environment, not to mention the disruptions of the COVID-19 pandemic they were suffering,Reference Copeland, McGinnis and Bai53 which could increase the risks of high IU among this age group. Furthermore, poor physical and mental state also increased the risk of high IU. Pandemics would result in deep uncertain health risks and prompt health-related anxiety.Reference Taha, Matheson, Cronin and Anisman54 As a result, students with poor physical and mental status tended to display high IU during COVID-19. Therefore, providing sufficient care access to freshmen and those with mental and physical illness is necessary to help them resist the disruptions from the pandemics.

Spending longer time surfing information about COVID-19 was associated with higher IU levels. Prior studies found that high IU individuals tend to perform maladaptive behaviors like excessive information-seeking to increase certainty and reduce uncertainty-related distress.Reference Bottesi, Carraro, Martignon, Cerea and Ghisi55–Reference Bottesi, Gürdere, Cerea, Sica and Ghisi57 Moreover, students with high IU were more likely to report more impacts from the COVID-19 pandemic. IU was related to information-processing bias, and individuals with high IU tended to perceive pandemics as more aversive and perform more maladaptive emotion regulation strategies.Reference Fergus and Carleton35 Therefore, in order to buffer against the negative response that uncertain situations arise, increasing adaptive emotion regulation strategies such as cognitive reappraisal is effective and efficient to achieve a degree of certainty and to maintain mental health in students’ daily life under the shadow of pandemics.Reference Hebert and Dugas13, Reference Wang, Goldenberg and Dorison58, Reference Sahib, Chen, Cárdenas and Calear59

Based on our results, females, freshmen, and those who longer time surfing information about COVID-19 and report more impacts were associated with higher risks of high IU. These high-risk populations warrant prioritized interventions that specifically address their characteristic vulnerabilities. To effectively support students with high IU, evidence-based interventions should target their core difficulties in processing uncertainty. Cognitive Behavioral Therapy adapted for IU (CBT-IU) offers a comprehensive framework, combining cognitive restructuring with behavioral experiments to build tolerance for uncertainty.Reference Hebert and Dugas13, Reference Miller and McGuire60 Moreover, Hebert and DugasReference Hebert and Dugas13 indicated that the core psychopathological mechanism underlying IU is the catastrophic interpretation of uncertainties. These approaches can be complemented by interpretation bias modification training to directly address distorted threat appraisals, alongside emotion regulation strategies like cognitive reappraisal to reduce the emotional impact of uncertain situations on high IU students.Reference Gober, Lazarov and Bar-Haim61, Reference Moore, Gillanders and Stuart62

There were also some limitations in the present study. Firstly, this study was a cross-sectional study, which limited our ability to draw causal inferences. In the future, longitudinal research should be done to provide more direct evidence. Secondly, our data only involved Guangdong Province, limiting the generalizability of the results. Thirdly, psychological symptoms were screened through questionnaires. This may lead to reporting bias. In particular, SI was measured through the ninth item of PHQ-9, which may limit the explanation of the SI results.Reference Wang, Ross and Zhou28, Reference Na, Yaramala and Kim63 Limitations notwithstanding, the present study provides an important step in further revealing the relationship between IU and mental health during COVID-19, suggesting that monitoring and intervening in pandemic-related uncertainties and target groups at risk of high IU could be useful in alleviating the mental health crises during pandemics.

Conclusion

In our study, 4 subgroups of IU were identified. Our findings showed that mental health symptoms varied according to the degree of intolerance of uncertainty, and students with high IU were associated with worse mental health during the COVID-19 pandemic. Females, freshmen, those perceiving more impacts, and those spending longer time surfing pandemic-related information online had increased risk of high IU. Therefore, monitoring degree of intolerance of uncertainty would be effective for preventing mental health crises during pandemics.

Supplementary material

The supplementary material for this article can be http://doi.org/10.1017/dmp.2025.10220.

Competing interests

The author(s) declare none.

Footnotes

Zijie, Yujing, and Yishuai, are contributed equally to this work

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Figure 0

Table 1. Model fit of latent profile analysis of IU

Figure 1

Figure 1. The 4-profile model and mean values of 12 items of IUS.Note: Inhibitory IU and prospective IU were the dimensions of IUS-12.

Figure 2

Figure 2. Differences in mental health symptoms among subgroups of Intolerance of Uncertainty.Note: *** indicates that the mean difference is statistically significant (P <.001).

Figure 3

Table 2. Factors associated with the high IU group according to multinomial logistic regression

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