Haojie Fu
Bin Wang- 1Psychological Education and Counseling Center, Southwest University of Science and Technology, Mianyang, China
- 2Psychology Department, Southwest University of Science and Technology, Mianyang, China
- 3School of Humanities and Social Science, University of Science and Technology of China, Hefei, China
This study aims to explore the influence mechanism of COVID-19 risk perception on anxiety in emerging adulthood in the context of public health events of the second round of COVID-19 outbreaks and provide support for exploring the path of mental health after the normalization of the epidemic situation. An online questionnaire, combined with community social work, was used in this study, and data of 522 emerging adults were collected in February 2021. The Perceived Risk of COVID-19 pandemic scale (PRCPS), the generalized anxiety disorder 7-item (GAD-7) scale, the scale of affect balance, and the connor-davidson resilience scale (CD-RISC) were used to investigate. The results showed that: first, the risk perception of COVID-19 in early adulthood was positively predictive of anxiety symptoms [B = 0.110, p < 0.05, 95%CI = (0.042, 0.176)]. Second, the affective quality of life plays a mediating role between the risk perception of COVID-19 and anxiety [B = 0.108, 95%CI = (0.060, 0.161)]. Thirdly, resilience plays a moderating role between the risk perception of COVID-19 and anxiety, the higher the resilience of emerging adulthood, the weaker effects of the risk perception of COVID-19 negative prediction of anxiety [B = −0.110, p < 0.001, 95%CI = (−0.170, −0.049)]. Therefore, to control the anxiety of emerging adulthood in public health events, we should pay attention to the propaganda and management of epidemic information, improve the quality of life, and attention should be paid to the emerging adulthood with low resilience.
Introduction
The COVID-19 pandemic, which outbreaks in late 2019 has greatly impacted the normal operation of the world economy and society. The WHO announced the outbreak of COVID-19 as a public health emergency of international concern. In the winter and spring season of 2020–2021, large-scale local outbreaks were detected in the rural areas of Hebei, Jilin, and Heilongjiang provinces, and more than 2,000 cases were reported in total (Gao, 2021). Viral panic, conspiracy ideation, and contagious fear during the COVID-19 pandemic filled everyone’s life and perceived lot of risks (Bratu, 2020; Lăzăroiu and Adams, 2020; Popescu Ljungholm and Olah, 2020). Risk perception in terms of virus anxiety and emotional contagion shaping the COVID-19 pandemic fear (Dobson-Lohman and Potcovaru, 2020; Lăzăroiu et al., 2020; Nemțeanu et al., 2021). In addition to posing a major threat to physical health, the COVID-19 pandemic also poses a threat to mental health due to people’s long-term fear and uncertainty during the epidemic (Adhanom Ghebreyesus, 2020; Hyland et al., 2020). People get depression, anxiety, and psychological stress (Duncan, 2020). And due to the COVID-19, about 24.9% of college students have experienced anxiety (Cao et al., 2020).
Especially for young people, emerging adults are most impaired (Wirkner et al., 2021). According to Arnett (2007), emerging adulthood (18–25 years old) is the age of instability due to residential, love, work, and education changes and a self-focused age because emerging adults have little in the way of social obligations, duties, and commitments to others, which leaves them with a great deal of autonomy in running their own lives. The mental health problems of emerging adults in their early adulthood may be more serious. The uniqueness of early adulthood is that they do not think they are adults or teenagers. Most of them do not have a stable family and living environment (Arnett, 2000). Therefore, emerging adults are one of the groups most may affected by the COVID-19 epidemic, and they have not yet obtained a stable occupational environment and spouse support. When dealing with major public crisis events, the insufficient coping ability of young adults in adulthood will cause mental health problems (Gariepy et al., 2016; Li et al., 2021). A longitudinal study of depression and anxiety among young adults affected by the epidemic in the United States found that they were indeed under great pressure, and the psychological and social stressors brought by the COVID-19 epidemic related to their depression and anxiety (Kujawa et al., 2020).
Risk perception is an individual’s subjective feeling and understanding of external objective stressors, which will be affected by psychological, social, and even cultural factors (Xi et al., 2020). Studies have shown that the perceived risk of the COVID-19 pandemic (PRCP) can positively predict Generalized Anxiety Disorder (GAD; Oyetunji et al., 2021; Yin et al., 2021). But there are also different results, Musche’s study on diabetic patients found that their risk perception is higher than that of the control group, but the level of GAD is no different from that of the control group (Musche et al., 2021). Therefore, PRCP may have a moderating variable in predicting GAD. According to the psychological stress model, combined with stressors, appraisal, social environment, and disorders, which have helped to elucidate the determinants of health in which stress can be an integrative variable (Lemyre and Lalande-Markon, 2009). And Kumpfer and Bluth (2004) resilience framework operationalized resilience as an internal factor which has the characteristic of a dynamic change process by combining the construct internal self-characteristics and resilience processes. Resilience helps reduce the negative impact of adversity on individuals and improve adaptation and growth (Huang et al., 2019). In different individuals, even the stress induced by the same stimulus with different situation will have different effects. Thus, the individual’s resilience factors can help them deal with the negative effects of stress (Wilks and Croom, 2008).
According to the common-sense model (CSM) in risk perception theory, the CSM hypothesizes that individuals create mental representations of their illness based on the concrete and abstract sources of information available to them in order to make sense of and manage the problem. People typically make simultaneous cognitive and emotional representations of their illness (Hagger and Orbell, 2003). Combined with the model of emotion dysregulation proposed by Mennin et al., heightened intensity of emotions, poor understanding of emotions, negative reactivity to emotions, and maladaptive management of emotions—best reflected the structure of four commonly used measures of emotion function and dysregulation, it is believed that the vicious circle mode of emotional dysregulation will bring more avoidance behavior, lead to negative emotional experience and the concurrent symptoms of GAD (Mennin et al., 2007). And emotion can be used as an mediating variable when GAD is affected (Marganska et al., 2013). Therefore, emotional experience may play a mediating role in the model of PRCP affecting GAD.
H1: PRCP will have a positive correlation with GAD.
H2: Resilience will moderate the effect PRCP has on GAD.
H3: The effects of PRCP on GAD will be partially mediated by the IGA.
The overarching aim is to understand the conditional process that GAD is affected by PRCP through IGA, and the extent of GAD affected by PRCP is different for people with different levels of resilience. This study uses the “conditional process model,” a tool for understanding causal processes, to understand the mechanism of independent variables affecting the dependent variable proposed by Hayes (2013). It is used to estimate the direct and indirect pathways through which a variable transmits its effects, as well as to model how the size of those effects depends on (or are conditional on) the value(s) of one or more moderators (Hayes, 2013). We summarize our hypotheses in Figure 1.
Figure 1. Hypothesized conditional process model.
Materials and Methods
Participants
A total of 704 data were collected in February 2021 in the form of an online questionnaire and combined with community social work in Sichuan Province of China. There is no extensive epidemic in Sichuan, but there have been large-scale local outbreaks in rural areas of other provinces in China, such as Hebei, Jilin and Heilongjiang (Gao, 2021). People perceive the risk of the epidemic, but life has returned to normal. Invalid questionnaires were filtered or according to the following steps: Remove data with an answer time of fewer than 300 s, Find similar (according to IP, submission time, age, residence, etc.) to filter duplicate questionnaires, The Mahalanobis distance is used to exclude data outside the 0.001 standards (Aguinis et al., 2013). A total of 522 valid questionnaires were collected, with an effective rate of 74.15%. The included baseline sample was 37.2% men, 53.1% women, and 9.8% nonbinary. Mean age was 21.77 years (SD = 2.26). 7.8% were married, 91.8% were unmarried, and 0.2% divorced.
Measures
Perceived Risk of COVID-19 Pandemic Scale
Perceived Risk of COVID-19 Pandemic Scale was used to assess the PRCP of emerging adults (Xi et al., 2020). The scale consists of nine questions, which are divided into three dimensions: emotional feeling, e.g., I worry about getting infected with COVID-19 (None of the time, Rarely, Some of the time, A moderate amount of time, A lot of the time, All of the time), cognitive judgment, e.g., I am sure I will NOT get infected with COVID-19 (Strongly disagree, Disagree, Somewhat disagree, Somewhat agree, Agree, Strongly agree), and psychological representation of unusual severity, e.g., Getting COVID-19 is something I have (Never thought about, Rarely thought about, Thought about some of the time, Thought about often), options range Likert 4–6 points scoring, and 1 reverse scoring question. Cronbach’s α = 0.843. The Cronbach’s coefficient for the present study was 0.843 (total score).
Index of General Affect
Campbell’s Index of General Affect Scale was a tool to describe the emotional state of oneself over a while through positive and negative emotions (Campbell, 1976), it’s an absolute emotional state (Ash, 2000). There are eight items on the scale, to ask our respondents to react to a series of paired adjectives, describing their lives in positive or negative terms, presented in the semantic differential format. Thus, they were asked to describe their lives in general as falling at a point they chose in the space between interesting and boring, enjoyable and miserable, lonely and friendly, rewarding and disappointing, and the like from 1 to 7. The Cronbach’s coefficient for the present study was 0.961 (total score).
Generalized Anxiety Disorder-7
A Chinese version of the Generalized Anxiety Disorder-7 (GAD-7) scale was used to assess the subject’s anxiety symptoms. The GAD-7 has been previously used in Chinese populations and was found to have good reliability (Cronbach α = 0.90; Cronbach α = 0.90; Tong et al., 2016). In our study, seven items assess the frequency of anxiety symptoms over the past 2 weeks on a four-point Likert scale ranging from 0 (never) to 3 (nearly every day). The total score of GAD-7 ranged from 0 to 21, with increasing scores indicating more severity resulting from anxiety (Spitzer et al., 2006). The Cronbach’s coefficient for the present study was 0.956 (total score).
Resilience
The Connor-Davidson resilience scale (CD-RISC; Connor and Davidson, 2003) was used to evaluate the resilience in the present study. The scale was developed based on concepts of hardiness, adaptation, and stress endurance and validated in diverse samples. Initial factor analyses identified five factors: (1) notion of personal competence, high standards and tenacity, (2) trust in one’s instincts, tolerance of negative affect, and strengthening effects of stress, (3) positive acceptance of change, and secure relationships, (4) control, and (5) spiritual influences, and the Chinese version was tested by Yu and Zhang (2007). The scale has a total of 25 items and uses a five-point scoring, from “0 to 4” to indicate “not at all to almost always.” The Cronbach’s coefficient for the present study was 0.987 (total score).
Statistics
SPSS 24.0 was used for descriptive statistics, and the MATRIX macro compiled by Hayes et al. is used for inferential statistics (Hayes, 2013). Hypothesis 2 predicts that PRCP will positively influence GAD while hypotheses 3 predict that IGA will mediate the involvement PRCP GAD link. To test hypotheses 2 and 3, Preacher and Hayes’ (2008) mediation analysis, i.e., PROCESS Macro (model 5) was used. The specific steps are as follows: First, conduct common method deviation analysis; Second, descriptive analysis and Pearson correlation analysis of main variables; Third, use the PROCESS plug-in, select the independent variable, mediating variable, moderate variable, and dependent variable into the corresponding option box, in turn, select model 5, set the sample size to 5,000, select the nonparametric percentile bootstrap method for deviation correction, the confidence level of the confidence interval is 95%, and the grouping condition is mean and mean plus or minus a standard deviation.
Common Method Deviation Test
The Harman single factor method was used to detect the common method deviation. The exploratory factor analysis results of 49 items show that there are seven factors with eigenvalues higher than 1, and the variance interpretation rate of the first factor is 46.16% (<50%), indicating that there is no serious common method deviation in this study (Podsakoff et al., 2003; Malhotra et al., 2006).
Results
Descriptive Statistics and Correlations Among PRCP, IGA, Resilience, and GAD
Most of the correlations between the variables were statistically significant. The correlation coefficients had different signs depending on particular variables. The PRCP was positively associated with GAD, and negatively associated with IGA and resilience. The IGA was negatively related to GAD but positively related to resilience. The GAD was negatively related to resilience. The correlation between gender, age, marital status, PRCP, IGA, resilience, and GAD were analyzed. There is a significant correlation between demographic variables and other variables except for marital status with IGA and resilience. Therefore, gender, age, and marital status will be included in the control variables. See Table 1 for details. H1 was verified.
Table 1. Intercorrelations and descriptive statistics for gender, marital status, age, PRCP, IGA, GAD, and resilience (N = 522).
Conditional Process Analysis
The Bootstrap mediation effect test method was used to test concerning the conditional process model (Model 5) proposed by Hayes. The results were shown in Tables 2, 3.
Table 2. Mediation between different levels of resilience to GAD.
Table 3. Effect of the conditional process model.
Resilience score was divided according to M, M + SD, and M−SD and three elastic levels were obtained: medium, low and high. The direct impact of PRCP on GAD under different elastic levels was analyzed. As shown in Table 2 and Figure 2, it is found that under the high level of resilience, the direct effect of PRCP on GAD is not significant, and only under the medium and low level of resilience, the direct effect of PRCP on GAD is significant. The confidence intervals of 95% CI were [0.127, 0.312], [0.042, 0.177], and [−0.090, 0.089] at −1, 0 and +1 SD, respectively. The smaller the slope, the higher the impact of PRCP on GAD. H2 was verified.
As shown in Table 3, the indirect effect is 0.108, and the confidence interval 95% CI = [. 061, 0.158] does not include 0, indicating that the mediating effect is significant. It shows that PRCP has a positive predictive effect on anxiety GAD, and the IGA plays as a mediating variable between PRCP and IGA. H3 was verified.
Figure 2. The association between PRCP and GAD is moderated by resilience.
Discussion
The purpose of this study is to explore the impact of epidemic risk perception on anxiety after major public health events become normalized. Evaluating and controlling the mental state after public health events can effectively reduce the resulting diseases to be minimized (Sharot, 2011). We are mainly concerned about the 18–25-year-old emerging adults because most of them have not obtained a stable professional environment and spouse support. In the face of major public events, the lack of coping ability of young adults may bring corresponding mental health problems. It is found that the effects of PRCP on GAD will be partially mediated by the IGA, and emerging adults with lower resilience after they are perceiving the risk, they are more likely to be anxious.
Firstly, it is similar to previous studies our research results confirm the H1 that the GAD of emerging adults is significantly related to the PRCP (Oyetunji et al., 2021; Yin et al., 2021). For previous studies that do not support this result (Musche et al., 2021), we introduced resilience as a moderator variable to test. The final results support our H2 that resilience moderates the effect PRCP has on GAD. The direct effect of PRCP on GAD is not significant for people with a high level of resilience, but significant for people with a low level of resilience. From the perspective of resilience process theory, people with a high level of resilience have stronger dispositional resilience and are more likely to perceived more social support (Montpetit et al., 2010). In addition to social support, there is also the impact of individual ability. Dratva studied the relationship between College Students’ risk perception and GAD at Swiss university, shows that the GAD level of health students on campus is significantly lower (Dratva et al., 2020), expertise and experience on infection risks and health may have played a role. Resilience moderate the effect PRCP has on GAD explains the contradictions of previous studies. The research on diabetes patients is special for disease experience. From the perspective of Kumpfer and Bluth (2004) resilience framework theory, previous experience will make their resilience level higher. Therefore, even if their risk perception is high, there is no difference in anxiety under the effect of resilience. It helps researchers to understand the process mechanism of resilience and the mechanism of GAD caused by our perception in the face of stressful events. Therefore, in the specific work, we should pay attention to improving the level of resilience. The three-factor structure of resilience, social support, and perceived social support are key elements, as well as relevant health knowledge (Dratva et al., 2020), better education, and economic prosperity (Ruscio et al., 2017), etc. These factors can effectively reduce the incidence of GAD. For COVID-19, people know that they can have vaccines and medical care can be guaranteed (Jaspal and Breakwell, 2021), which are powerful and very effective channels to enhance resilience and reduce GAD.
For people with higher resilience, there is no significant correlation between their PRCP and GAD, combined with GAD’s emotional disorder theory, we know that GAD symptoms are mainly caused by emotional disorders (Mennin et al., 2007). Like Watson’s research showing emotional disorder strong and consistent associations with GAD (Watson and Naragon-Gainey, 2014). Therefore, PRCP may affect GAD through the emotional state. Therefore, the third hypothesis is that the IGA, which contains both positive and negative emotional state index scores, plays a meditating role in PRCP. The results of the current study support H3. That PRCP can lead to a decline in our IGA, which will slide from positive to negative. In this sliding process, the GAD symptoms will also increase as a result of the change of emotional state, which is consistent with previous studies (Ning et al., 2020; Mohammadkhani et al., 2021). Therefore, in the specific work, if we can pay attention to the scientific and rationality of risk publicity and improve the IGA, we can effectively block the formation path of GAD from PRCP and help to reduce the occurrence of GAD.
The conditional process model of H2 and H3 in this study shows that improving the level of residents’ resilience, paying attention to the active publicity in the process of epidemic risk, and regulating residents’ emotions can help to reduce GAD.
This study confirms two very effective hypotheses, but there are also some limitations. The moderator variables of the mediating path have not been explored clearly. The subject of this study is aimed at emerging adults. This study used a cross-sectional data set, which is liable as far as behavioral analysis is concerned. Such data cannot be used to explain cause and effect explicitly (Maxwell et al., 2011). It is the age of identity explorations, the age of instability (Arnett, 2007). However, for teenagers and adult groups, whether their GAD will be affected by the same conditional process model needs further research and discussion.
Conclusion
In the face of major public health events, people will feel very anxious. It can be seen from the conditional process obtained, the higher PRCP, the more GAD. On the other hand, if we have enough resilience, such as enough medical knowledge or perceived support from society and family, PRCP will no longer have an impact on GAD. In the face of public health events, it may be wise to give priority to dealing with emotions. Intervention against negative emotions or improve positive emotion can effectively block the path of PRCP affecting GAD. This study further complements our understanding of the conditional process of GAD under COVID-19 risk.
Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Ethics Statement
Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study.
Author Contributions
BW contributed to conception and design of the study and wrote sections of the manuscript. HF organized the database, performed the statistical analysis, and wrote the first draft of the manuscript. All authors contributed to the article and approved the submitted version.
Funding
This work was supported by the Research Foundation of Student Education Management and Reform Research Project funded by Southwest University of Science and Technology (19sxb118), the Research & Instruction Center of Mianyang Adolescents’ Psychological Growth (SCWCN2019YB14), the Longshan Talents Plan of Southwest University of Science and Technology (18LSX610), the Psycho-social Service and Crisis Intervention Research Center of Southwest University of Science and Technology (108102), the Institute of Psychology, CAS, (GJ202003).
Conflict of Interest
The authors declare that this review was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s Note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
Adhanom Ghebreyesus, T. (2020). Addressing mental health needs: an integral part of COVID-19 response. World Psychiatry 19, 129–130. doi: 10.1002/wps.20768
Aguinis, H., Gottfredson, R. K., and Joo, H. (2013). Best-practice recommendations for defining, identifying, and handling outliers. Organ. Res. Methods 16, 270–301. doi: 10.1177/1094428112470848
Arnett, J. J. (2000). Emerging adulthood: A theory of development from the late teens through the twenties. Am. Psychol. 55, 469–480. doi: 10.1037/0003-066X.55.5.469
Arnett, J. J. (2007). Emerging adulthood: what is it, and what is it good For? Child Dev. Perspect. 1, 68–73. doi: 10.1111/j.1750-8606.2007.00016.x
Ash, C. J. (2000). Interrelationships of Life Experiences and Perceptions of Control in the Reported Life Satisfaction of Adolescents. University of South Carolina.
Bratu, S. (2020). The fake news sociology of COVID-19 pandemic fear: dangerously inaccurate beliefs, emotional contagion, and conspiracy ideation. Linguist. Philos, Invest. 19, 128–134. doi: 10.22381/LPI19202010
Campbell, A. (1976). Subjective measures of well-being. Am. Psychol. 31, 117–124. doi: 10.1037/0003-066X.31.2.117
Cao, W., Fang, Z., Hou, G., Han, M., Xu, X., Dong, J., et al. (2020). The psychological impact of the COVID-19 epidemic on college students in China. Psychiatry Res. 287:112934. doi: 10.1016/j.psychres.2020.112934
Connor, K. M., and Davidson, J. R. T. (2003). Development of a new resilience scale: The Connor-Davidson resilience scale (CD-RISC). Depress. Anxiety 18, 76–82. doi: 10.1002/da.10113
Dobson-Lohman, E., and Potcovaru, A.-M. (2020). Fake news content shaping the COVID-19 pandemic fear: virus anxiety, emotional contagion, and responsible media reporting. Analy. Metaphys. 19, 94–100. doi: 10.22381/AM19202011
Dratva, J., Zysset, A., Schlatter, N., von Wyl, A., Huber, M., and Volken, T. (2020). Swiss university students’ risk perception and general anxiety during the COVID-19 pandemic. Int. J. Environ. Res. Public Health 17:7433. doi: 10.3390/ijerph17207433
Duncan, C. (2020). Gender-related depression, anxiety, and psychological stress experienced during the COVID-19 pandemic. J. Res. Gender Stud. 10, 84–94. doi: 10.22381/JRGS10220204
Gao, G. F. (2021). COVID-19: a year long and beyond? China CDC Weekly 3, 157–158. doi: 10.46234/ccdcw2021.052
Gariepy, G., Honkaniemi, H., and Quesnel-Vallee, A. (2016). Social support and protection from depression: systematic review of current findings in Western countries. Br. J. Psychiatry 209, 284–293. doi: 10.1192/bjp.bp.115.169094
Hagger, M. S., and Orbell, S. (2003). A meta-analytic review of the common-sense model of illness representations. Psychol. Health 18, 141–184. doi: 10.1080/088704403100081321
Hayes, A. F. (2013). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach. New York, NY, United States: Guilford Press.
Huang, S., Han, M., Sun, L., Zhang, H., and Li, H.-J. (2019). Family socioeconomic status and emotional adaptation among rural-to-urban migrant adolescents in China: The moderating roles of adolescent's resilience and parental positive emotion. Int. J. Psychol. 54, 573–581. doi: 10.1002/ijop.12499
Hyland, P., Shevlin, M., McBride, O., Murphy, J., Karatzias, T., Bentall, R. P., et al. (2020). Anxiety and depression in the Republic of Ireland during the COVID-19 pandemic. Acta Psychiatr. Scand. 142, 249–256. doi: 10.1111/acps.13219
Jaspal, R., and Breakwell, G. M. (2021). Social support, perceived risk and the likelihood of COVID-19 testing and vaccination: cross-sectional data from the United Kingdom. Curr. Psychol. 41, 492–504. doi: 10.1007/s12144-021-01681-z
Kujawa, A., Green, H., Compas, B., Dickey, L., and Pegg, S. (2020). Exposure to COVID-19 pandemic stress: associations with depression and anxiety in emerging adults in the United States. Depress. Anxiety 37, 1280–1288. doi: 10.1002/da.23109
Kumpfer, K. L., and Bluth, B. (2004). Parent/child transactional processes predictive of resilience or vulnerability to “substance abuse disorders”. Subst. Use Misuse 39, 671–698. doi: 10.1081/JA-120034011
Lăzăroiu, G., and Adams, C. (2020). Viral panic and contagious fear in scary times: The proliferation of COVID-19 misinformation and fake news. Analy. Metaphys. 19, 80–86. doi: 10.22381/AM1920209
Lăzăroiu, G., Horak, J., and Valaskova, K. (2020). Scaring ourselves to death in the time of COVID-19: pandemic awareness, virus anxiety, and contagious fear. Linguist. Philos, Invest. 19, 114–120. doi: 10.22381/LPI1920208
Lemyre, L., and Lalande-Markon, M. P. (2009). Psychological stress measure (PSM-9): integration of an evidence-based approach to assessment, monitoring, and evaluation of stress in physical therapy practice. Physiother. Theory Pract. 25, 453–462. doi: 10.1080/09593980902886321
Li, F., Luo, S., Mu, W., Li, Y., Ye, L., Zheng, X., et al. (2021). Effects of sources of social support and resilience on the mental health of different age groups during the COVID-19 pandemic. BMC Psychiatry 21, 16–14. doi: 10.1186/s12888-020-03012-1
Malhotra, N. K., Kim, S. S., and Patil, A. (2006). Common method variance in IS research: A comparison of alternative approaches and a reanalysis of past research. Manag. Sci. 52, 1865–1883. doi: 10.1287/mnsc.1060.0597
Marganska, A., Gallagher, M., and Miranda, R. (2013). Adult attachment, emotion dysregulation, and symptoms of depression and generalized anxiety disorder. Am. J. Orthop. 83, 131–141. doi: 10.1111/ajop.12001
Maxwell, S. E., Cole, D. A., and Mitchell, M. A. (2011). Bias in cross-sectional analyses of longitudinal mediation: partial and complete mediation Under an autoregressive model. Multivar. Behav. Res. 46, 816–841. doi: 10.1080/00273171.2011.606716
Mennin, D. S., Holaway, R. M., Fresco, D. M., Moore, M. T., and Heimberg, R. G. (2007). Delineating components of emotion and its Dysregulation in anxiety and mood psychopathology. Behav. Ther. 38, 284–302. doi: 10.1016/j.beth.2006.09.001
Mohammadkhani, S., Attar, F., and Akbari, M. (2021). The linkage between negative affectivity with emotional distress in college student: The mediator and moderator role of difficulty in emotion regulation, repetitive negative thinking, and emotional invalidation. Curr. Psychol. doi: 10.1007/s12144-021-01904-3
Montpetit, M. A., Bergeman, C. S., Deboeck, P. R., Tiberio, S. S., and Boker, S. M. (2010). Resilience-as-process: negative affect, stress, and coupled dynamical systems. Psychol. Aging 25, 631–640. doi: 10.1037/a0019268
Musche, V., Kohler, H., Bäuerle, A., Schweda, A., Weismüller, B., Fink, M., et al. (2021). COVID-19-related fear, risk perception, and safety behavior in individuals with diabetes. Health 9:480. doi: 10.3390/healthcare9040480
Nemțeanu, M. S., Dinu, V., and Dabija, D. C. (2021). Job insecurity, job instability and job satisfaction in the context of COVID 19 pandemic. J. Competit. 13, 65–82. doi: 10.7441/joc.2021.02.04
Ning, L., Niu, J., Bi, X., Yang, C., Liu, Z., Wu, Q., et al. (2020). The impacts of knowledge, risk perception, emotion and information on citizens’ protective behaviors during the outbreak of COVID-19: a cross-sectional study in China. BMC Public Health 20:1751. doi: 10.1186/s12889-020-09892-y
Oyetunji, T. P., Ogunmola, O. A., Oyelakin, T. T., Olorunsogbon, O. F., and Ajayi, F. O. (2021). COVID-19-related risk perception, anxiety and protective behaviours among Nigerian adults: a cross-sectional study. J. Public Health 1–9. doi: 10.1007/s10389-021-01502-4
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., and Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. J. Appl. Psychol. 88, 879–903. doi: 10.1037/0021-9010.88.5.879
Popescu Ljungholm, D., and Olah, M. L. (2020). Regulating fake news content during COVID-19 pandemic: evidence-based reality, trustworthy sources, and responsible media reporting. Rev. Contemp. Philos. 19, 43–49. doi: 10.22381/RCP1920203
Preacher, K. J., and Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav. Res. Methods 40, 879–891. doi: 10.3758/BRM.40.3.879
Ruscio, A. M., Hallion, L. S., Lim, C. C., Aguilar-Gaxiola, S., Al-Hamzawi, A., Alonso, J., et al. (2017). Cross-sectional comparison of the epidemiology of DSM-5 generalized anxiety disorder across the globe. JAMA Psychiat. 74, 465–475. doi: 10.1001/jamapsychiatry.2017.0056
Spitzer, R. L., Kroenke, K., Williams, J. B., and Löwe, B. (2006). A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch. Intern. Med. 166, 1092–1097. doi: 10.1001/archinte.166.10.1092
Tong, X., An, D., McGonigal, A., Park, S. P., and Zhou, D. (2016). Validation of the generalized anxiety Disorder-7 (GAD-7) among Chinese people with epilepsy. Epilepsy Res. 120, 31–36. doi: 10.1016/j.eplepsyres.2015.11.019
Watson, D., and Naragon-Gainey, K. (2014). Personality, Emotions, and the Emotional Disorders. Clin. Psychol. Sci. 2, 422–442. doi: 10.1177/2167702614536162
Wilks, S. E., and Croom, B. (2008). Perceived stress and resilience in Alzheimer's disease caregivers: testing moderation and mediation models of social support. Aging Ment. Health 12, 357–365. doi: 10.1080/13607860801933323
Wirkner, J., Christiansen, H., Knaevelsrud, C., Lüken, U., Wurm, S., Schneider, S., et al. (2021). Mental health in times of the COVID-19 pandemic. Eur. Psychol. 26, 310–322. doi: 10.1027/1016-9040/a000465
Xi, J., She, Z., Ju, K., Zhao, J., Hou, X., Peng, Y., et al. (2020). The development and psychometric properties of the COVID-19 risk perception scale. J. Capital Norm. Univ. 4, 131–141.
Yin, Q., Chen, A., Song, X., Deng, G., and Dong, W. (2021). Risk perception and PTSD symptoms of medical staff combating against COVID-19: a PLS structural equation model. Front. Psychol. 12:607612. doi: 10.3389/fpsyt.2021.607612
Keywords: COVID-19, emerging adults, risk perception, anxiety, resilience
Citation: Fu H and Wang B (2022) The Effect of Risk Perception on Anxiety in Emerging Adulthood Under the Local Outbreak of COVID-19: A Conditional Process Analysis. Front. Psychol. 13:759510. doi: 10.3389/fpsyg.2022.759510
Edited by:
Barbara Hildegard Juen, University of Innsbruck, AustriaReviewed by:
Aurel Pera, University of Craiova, RomaniaSilvia Exenberger, Tyrol Clinics GmbH, Austria
Copyright © 2022 Fu and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Bin Wang, d2FuZ2JpbkBzd3VzdC5lZHUuY24=