Potential categories of employment stress among rural college students and their relationship to employment psychology

Background: Psychological problems related to employment are among the most common psychological problems faced by rural college students. Employment stress is an important factor affecting the development of psychological health in employees; thus, reducing employment stress can improve the psychological state of employment.

Objective: This study aimed to understand the potential profiles of employment stress among rural college students to determine the relationship between different profiles and employment psychology.

Methods: This study was conducted in a higher education institution in Qingdao, Shandong Province, China between June and December 2023, and 249 rural college students participated. The Employment Stress Scale and Employment Psychology Scale were used to collect the data. Data were analyzed using latent profile analysis, independent sample t-tests, and binary logistic regression analysis.

Results: The results showed that rural university students were categorized into low-level (49.80%) and high-level (50.21%) employment stress groups. There was a statistically significant difference between the employment psychology of rural college students in the low- and high-level groups (p < 0.001). Juniors/seniors were more likely to be classified in the high-level group (OR = 0.477, p = 0.011).

Conclusion: Intervention programs should be developed and implemented to address the characteristics of employment stress among rural college students with different profiles to promote the healthy development of their attitudes toward employment.

1 Introduction

Statistics from China’s Ministry of Education of the People’s Republic of China (n.d.) showed that the number of college students in China was 30.32 million in 2019, and as of 2022 had risen as high as 36.56 million, with rural college students accounting for about 47% of the total. The above data is evidence of the high growth rate in the number of college students in China in recent years, and the number of employed people is also increasing (Senekal and Smith, 2022; Yin, 2022). However, there is no large-scale postgraduate expansion and recruitment program in China, which leads to a grim employment situation for college students (Pei, 2021; Li, 2022; Li et al., 2022; Xiong et al., 2022; Zhi, 2023). This phenomenon causes college students to adapt to their current form of employment; however, the process of adaptation produces complex and fluctuating psychological changes related to employment. All of these factors are usually referred to as “employment psychology” (Achdut and Refaeli, 2020; Wei, 2023).

Employment psychology plays an important role in the lives, studies, employment, and career choices of college students (Hernández-Sánchez et al., 2020). Previous studies have shown that good employment psychology can help mobilize college students’ strong learning motivation and improve their comprehensive ability to adapt to the employment environment in China (Luciano et al., 2021; Senekal and Smith, 2022). In contrast, poor employment psychology affects college students’ thinking patterns and memory and reduces their employment competitiveness (Jackson and Tomlinson, 2020; Wan Mohd Yunus et al., 2021; Peng et al., 2023). In summary, good employment psychology is of great significance in promoting the successful employment of rural college students.

However, the results of the current study show that the employment psychological level of college students is generally not high (Liu et al., 2019; Saddik et al., 2020; Song et al., 2021), and rural college students are more prone to negative psychological factors, such as low self-esteem and anxiety due to the heavy economic burden carried by their families, leading to low self-evaluation and a narrowing of the scope of employment (Chen et al., 2020; Pillay et al., 2020). In addition, most parents of rural college students have a low level of education, lack appropriate parenting styles, and provide insufficient emotional support, which leads to difficulties in regulating the emotions of rural college students and a poor employment mentality, diminishing the effects of employment. Therefore, it is important to explore the potential factors influencing rural college students’ employment psychology to enhance their employability and help them succeed.

According to social resources theory, scarcity of social resources leads to great stress and affects the mental health of individuals (Brunner et al., 2019; Goldmann, 2019). Employment stress refers to a series of physiological, psychological, and behavioral response processes that arise from internal and external stressors during an individual’s job search and selection (Ganster and Rosen, 2013; Wong et al., 2021). For rural college students in the process of seeking employment, there are problems such as the poor job market, poor employability, and insufficient social support (Mishra, 2020). These many sources of employment-related pressure result in enormous pressure, representing a major component in their employment psychology makeup (Arias-de la Torre et al., 2019).

Lee (2020) research showed a negative correlation between employment stress and employment psychology in a group of rural college students. Compared with rural college students with a high level of employment stress, rural college students with a low level of employment stress have a more objective personal evaluation, a better outlook on employment, and a healthier employment psychology. These results suggest that alleviating employment pressure on rural college students may be an important focus for improving their employment psychology. However, most studies have assessed the employment stress levels of rural college students only by evaluating their total employment stress scores, ignoring the group heterogeneity of rural college students’ employment stress, and therefore these results cannot provide precise suggestions for the development and implementation of relevant interventions.

Previous studies have shown that employment stress includes many aspects such as personal factors, social environment, family and friends, school, and professional issues, and that rural college students have different characteristics of employment stress when they face different employment dilemmas (Szromek and Wolniak, 2020; Kossek et al., 2021; Ojo et al., 2021; Rawal, 2023). According to the characteristics of the employment pressure on rural college students, formulating individualized employment guidance and support will help adjust their employment psychology and employment outlook, and effectively help rural college students achieve successful employment (Li et al., 2022). Previous studies showed that rural college students with the same employment psychology presented different employment stress characteristics (Belle et al., 2022; von Keyserlingk et al., 2022). Therefore, correctly assessing the employment stress characteristics of rural college students can guide researchers in developing individualized intervention strategies to promote their employment-related mental health.

Potential profile analysis classifies individuals into potential profiles based on the intersection of multiple features of the observed variables (Marsh et al., 2009; Lanza and Rhoades, 2013; Yang Q. et al., 2022). Thus, latent profile analysis can categorize rural college students with the same employment stress traits and characteristics into similar categories, ensuring intergroup heterogeneity among rural college students. Previous studies have analyzed stress, positive thoughts, anxiety, and needs using latent profile analysis with college students as research subjects. However, potential profiling of categories of employment stress among rural college students has not yet been conducted. Therefore, this study aims to identify the potential profiles of employment stress among rural college students, explore the factors affecting the classification of employment stress subgroups in terms of demographic characteristics, compare the employment psychology of rural college students in different employment stress subgroups, and provide targeted suggestions to induce an appropriate employment psychology.

2 Methods

2.1 Participants

A total of 254 rural college students enrolled in a higher education institution in Qingdao, Shandong Province, China, ranging from freshmen to seniors, were selected from August to November 2023 using convenience sampling. The inclusion criteria were: no mental disorders, informed consent, voluntary participation, and good physical health. The exclusion criteria were being under 18 years of age and not likely to seek employment. Of the 254 rural college students initially identified for research, 5 were excluded due to 20% or more of the questionnaire remaining unanswered and obvious patterns, giving a final total of 249 research subjects (67.9% female), an effective questionnaire recovery rate of 0.98.

2.2 Research process

The process was approved by the Bioethics Review Committee of Sacred Heart Campus of The Catholic University of Korea (CUK-IRB) (Institutional Review Board) (Number: 1040395–202,312-031). By reading the records of one-on-one heart-to-heart conversations among college students and checking the information in the students’ files, the research subjects eligible for this study were included. The help of the school leaders was sought to explain the purpose and significance of this study, to inform the students of the voluntary nature of the survey, and assure them the relevant information would be kept strictly confidential. Verbal consent was then received from the student participants. Relevant questionnaires were then collected by counselors and classroom teachers, and each questionnaire took approximately 25–30 min to complete.

3 Instruments

3.1 The demographic questionnaire

The General Demographic Information form was used to collect demographic characteristics of rural college students, and gathered basic information such as gender, age, grade level, and whether or not they were student leaders.

3.2 Employment stressors questionnaire

The Employment Stressor Scale utilizes the Employment Stressor Moderation Questionnaire developed by Dai (2009). This employment stressor scale has been widely used in China and has been shown to have good reliability. The Employment Stressors Scale consists of five parts: personal factors (five items), social environment factors (four), family and friends factors (four), school factors (three), and professional factors (five), for a total of 21 items. Each item was rated on a 5-point Likert scale ranging from “1 (not at all compliant)” to “5 (fully compliant).” The total score ranged from 21 to 105, with higher scores indicating higher levels of employment stress. The Cronbach’s alpha coefficient for this scale in this study was 0.812.

3.3 Employment psychology questionnaire

The Employment Psychology Scale was adopted from Liu (2010) self-edited Employment Psychology Questionnaire for College Students. The Employment Psychology Scale has been validated for research use with Chinese groups and consists of four parts: Preparation for Employment (12 items), Employment Perceptions (10), Employability (8), and Employment Perceptions (7), giving 37 items in total. Each item was rated on a 5-point Likert scale ranging from “1 (not at all compliant)” to “5 (fully compliant).” The total score ranged from 37 to 185, with higher scores representing a greater psychological likelihood of employment. Cronbach’s coefficient for this scale in this study was 0.763.

3.4 Data analyses

Potential profiles were analyzed using Mplus 7.0. From the initial one-profile model to the final four-profile model, all the parameters were estimated by gradually increasing the number of profiles until the best-fit index emerged. The Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were used to identify the optimal model, followed by Lo–Mendell–Rubin (LMR) p-values and Bootstrap Likelihood Ratio Test (BLRT) p-values to determine whether the former profile was the best-fitting model. The specific criteria were as follows: smaller AIC and BIC values implied a better model fit. The values of LMRT (p) and BLRT (p) were less than 0.05, implying that the previous profile was the best-fitting model. In addition, entropy values greater than 0.8 meant that the potential profiles were well separated.

Other data analyses were performed using the IBM SPSS software (version 25.0). The results of the histogram and Q-Q plot analyses showed that the data in this study conformed to an approximately normal distribution. After determining the optimal model, mean ± standard deviation was used to describe the employment stress scores. Independent sample t-tests were conducted to compare the psychological differences in employment experienced by participants across potential profiles. The relationship between the factors affecting the potential employment pressure profile and demographic characteristics was explored using chi-square tests and a multiple regression analysis.

4 Results

4.1 Latent profiles of employment stress

This study analyzed models with different numbers of profiles and compared four models. The AIC and BIC of the two-profile model are significant as well as entropy values greater than 0.8 for the two-profile model, although they are not minimized, for the LMRT(p) and BLRT(p) values. The LMRT(p) values were not significant when comparing the two- and three-profile models, indicating that the three-profile model was not superior to the two-profile model. Table 1 presents the model fit statistics for the potential profile estimates.

Table 1

Table 1. Results of applying the potential profile model of employment pressure on rural college students (N = 249).

Each profile was named based on the score for each dimension of employment stress. The results of this study show a significant difference in employment stress dimension scores between the two subgroups of rural college students. Consequently, the two employment stress profile model categories were designated as high- and low-level groups. This is shown in Table 2 and Figure 1.

Table 2

Table 2. Difference-in-differences analysis of scores on dimensions of two potential profiles of employment stress among rural college students (N = 249).

Figure 1

Figure 1. Line graph of mean scores for each dimension of the two potential profiles of employment stress among rural college students.

Profile 1 (high overall group, 49.4%) had a high probability of occurrence for almost all the items. Compared to Profile 2, Profile 1 scored higher on personal, social environment, family and friends, school, and professional issues. This indicated that this group of rural college students experienced high levels of employment stress. Profile 2 (low overall group: 50.6%) had a low probability of appearing at a low level for almost all items. This indicates that this group of rural college students experienced a low level of employment pressure.

4.2 Differences in employment psychology

As shown in Table 3, there were statistically significant differences between the two groups in terms of employability (t = 3.969, p < 0.001), employment cognition (t = 2.272, p = 0.024), and employment psychology (t = 2.101, p = 0.037), and the differences were statistically significant.

Table 3

Table 3. Differences in the level of employment psychology among rural college students in different employment stress groups (N = 249).

4.3 Differences in characteristics

As shown in Tables 4, 5, there was a significant difference between the two groups of rural college students at the grade level (χ2 = 4.997, p = 0.025), with juniors and seniors more likely to be in the high-level group (OR = 2.134, CI = 1.157–3.93, p = 0.015).

Table 4

Table 4. Differences in demographic distribution of employment stress types among rural college students in different employment stress groups (N = 249).

Table 5

Table 5. Regression analysis of employment stress characteristics of rural college students across potential profiles (N = 249).

5 Discussion

This study not only explored the employment stress characteristics of rural college students but also investigated potential employment stress categories and the relationship between those stress categories and employment psychology. Ultimately, the results of this study indicate that rural college students can be categorized into two employment stress subgroups and that these subgroups can provide directions for intervention and points of focus for future research.

The results of the potential profile analysis highlight significant individual differences in the employment pressure of rural college students in this study, which can be divided into a low-level group (Category 1) and a high-level group (Category 2), accounting for 49.40 and 50.60% of the total number, respectively, indicating serious polarization in the employment pressure of rural college students.

The reasons for this may be related to different individual perceptions of employment opportunities. For example, Yang S. et al. (2022) showed that the more sensitive the perception of employment opportunities, the greater the individual’s employment stress. Therefore, colleges, universities, and recruiters should communicate and exchange more information to convey the real employment situation to rural college students and help them establish correct perceptions of employment opportunities.

The results showed that those in the high level group had higher scores for personal factors, social environment, family and friends, school, and professional issues, suggesting that employment stress among rural college students is the result of a combination of factors. This result is consistent with the findings of Li et al. (2022), whose study showed that personal and professional factors are related to students’ personal evaluations and employment expectations, and that low personal evaluations and high employment expectations often lead to a loss of individual self-confidence in employment and a sudden increase in employment pressure. In addition, according to the stimulus-physiological/psychological-response (SOR) theoretical model, external factors can produce a direct psychological response in the body: when the employment social environment the is highly competitive, there are too few jobs, and the school’s previous students have poor employment records, it will increase the individual’s employment panic (Ardern et al., 2012; Anselme and Güntürkün, 2019; Mladenović et al., 2023).

Salami et al. (2021) showed that family support was strongly related to individuals’ employment stress. This study suggested that social support from family and friends increases college students’ sense of security and reduces negative emotions. In addition, the contrast between the high expectations of the parents of rural college students and the currently highly competitive employment environment may also increase employment pressure on these students.

Another factor is that the economic conditions in which the families of rural college students exist tend to be, at best, average, and raising a college student exhausts most of the family’s resources; parents therefore expect their children to find a good job and help their families, which leads to great pressure on rural college students to find employment.

Based on these factors, schools should encourage students to establish a correct view of career choices, change the traditional concept of employment, give full play to their own strengths, and choose suitable employment positions. Teachers should organize class meetings with outstanding graduates to enhance rural college students’ confidence in employment. Simultaneously, an integrated school-family training model should be established to mobilize the emotional support of family and friends for these students.

The results also show that gender and being a student leader have no effect on employment pressure, mainly because the trend of rural college students choosing to go to higher education has gradually become more prominent, and the number of students going to graduate school has been increasing annually, while the number of urban students has been decreasing. This phenomenon shows that, although an urban–rural gap still exists, the differences in job characteristics in the primary and secondary labor markets are widening, and rural students dominate the examination group. Therefore, the influence of student cadres and gender factors on rural university students’ employment pressure is relatively small, and the main factors still lie in the rural economy and the structural problems of the labor market.

The results also highlighted the difference between the low- and high-level groups on the total scores of Employability, Employment Perceptions, and Employment Psychology (Lee, 2020; Yang S. et al., 2022), which is consistent with the studies of Lee. On the one hand, this may be due to the fact that rural college students under significant pressure to find employment will be immersed in negative emotions and adopt negative coping methods, such as implicit learning and avoidance, to deal with this pressure. This ultimately leads to reduced learning input behavior, decreased employability, and increased negative emotions.

The fact that most rural college students are first-generation college students in their families, with relatively fewer social resources available and less employment information and opportunities can cause them to be picky about employment positions and blindly follow current trends and exhibit other behaviors during the employment process, which affects employment cognition and leads to poor employment psychology. In summary, schools should pay attention to providing psychological guidance for rural college students, open public psychological counseling rooms, and effectively monitor the psychological condition of rural college students. They should establish an effective employment training system, carry out timely employment guidance, help rural college students set up correct employment goals, and provide effective employment assistance.

A key aspect of the results was the identification of grade level as an important indicator for identifying high employment stress among college students. This study found that rural junior/senior college students were more likely to fall into the high-level group, which is consistent with the findings of previous research. This may be due to the fact that juniors/seniors are at the fork of employment and further education and are directly faced with the problem of solving the employment problem, making employment pressures relatively greater. There is also the fact that after apprenticeships and internships, junior and senior students have a clearer understanding of the vocational skills required for employment positions, while their own professional knowledge is not yet solidly grasped, leading fear of being rejected for employment by employers.

Previous studies have found gender differences in employment stress and indicated that women experience higher levels of employment stress than men because they are more sensitive and perceive stress more easily, while men are more resilient to setbacks and have greater mental toughness in withstanding the psychological stress caused by factors related to employment (Juster et al., 2019; Christiansen and Berke, 2020; Debowska et al., 2022). In addition, Wang’s research showed that the employment pressure on college students who are only children is often lower than that on students from larger families, due to the fact that one-child families can utilize all the family resources of the family and devote more parental attention and care to the child. This gives such children more sources of social support and helps to alleviate the employment pressure (Chang et al., 2020; Islam et al., 2020; Owens et al., 2020). On the other hand, children with siblings who have to solve employment problems on their own and often bear the responsibility of taking care of those siblings are more eager and under pressure to obtain employment, which increases their stress. However, while data was gathered on gender and one-child v. multi-child families, neither issue was included in the regression equation model in this study. In relation to gender. For the issue of whether participants were from one-child or multi-child families, the issue was omitted because of the relatively proportion number of women, who almost always have siblings, and the relatively small sample size of men, who form the majority of only children (Chow et al., 2019; Goodman, 2020).

This study considered the heterogeneity of rural college students; the potential categories of employment stress among rural college students were explored using potential profile analysis, and their relationships with psychological and sociodemographic employment profiles were analyzed, which provided a theoretical basis for the development of targeted psychological interventions. However, this study has several limitations. First, it examined only rural college students at a single higher education institution in Shandong Province. This implies a possible selection bias because rural development is uneven across regions in China; employment pressure may therefore vary. Future research should investigate the current status of employment stress among rural college students in multiple regions and colleges to increase sample diversity. Second, this study did not fully consider the effect of covariates when conducting LPA; therefore, subsequent studies should incorporate additional covariates based on relevant theories to ensure grouping accuracy.

6 Conclusion

This study analyzed the characteristics of employment stress among rural college students through potential profile analysis and identified a low-level group (Category 1) and a high-level group (Category 2). This study found differences in employment psychology between the two groups. Grade level as a risk factor for employment stress among rural college students. In conclusion, the different categorization characteristics of rural college students’ employment stress can help schools identify potential high-risk groups at an early stage and provide timely and targeted interventions. Such interventions can have beneficial effects and effectively alleviate the employment stress of rural college students.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving humans were approved by Bioethics Review Committee of Sacred Heart Campus of The Catholic University of Korea (CUK-IRB) (Institutional Review Board) (Number: 1040395-202312-031). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

XW: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Writing – original draft, Writing – review & editing. KK: Formal analysis, Project administration, Supervision, Writing – review & editing. ZJ: Data curation, Software, Writing – review & editing.

Funding

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Acknowledgments

The authors would like to thank the leaders, teachers, and students of Qingdao University of Technology for their help and support.

Conflict of interest

The authors declare that the research 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

Achdut, N., and Refaeli, T. (2020). Unemployment and psychological distress among young people during the COVID-19 pandemic: psychological resources and risk factors. Int. J. Environ. Res. Public Health 17:7163. doi: 10.3390/ijerph17197163

PubMed Abstract | Crossref Full Text | Google Scholar

Anselme, P., and Güntürkün, O. (2019). Incentive hope: a default psychological response to multiple forms of uncertainty. Behav. Brain Sci. 42:e58. doi: 10.1017/S0140525X18002194

PubMed Abstract | Crossref Full Text | Google Scholar

Ardern, C. L., Taylor, N. F., Feller, J. A., and Webster, K. E. (2012). A systematic review of the psychological factors associated with returning to sport following injury. Br. J. Sports Med. 47, 1120–1126. doi: 10.1136/bjsports-2012-091203

Crossref Full Text | Google Scholar

Arias-de la Torre, J., Fernández-Villa, T., Molina, A. J., Amezcua-Prieto, C., Mateos, R., Cancela, J. M., et al. (2019). Psychological distress, family support and employment status in first-year university students in Spain. Int. J. Environ. Res. Public Health 16:1209. doi: 10.3390/ijerph16071209

PubMed Abstract | Crossref Full Text | Google Scholar

Belle, M. A., Antwi, C. O., Ntim, S. Y., Affum-Osei, E., and Ren, J. (2022). Am I gonna get a job? Graduating students’ psychological capital, coping styles, and employment anxiety. J. Career Dev. 49, 1122–1136. doi: 10.1177/08948453211020124

Crossref Full Text | Google Scholar

Brunner, B., Igic, I., Keller, A. C., and Wieser, S. (2019). Who gains the most from improving working conditions? Health-related absenteeism and presenteeism due to stress at work. Eur. J. Health Econ. 20, 1165–1180. doi: 10.1007/s10198-019-01084-9

PubMed Abstract | Crossref Full Text | Google Scholar

Chang, J., Wang, S.-W., Mancini, C., McGrath-Mahrer, B., and Orama de Jesus, S. (2020). The complexity of cultural mismatch in higher education: norms affecting first-generation college students’ coping and help-seeking behaviors. Cult. Divers. Ethn. Minor. Psychol. 26, 280–294. doi: 10.1037/cdp0000311

PubMed Abstract | Crossref Full Text | Google Scholar

Chen, S., Mei, R., Tan, C., Li, X., Zhong, C., and Ye, M. (2020). Psychological resilience and related influencing factors in postoperative non-small cell lung cancer patients: a cross-sectional study. Psychooncology 29, 1815–1822. doi: 10.1002/pon.5485

PubMed Abstract | Crossref Full Text | Google Scholar

Chow, A., Dharma, C., Chen, E., Mandhane, P. J., Turvey, S. E., Elliott, S. J., et al. (2019). Trajectories of depressive symptoms and perceived stress from pregnancy to the postnatal period among Canadian women: impact of employment and immigration. Am. J. Public Health 109, S197–S204. doi: 10.2105/AJPH.2018.304624

PubMed Abstract | Crossref Full Text | Google Scholar

Christiansen, D. M., and Berke, E. T. (2020). Gender-and sex-based contributors to sex differences in PTSD. Curr. Psychiatry Rep. 22, 1–9. doi: 10.1007/s11920-020-1140-y

Crossref Full Text | Google Scholar

Dai, K. (2009). A study on the relationship between employment stressors and coping styles and personality types among college students. Master's Thesis, Jilin: Northeast Normal University.

Google Scholar

Debowska, A., Horeczy, B., Boduszek, D., and Dolinski, D. (2022). A repeated cross-sectional survey assessing university students' stress, depression, anxiety, and suicidality in the early stages of the COVID-19 pandemic in Poland. Psychol. Med. 52, 3744–3747. doi: 10.1017/S003329172000392X

PubMed Abstract | Crossref Full Text | Google Scholar

Ganster, D. C., and Rosen, C. C. (2013). Work stress and employee health: a multidisciplinary review. J. Manag. 39, 1085–1122. doi: 10.1177/0149206313475815

Crossref Full Text | Google Scholar

Goldmann, G. (2019). Handbook of social resource theory: theoretical extensions, empirical insights, and social applications. J. Soc. Sci. 56, 139–140. doi: 10.1016/j.soscij.2019.01.012

Crossref Full Text | Google Scholar

Goodman, S. H. (2020). Intergenerational transmission of depression. Annu. Rev. Clin. Psychol. 16, 213–238. doi: 10.1146/annurev-clinpsy-071519-113915

Crossref Full Text | Google Scholar

Hernández-Sánchez, B. R., Cardella, G. M., and Sánchez-García, J. C. (2020). Psychological factors that lessen the impact of covid-19 on the self-employment intention of business administration and economics’ students from latin america. Int. J. Environ. Res. Public Health 17:5293. doi: 10.3390/ijerph17155293

PubMed Abstract | Crossref Full Text | Google Scholar

Islam, M. S., Sujan, M. S. H., Tasnim, R., Sikder, M. T., Potenza, M. N., and Van Os, J. (2020). Psychological responses during the COVID-19 outbreak among university students in Bangladesh. PLoS One 15:e0245083. doi: 10.1371/journal.pone.0245083

PubMed Abstract | Crossref Full Text | Google Scholar

Jackson, D., and Tomlinson, M. (2020). Investigating the relationship between career planning, proactivity and employability perceptions among higher education students in uncertain labour market conditions. High. Educ. 80, 435–455. doi: 10.1007/s10734-019-00490-5

Crossref Full Text | Google Scholar

Juster, R.-P., de Torre, M. B., Kerr, P., Kheloui, S., Rossi, M., and Bourdon, O. (2019). Sex differences and gender diversity in stress responses and allostatic load among workers and LGBT people. Curr. Psychiatry Rep. 21, 1–11. doi: 10.1007/s11920-019-1104-2

Crossref Full Text | Google Scholar

Kossek, E. E., Perrigino, M., and Rock, A. G. (2021). From ideal workers to ideal work for all: a 50-year review integrating careers and work-family research with a future research agenda. J. Vocat. Behav. 126:103504. doi: 10.1016/j.jvb.2020.103504

Crossref Full Text | Google Scholar

Lanza, S. T., and Rhoades, B. L. (2013). Latent class analysis: an alternative perspective on subgroup analysis in prevention and treatment. Prev. Sci. 14, 157–168. doi: 10.1007/s11121-011-0201-1

PubMed Abstract | Crossref Full Text | Google Scholar

Lee, K. (2020). Social support and self-esteem on the association between stressful life events and mental health outcomes among college students. Soc. Work Health Care 59, 387–407. doi: 10.1080/00981389.2020.1772443

PubMed Abstract | Crossref Full Text | Google Scholar

Li, Y. (2022). Analysis of employment destinations of Shanghai college graduates based on the AHP model. Adv. Appl. Math. 11, 3745–3752. doi: 10.12677/AAM.2022.116402

Crossref Full Text | Google Scholar

Li, X., Pu, R., and Liao, H. (2022). The impacts of innovation capability and social adaptability on undergraduates’ employability: the role of self-efficacy. Front. Psychol. 13:954828. doi: 10.3389/fpsyg.2022.954828

PubMed Abstract | Crossref Full Text | Google Scholar

Li, T., Sun, X., and Wu, Z. (2022). How does the employment situation of fresh graduates of Chinese universities change in the context of the 2021 epidemic? -an empirical study based on national survey data in 2021 and 2020. J. East China Normal Univ. 40:100–113. doi: 10.16382/j.cnki.1000-5560.2022.02.008

Crossref Full Text | Google Scholar

Liu, C. (2010). Research on the employment psychological problems of contemporary college students and their influencing factors Jilin University, 13. (PhD dissertation) Available at: https://kns.cnki.net/KCMS/detail/detail.aspx?dbname=CDFD0911&filename=2010107622.nh

Google Scholar

Liu, X., Ping, S., and Gao, W. (2019). Changes in undergraduate students’ psychological well-being as they experience university life. Int. J. Environ. Res. Public Health 16:2864. doi: 10.3390/ijerph16162864

PubMed Abstract | Crossref Full Text | Google Scholar

Luciano, M., Sampogna, G., Del Vecchio, V., Giallonardo, V., Palummo, C., Andriola, I., et al. (2021). The impact of clinical and social factors on the physical health of people with severe mental illness: results from an Italian multicentre study. Psychiatry Res. 303:114073. doi: 10.1016/j.psychres.2021.114073

Crossref Full Text | Google Scholar

Marsh, H. W., Lüdtke, O., Trautwein, U., and Morin, A. J. (2009). Classical latent profile analysis of academic self-concept dimensions: synergy of person-and variable-centered approaches to theoretical models of self-concept. Struct. Equ. Model. Multidiscip. J. 16, 191–225. doi: 10.1080/10705510902751010

Crossref Full Text | Google Scholar

Ministry of Education of the People’s Republic of China. Number of Students of Formal Education by Type and Level. Available at: http://www.moe.gov.cn/jyb_sjzl/moe_560/2021/quanguo/202301/t20230104_1038067.html.

Google Scholar

Mishra, S. (2020). Social networks, social capital, social support and academic success in higher education: a systematic review with a special focus on ‘underrepresented’students. Educ. Res. Rev. 29:100307. doi: 10.1016/j.edurev.2019.100307

Crossref Full Text | Google Scholar

Mladenović, D., Todua, N., and Pavlović-Höck, N. (2023). Understanding individual psychological and behavioral responses during COVID-19: application of stimulus-organism-response model. Telematics Inform. 79:101966. doi: 10.1016/j.tele.2023.101966

PubMed Abstract | Crossref Full Text | Google Scholar

Ojo, A. O., Fawehinmi, O., and Yusliza, M. Y. (2021). Examining the predictors of resilience and work engagement during the COVID-19 pandemic. Sustain. For. 13:2902. doi: 10.3390/su13052902

Crossref Full Text | Google Scholar

Owens, M. R., Brito-Silva, F., Kirkland, T., Moore, C. E., Davis, K. E., Patterson, M. A., et al. (2020). Prevalence and social determinants of food insecurity among college students during the COVID-19 pandemic. Nutrients 12:2515. doi: 10.3390/nu12092515

Crossref Full Text | Google Scholar

Pei, K. (2021). R& D human capital allocation and technological innovation: a heterogeneous educational level perspective. Sci. Technol. Prog. Res. 38, 11–20.

Google Scholar

Peng, Y., Lv, S. B., Low, S. R., and Bono, S. A. (2023). The impact of employment stress on college students: psychological well-being during COVID-19 pandemic in China. Curr. Psychol. 1–12. doi: 10.1007/s12144-023-04785-w

Crossref Full Text | Google Scholar

Pillay, A. L., Thwala, J. D., and Pillay, I. (2020). Depressive symptoms in first year students at a rural south African university. J. Affect. Disord. 265, 579–582. doi: 10.1016/j.jad.2019.11.094

Crossref Full Text | Google Scholar

Rawal, D. M. (2023). Work life balance among female school teachers [k-12] delivering online curriculum in Noida [India] during COVID: empirical study. Manag. Educ. 37, 37–45. doi: 10.1177/0892020621994303

Crossref Full Text | Google Scholar

Saddik, B., Hussein, A., Sharif-Askari, F. S., Kheder, W., Temsah, M.-H., Koutaich, R. A., et al. (2020). Increased levels of anxiety among medical and non-medical university students during the COVID-19 pandemic in the United Arab Emirates. Risk Manag. Healthc. Policy 13, 2395–2406. doi: 10.2147/RMHP.S273333

PubMed Abstract | Crossref Full Text | Google Scholar

Salami, T., Lawson, E., and Metzger, I. W. (2021). The impact of microaggressions on black college students’ worry about their future employment: the moderating role of social support and academic achievement. Cult. Divers. Ethn. Minor. Psychol. 27, 245–255. doi: 10.1037/cdp0000340

Crossref Full Text | Google Scholar

Senekal, J. S., and Smith, M. R. (2022). Assessing the employability and employment destinations of professional psychology alumni. S. Afr. J. Psychol. 52, 11–22. doi: 10.1177/00812463211025466

Crossref Full Text | Google Scholar

Song, B., Zhao, Y., and Zhu, J. (2021). COVID-19-related traumatic effects and psychological reactions among international students. J. Epidemiol. Global Health 11:117. doi: 10.2991/jegh.k.201016.001

Crossref Full Text | Google Scholar

Szromek, A. R., and Wolniak, R. (2020). Job satisfaction and problems among academic staff in higher education. Sustain. For. 12:4865. doi: 10.3390/su12124865

Crossref Full Text | Google Scholar

von Keyserlingk, L., Yamaguchi-Pedroza, K., Arum, R., and Eccles, J. S. (2022). Stress of university students before and after campus closure in response to COVID-19. J. Community Psychol. 50, 285–301. doi: 10.1002/jcop.22561

PubMed Abstract | Crossref Full Text | Google Scholar

Wan Mohd Yunus, W. M. A., Badri, S. K. Z., Panatik, S. A., and Mukhtar, F. (2021). The unprecedented movement control order (lockdown) and factors associated with the negative emotional symptoms, happiness, and work-life balance of Malaysian university students during the coronavirus disease (COVID-19) pandemic. Front. Psych. 11:566221. doi: 10.3389/fpsyt.2020.566221

PubMed Abstract | Crossref Full Text | Google Scholar

Wei, X. (2023). Macro-educational approach on employment psychological disorders among college students: an educational psychology perspective. CNS Spectr. 28:S101. doi: 10.1017/S1092852923005060

Crossref Full Text | Google Scholar

Wong, A. K. F., Kim, S. S., Kim, J., and Han, H. (2021). How the COVID-19 pandemic affected hotel employee stress: employee perceptions of occupational stressors and their consequences. Int. J. Hosp. Manag. 93:102798. doi: 10.1016/j.ijhm.2020.102798

Crossref Full Text | Google Scholar

Xiong, W., Yang, J., and Shen, W. (2022). Higher education reform in China: a comprehensive review of policymaking, implementation, and outcomes since 1978. China Econ. Rev. 72:101752. doi: 10.1016/j.chieco.2022.101752

Crossref Full Text | Google Scholar

Yang, S., Yang, J., Yue, L., Xu, J., Liu, X., Li, W., et al. (2022). Impact of perception reduction of employment opportunities on employment pressure of college students under COVID-19 epidemic–joint moderating effects of employment policy support and job-searching self-efficacy. Front. Psychol. 13:986070. doi: 10.3389/fpsyg.2022.986070

PubMed Abstract | Crossref Full Text | Google Scholar

Yang, Q., Zhao, A., Lee, C., Wang, X., Vorderstrasse, A., and Wolever, R. Q. (2022). Latent profile/class analysis identifying differentiated intervention effects. Nurs. Res. 71, 394–403. doi: 10.1097/NNR.0000000000000597

PubMed Abstract | Crossref Full Text | Google Scholar

Yin, L. (2022). From employment pressure to entrepreneurial motivation: an empirical analysis of college students in 14 universities in China. Front. Psychol. 13:924302. doi: 10.3389/fpsyg.2022.924302

PubMed Abstract | Crossref Full Text | Google Scholar

Zhi, L. (2023). Research on the influence of mental health on college students' employment based on fuzzy clustering techniques. Soft. Comput. 27, 19095–19111. doi: 10.1007/s00500-023-09312-4

Crossref Full Text | Google Scholar