Research article Special Issues

A cross-sectional population-based study on the influence of the COVID-19 pandemic on incomes in Greece

  • The Coronavirus Disease 2019 (COVID-19) pandemic induced economic shock in Greece, which translated into a decrease in household income. Thus, the objective of this study is to measure social inequality with regard to income loss due to the COVID-19 pandemic in Greece. In addition, we aim to identify the characteristics of those experiencing income loss due to the pandemic. The study uses data from the “Public Opinion in the European Union (EU) in Time of Coronavirus Crisis. Third Round” survey. The sample consists of 1036 individuals aged between 16 and 54 years. To measure inequality, the Erreygers' Concentration Index (CI) is calculated, using social class as the ranking variable. To identify the characteristics of those experiencing income loss, a logistic regression model is fitted using the region of residence and several demographic and socioeconomic variables as potential predictors. According to the results, social inequality does not exist with regard to income loss due to the COVID-19 pandemic. Thus, our findings indicate the negative influence of the pandemic on the incomes of individuals from all social classes in Greece. According to the results of the logistic regression model, the odds of experiencing income loss are higher for residents of the Aegean Islands and Crete but also for self-employed, part-time employed, and unemployed individuals. These findings indicate the negative influence of the pandemic on Greek tourism and on sectors employing a large proportion of non-standard workers. Although inequality does not exist, a substantial proportion of those losing income due to the pandemic is in line with the global picture.

    Citation: Dimitris Zavras. A cross-sectional population-based study on the influence of the COVID-19 pandemic on incomes in Greece[J]. AIMS Public Health, 2021, 8(3): 376-387. doi: 10.3934/publichealth.2021029

    Related Papers:

    [1] Ahmad A Mirza, Hammam Baarimah, Mukhtiar Baig, Abdulrahim A Mirza, Mohammed A Halawani, Ghada M Beyari, Khalid S AlRaddadi, Mahmoud Alreefi . Academic and non-academic life stressors and their impact on psychological wellbeing of medical students. AIMS Public Health, 2021, 8(4): 563-580. doi: 10.3934/publichealth.2021046
    [2] Fiammetta Iannuzzo, Michele La Versa, Fabrizio Turiaco, Gianluca Pandolfo, Carmela Mento, Maria Rosaria Anna Muscatello, Antonio Bruno, Clara Lombardo . Boredom and affective temperaments as factors hindering smoking cessation: An exploration within an Italian sample. AIMS Public Health, 2025, 12(1): 33-43. doi: 10.3934/publichealth.2025003
    [3] Akari Miyazaki, Naoko Kumada Deguchi, Tomoko Omiya . Difficulties and distress experienced by Japanese public health nurses specializing in quarantine services when dealing with COVID-19: A qualitative study in peri-urban municipality. AIMS Public Health, 2023, 10(2): 235-251. doi: 10.3934/publichealth.2023018
    [4] Pradeep Kumar Sahu, Hakki Dalcik, Cannur Dalcik, Madan Mohan Gupta, Vijay Kumar Chattu, Srikanth Umakanthan . Best practices for effective implementation of online teaching and learning in medical and health professions education: during COVID-19 and beyond. AIMS Public Health, 2022, 9(2): 278-292. doi: 10.3934/publichealth.2022019
    [5] Marybeth Gasman, Tiffany Smith, Carmen Ye, Thai-Huy Nguyen . HBCUs and the Production of Doctors. AIMS Public Health, 2017, 4(6): 579-589. doi: 10.3934/publichealth.2017.6.579
    [6] Areeb Khalid, Muhammad Waqar Younas, Hashim Khan, Muhammad Sarfraz Khan, Abdur Rehman Malik, Adam Umair Ashraf Butt, Basit Ali . Relationship between knowledge on COVID-19 and psychological distress among students living in quarantine: an email survey. AIMS Public Health, 2021, 8(1): 90-99. doi: 10.3934/publichealth.2021007
    [7] Mohammad Mofatteh . Risk factors associated with stress, anxiety, and depression among university undergraduate students. AIMS Public Health, 2021, 8(1): 36-65. doi: 10.3934/publichealth.2021004
    [8] Sameer Badri Al-Mhanna, Alexios Batrakoulis, Abdulrahman M. Sheikh, Abdulaziz A. Aldayel, Abdulwali Sabo, Mahaneem Mohamed, Hafeez Abiola Afolabi, Abdirizak Yusuf Ahmed, Sahra Isse Mohamed, Mehmet Gülü, Wan Syaheedah Wan Ghazali . Impact of COVID-19 lockdown on physical activity behavior among students in Somalia. AIMS Public Health, 2024, 11(2): 459-476. doi: 10.3934/publichealth.2024023
    [9] Dylan C. Rowe, Zachary K. Winkelmann, Shawn M. Arent, Michelle A. Arent, Alexa J. Chandler, Nancy A. Uriegas, Toni M. Torres-McGehee . A qualitative report of the perceptions of the COVID-19 pandemic from collegiate student-athletes. AIMS Public Health, 2022, 9(2): 357-377. doi: 10.3934/publichealth.2022025
    [10] Maryam Tabrizi, Wei-Chen Lee . Geriatric oral health competency among dental providers. AIMS Public Health, 2021, 8(4): 682-690. doi: 10.3934/publichealth.2021054
  • The Coronavirus Disease 2019 (COVID-19) pandemic induced economic shock in Greece, which translated into a decrease in household income. Thus, the objective of this study is to measure social inequality with regard to income loss due to the COVID-19 pandemic in Greece. In addition, we aim to identify the characteristics of those experiencing income loss due to the pandemic. The study uses data from the “Public Opinion in the European Union (EU) in Time of Coronavirus Crisis. Third Round” survey. The sample consists of 1036 individuals aged between 16 and 54 years. To measure inequality, the Erreygers' Concentration Index (CI) is calculated, using social class as the ranking variable. To identify the characteristics of those experiencing income loss, a logistic regression model is fitted using the region of residence and several demographic and socioeconomic variables as potential predictors. According to the results, social inequality does not exist with regard to income loss due to the COVID-19 pandemic. Thus, our findings indicate the negative influence of the pandemic on the incomes of individuals from all social classes in Greece. According to the results of the logistic regression model, the odds of experiencing income loss are higher for residents of the Aegean Islands and Crete but also for self-employed, part-time employed, and unemployed individuals. These findings indicate the negative influence of the pandemic on Greek tourism and on sectors employing a large proportion of non-standard workers. Although inequality does not exist, a substantial proportion of those losing income due to the pandemic is in line with the global picture.



    Since the World Health Organization (WHO) officially announced the Corona virus disease-19 (COVID-19) pandemic [1], the ongoing COVID-19 health crisis has caused a dramatic burden on people's mental health [2]. Several reports indicated that the pandemic caused an increase in the prevalence of depression, insomnia, anxiety, and distress in at least one-third of the general population [3][5]. Furthermore, the prevalence of mental health symptoms was higher in people with occupational exposure risks, such as healthcare workers [6]. For instance, a previous study evaluated the resilience levels of nurses during the COVID-19 pandemic. Resilience was higher during the initial phase, but depression increased later. Self-efficacy, optimism, and emotional intelligence were found to be significant predictors of resilience, and emphasized the importance of fostering these factors to enhance healthcare workers' resilience and prevent burnout [7]. Moreover, while data may not always be consistent across studies, findings from the early stages of the pandemic suggest that females in the general population were more susceptible to experiencing anxiety [8]. In addition, the measures implemented to contain the pandemic and quarantine substantially impacted mental health due to frustration, boredom, isolation, and changes in personal social interactions [9][11].

    The medical curriculum is typically very demanding and challenges medical trainees' mental health [12]. Medical students are subject to stressors that are typical nearly all college students: financial hardship, health risk behaviors, sleep deprivation, peer pressure, and extreme competition [13]. Although previous studies extensively assessed medical students' mental health during the pandemic [14], little is known about the endemic-related stress in medical students. During the endemic phase of COVID-19, stress levels in healthcare and education sectors remained significantly high and medical students were required to change their academic and clinical settings and their attitude toward patient' care [15]. Specifically, the aftermath of the pandemic posed challenges for medical students who experienced stress in transitioning to the conventional rhythms of academic and clinical activities [16]. The disruptive impact of the pandemic on medical education, as characterized by canceled rotations, postponed examinations, and the transition to remote learning modalities, added to the complexity of adjustments to in-person education [17]. Furthermore, the post-pandemic landscape introduced uncertainties in career trajectories, thereby intensifying the burden of expectation management [18]. Thus, even as the pandemic ended, the residual effects of these stressors persisted, thus perpetuating a cycle of psychological distress [19]. Understanding the role of emotional reactivity and attachment during this new phase is essential to provide targeted support and interventions to promote the students' mental health and resilience. According to Akiskal's model, five temperament traits describe the emotional reactivity types that characterize healthy subjects' behavioral patterns and individuals with an affective disorder spectrum [20]. These traits are stable, innate, and predict psychopathology [21]. Previous studies that used the Temperament Evaluation Memphis, Pisa, Paris, and San Diego Auto-questionnaire (TEMPS-A) described an association of temperament traits with psychological distress during the pandemic. Furthermore, affective temperament traits can predict the perceived stress, anxiety, depression, and health-risk behaviors in medical students [22][25]. They have been employed to investigate the effect of the COVID-19 outbreak on mental health in both the general population and in psychiatric patients [26][29].

    Previous studies have shown that attachment relationships are essential when coping with stressful events [30]. The attachment theory suggests that children develop an emotional and behavioral substrate that regulates interactions and closeness with caregivers [31]. These interactions can grow in a positive way, thus resulting in a secure attachment. Conversely, if caregivers do not provide sensitive and meaningful interactions, children may develop an insecure attachment that indicates difficulty and distrust toward protective figures [31]. According to this theory, the attachment we develop during childhood affects our romantic and social interactions in adulthood and regulates our emotional reactivity [32]. For instance, adult attachment styles have been categorized as secure, anxious, and avoidant [33]. Individuals with a secure attachment rely on social support (a person's social networks and romantic relationships) to reduce arousal, emotional reactivity, and anxiety triggered by stressful events [34],[35]. On the contrary, individuals with insecure, avoidant adult attachment styles are uncomfortable with closeness and rely on themselves when facing stressful events. An insecure, anxious attachment style leads to reassurance-seeking and a dependence on partner support [36],[37]. The Attachment Style Questionnaire (ASQ) [38],[39] probes this theoretical framework. Previous studies in college students and the general population [40],[41] support the hypothesis that an insecure adult attachment is associated with stress and psychological morbidities during the COVID-19 pandemic.

    In light of the aforementioned literature, this study aims to investigate medical students' distress at a major medical university in China during the transition to the endemic phase of COVID-19. We hypothesized that specific temperament traits and insecure attachments might be particularly relevant in explaining medical students' distress during the transition to the endemic phase.

    A cross-sectional study was conducted at the Southern Medical University, Guangdong, China. All participants provided written informed consent before beginning the surveys. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines for cross-sectional studies. The study was approved by the ethics committee of the Southern Medical University, Guangdong, China (protocol number: 202207650) and was conducted following the principles of the Declaration of Helsinki. Informed consent was obtained from all subjects involved in this study.

    This cross-sectional, online study was conducted in May 2022. The eligible individuals included all medical students (N = 567) enrolled at the Southern Medical University who could read and sign the informed consent section. Data were collected using the online software WENJUANXING (www.wjx.cn.). The first page of the electronic questionnaire included a description and purpose of the study, statements regarding confidentiality, and the voluntary basis of participation in the study. Students that submitted the questionnaire were considered to have provided their informed consent. Participants with diagnoses of psychiatric diseases were prevented from progressing with the questionnaire. All of the data were collected anonymously, the participation was voluntary, and the students did not receive compensation.

    The survey included sociodemographic questions (e.g., age, gender, marital status, and academic year) and standardized questionnaires that investigated pandemic-related distress, affective temperament traits, and adult attachment styles in relationships.

    The Kessler Psychological Distress Scale (K10) is a self-rated 10-item questionnaire intended to investigate the distress that a person has experienced in the most recent 30 days [42]. The Chinese version of the scale was previously validated [43]. Each of the items is rated with a 5-level frequency Likert scale: 1. None of the time; 2. A little of the time; 3. Some of the time; 4. Most of the time; and 5. All of the time. The sum of each of the ten questions yields a score ranging from 10 to 50, with higher scores pointing to a more significant mental distress [42]. In this study, the Cronbach's alpha value of the K10 scale was 0.89.

    The Temperament Evaluation of Memphis, Pisa, Paris, and San Diego-auto-questionnaire short version (TEMPS-A) is a self-administered 39-item, true-false questionnaire measuring five dimensions of affective temperament (including cyclothymic, 12 items; depressive, irritable, hyperthymic, 8 items each; and anxious, 3 items). The score is obtained by summing the items after dividing them by subscales (false = 1; true = 2) [14]. The Chinese version of the scale was previously validated [44]. In this study, the Cronbach's alpha values of the TEMPS-A short version ranged from 0.71 to 0.84.

    The Attachment Style Questionnaire (ASQ) is a self-administered questionnaire consisting of 40 items to be answered using a 6-point Likert scale ( 1 = “totally disagree” and 6 = “totally agree”) [38]. The Chinese version of the scale was previously validated [45]. The subdomains include “Confidence”, “Discomfort with closeness”, “Relationships as secondary”, “Need for approval”, and “Preoccupation with relationships”. According to the attachment theory [34], confidence describes a secure attachment (ASQ-Secure), a discomfort with closeness and relationships as a secondary assess attachment avoidance (ASQ-Avoidance), and a need for approval and preoccupation with relationships assess attachment anxiety (ASQ-Anxiety). In this study, the Cronbach's alpha value of the secure, avoidant, and anxious attachment styles ranged from 0.68 to 0.75.

    Descriptive statistics of the demographic variables, the students' distress, the affective temperament dimensions, and the adult attachment styles were generated. The IBM Statistics software, version 25 (IBM Corp., Armonk, NY, USA), was used to perform the statistical analyses. First, we investigated if the data were normally distributed using the Kolmogorov- Smirnov normality test. Then, we performed a correlation analysis of the continuous variables using the Spearman's correlation coefficient. The variables were subsequently entered into a multiple linear regression analysis model to investigate the predictors of the students “distress” during the aftermath of COVID-19. We assessed multicollinearity using the variance inflation factor (VIF). The alpha level was set at 0.05, and a p  < 0 .05 was statistically significant.

    We conducted an a priori power analysis using G*Power, version 3.1.9.7 [46], to determine the minimum sample size required to test the study hypothesis. The results indicated that the required sample size to achieve 80% power to detect a medium effect was N = 232 for a multiple linear regression analysis, at a significance criterion of α = 0.05. Thus, the obtained sample size of N = 402 is adequate to test the study hypothesis. The mean participant age was (21.3 ± 3.1). Most of the participants were female (N  =  251, 62.4%), and the majority were single (N  = 331, 80%). 119 (29.6%) were freshman (Table 1).

    Table 1.  Demographic characteristics of the study population (N = 402).
    Project Data
    Age (Mean ± SD) 21.3 ± 3.1
    Gender
    Male 151 37.6%
    Female 251 62.4%
    Relationship
    Single 321 80%
    Partnered 81 20%
    Academic Year
    Year 1 119 29.6%
    Year 2 58 14.4%
    Year 3 32 8%
    Year 4 16 4%
    Year 5 110 27.4%
    Year 6 22 5.5%
    Year 7 21 5.2%
    Year 8 24 6%

     | Show Table
    DownLoad: CSV

    All the data analyzed in the study (temperament and attachment style dimensions) did not meet the criterion of compliance with the normal distribution. Hence, the data are reported as median and Q1 and Q3 quartile (Table 2).

    Table 2.  Psychological variables. Data presented as median (Interquartile range, Q1–Q3) (N = 402).
    Temperament Traits Data
    TEMPS-A Cyclothymic 0.6 IQR (0.3–1)
    TEMPS-A Depressive 0.3 IQR (0–1)
    TEMPS-A Irritable 0.1 IQR (0–1)
    TEMPS-A Hyperthymic 0.5 IQR (0.1–1)
    TEMPS-A Anxious 0.7 IQR (0.3–1)
    Adult attachment styles
    ASQ-Confidence 32 IQR (28–37)
    ASQ-Anxious 28.3 IQR (24–31)
    ASQ-Avoidant 24.5 IQR (21–28)
    Stress 19 IQR (15–24)

    Note: TEMPS-A, Temperament Evaluation of Memphis, Pisa, San Diego-auto questionnaire; ASQ: Attachment Styles Questionnaire. ASQ-Avoidant was obtained averaging the “Discomfort with Closeness” and “Relation as Secondary” dimensions; ASQ-Anxious was obtained by averaging the “Need for approval” and the “Preoccupation with Relationships dimensions”.

     | Show Table
    DownLoad: CSV

    Furthermore, the correlation analysis showed that the distress was positively correlated with the cyclothymic (r = 0.173, p < 0.01) and depressive (r = 0.198, p < 0.01) TEMPS-A subscales scores. The secure attachment style (ASQ-Confidence) was inversely correlated with stress (r = −0.099, p < 0.05), while the insecure attachment styles scores were associated with an increased stress (ASQ-Anxious: r = 0.418, p < 0.01; ASQ-avoidant: r= 0.547, p < 0.01) (Table 3).

    We carried out a multiple regression analysis to determine the variables that better predict stress in these medical students. The coefficient of determination (R2 = 0.36) indicates that the regression equation predicted 36% of the variance, further suggesting that the model has a good prediction power for the dependent variable. The ANOVA F-value (F = 18.36, p < 0.0001) indicates a significant and linear relationship between the predictor criterion variables. The results indicate that both the cyclothymic (β = 2.1, p = 0.048) and depressive (β = 1.2, p = 0.001) temperament traits are positive predictors of stress. Furthermore, insecure attachment styles can predict the criterion variable (ASQ-anxious: β = 0.19, p = 0.006; ASQ-avoidant: β = 0.07, p < 0.001) (Table 4). The VIF was <2. 1 for all the predictor variables, excluding a significant multicollinearity.

    Table 3.  Associations between stress with temperaments traits score and adult attachment styles score, and age (N = 402).
    No Project 1 2 3 4 5 6 7 8 9 10
    1 Age 1 - - - - - - - - -
    2 TEMPS-A cyclothymic 0.725** 1 - - - - - - - -
    3 TEMPS-A depressive 0.646** 0.680** 1 - - - - - - -
    4 TEMPS-A irritable 0.637** 0.591** 0.566** 1 - - - - - -
    5 TEMPS-A hyperthymic 0.747** 0.771** 0.680** 0.638** 1 - - - - -
    6 TEMPS-A anxious 0.709** 0.759** 0.683** 0.591** 0.786** 1 - - - -
    7 ASQ-confidence −0.441** −0.454** −0.568** −0.421** −0.519** −0.601** 1 - - -
    8 ASQ-anxious 0.001 0.110* 0.068 0 −0.101* 0 −0.062 1 - -
    9 ASQ-avoidant −0.051 0.084 0.053 −0.084 −0.168** −0.003 0.019 0.581** 1 -
    10 Stress 0.054 0.173** 0.198** −0.017 −0.026 0.084 −0.099** 0.418** 0.547** 1

    Note: * p<0.05, ** p<0.01. TEMPS-A, Temperament Evaluation of Memphis, Pisa, San Diego-auto questionnaire; ASQ: Attachment Styles Questionnaire.

     | Show Table
    DownLoad: CSV
    Table 4.  Multiple linear regression model for predictors of medical students', Dependent variable: students' stress (N = 402).
    Project B Std. Error p 95.0% CI
    Lower Bound Upper Bound
    (Constant) 0.275 3.912 0.944 −7.416 7.966
    Sex 0.038 0.611 0.95 −1.163 1.24
    Age 0.001 0.156 0.996 −0.305 0.307
    Relationship status 1.228 0.75 0.102 −0.247 2.704
    Academic year −0.027 0.134 0.841 −0.29 0.236
    TEMPS-A Cyclothymic 2.107 1.061 0.048* 0.02 4.193
    TEMPS-A Depressive 1.201 0.367 0.001** 0.481 1.922
    TEMPS-A Irritable −1.01 0.54 0.062 −2.071 0.051
    TEMPS-A Hyperthymic −1.517 0.916 0.098 −3.318 0.283
    TEMPS-A Anxious −0.374 0.926 0.687 −2.194 1.446
    ASQ-confidence −0.02 0.034 0.554 −0.086 0.046
    ASQ-anxious 0.19 0.068 0.006** 0.055 0.324
    ASQ-avoidant 0.544 0.07 <0.0001** 0.406 0.683

    Note: *p < 0.05, **p < 0.01.

     | Show Table
    DownLoad: CSV

    Transitioning to the endemic phase of COVID-19 can be challenging and stressful for medical students. Investigating factors related to stress during this period is paramount. In a multiple regression analysis, the cyclothymic and depressive temperament traits were significant predictors of distress. Furthermore, the anxious and avoidant attachment styles in relationships were associated with higher stress levels in the medical students.

    We report an association between cyclothymic and depressive temperament scores and stress. Our results coincide with the previously published literature. For instance, the cyclothymic temperament is characterized by cyclical mood swings, with periods of hypomania and depression [20]. These features have similarities to bipolar disorder, though they are “subthreshold” and do not meet the criteria for a diagnosis of bipolar disorder [47]. Yet, these traits influence behaviors and emotional reactivities and impact daily activities [48]. Individuals with high scores on the cyclothymic subscale demonstrated symptoms of anxiety and depression and were more likely to develop mood disorders and even bipolar disorder later in life [49]. Furthermore, our findings align with the available literature, which indicates that cyclothymic temperament can make a person more vulnerable to stress [20]. The rapid mood changes characteristic of this trait interfered with the coping strategies that were implemented to deal with daily stressors [22]. People with cyclothymic temperament may also have more difficulty regulating their emotions and managing stress in general [20]. Similarly, depressive temperament traits are characterized by excessive self-esteem, pessimism, rumination, and apathy [20]. Individuals with a depressive temperament are more empathic and prone to guilt. High scores in depressive traits have been associated with stress, burnout, and mood disorders [50].

    Our data confirmed and expanded the findings of other studies that investigated the association of temperament traits with a positive mental status in medical students during the COVID-19 pandemic. Cyclothymic and depressive traits inversely correlated with mental flourishing in a cohort of Italian medical students [51] and predicted health-risk behaviors and perceived stress in medical students [24],[25]. Our study validated the impact of cyclothymic and depressive traits on mental health in different cultural and pandemic settings. Taken together, these results support the use of TEMPS-A to investigate the harmful effects of the pandemic on medical students' mental health. Moreover, studies in the general population [26] and healthcare workers [52] further support our findings.

    We showed that the secure adult attachment style inversely correlated with stress. In contrast, higher scores in the insecure attachment dimensions (anxious and avoidant) were associated with and predicted stress in Chinese medical students during the time of our survey. Previous research indicates that individuals with habitual and rewarding loving relationships demonstrated excellent social interactions, high mental health, and low psychiatric morbidity [53],[54]. Our results are consistent with this view and further support the attachment theory conceptualization of a relationship as an emotional aid in response to stressful and adverse events [55]. The data were consistent with previous results that indicated a link between secure attachment adaptative coping, empathy, and resilience [56]. In contrast, individuals with an insecure-anxious attachment style were self-doubting and depended on others for validation. At the same time, they displayed proximity-seeking behaviors, a fear of rejection, and a distrust of others. Medical students with high scores in the insecure-avoidant dimension were afraid of intimacy and lacked empathy [57]. Additionally, they had a negative view of themselves and were overly independent [39]. Our results support the notion that individuals with insecure attachments have difficulty managing stressful events. For instance, individuals with an insecure-anxious attachment style have difficulty trusting others, making it harder for them to reach out for help when needed [35]. Additionally, they may experience more intense emotional reactions to stressors. Individuals with insecure-avoidant attachment style may have difficulty forming close relationships, which may result in them not having a support system to rely on during times of stress [35],[36].

    The current study conceptual frameworks are rooted in stress theories that explain how an individual's response to stressors can affect their physical and mental health. According to Lazarus and Folkman [58], a medical students' stress may develop through abnormal cognitive appraisal and coping. To this extent, temperament traits and the consequent excessive emotional reactivity coupled with situational factors (fear of infection, curricular and social/behavioral changes) may lead to maladaptive emotional coping. Thus, stress may occur when the students perceive a situation as threatening and feel they do not have the resources to cope [51]. The allostatic load theory was described by McEwen and Stellar in 1993 [59] and focused on the physiological effects of chronic stress. According to this theory, allostatic load refers to the bodily and mental adaptations (hormonal, immunologic, inflammatory, trophic, and plastic) to chronic environmental stressors. Stress occurs when an individual perceives a new situation as threatening and feel that they do not have the resources to cope with it. This perception triggers the body's “fight or flight” response, which leads to maladaptive neuronal plasticity and, ultimately, a predisposition to anxiety and depression [59],[60]. Medical students are regularly exposed to academic stress and display abnormal cortical plasticity and metaplasticity [61][63]. Our data suggests that the students' innate emotional reactivity and attachment style may alter their coping strategies, which increases the allostatic load of life events. To this extent, the cognitive load theory [64] states that our cognitive capacity is limited, and excessive (cognitive) demands can result in an overload and decreased performance. The pandemic fatigue resulting from multiple pandemic waves can also be considered a specific and severe form of cognitive load as defined under the cognitive load theory [65]. It may be relevant to understand stress in students enrolled in a high demanding medical curriculum. Lastly, previous studies highlighted the interplay between stressors and relationship quality among the general population [66],[67]. According to the socioemotional selectivity theory (SST) [68], the impact of pandemic-related stress may be particularly pronounced for individuals who place a high value on emotional closeness. SST can be used to understand the pandemic's effects on the medical students' social and emotional goals and how these may have changed because of the pandemic. As previously reported during the SARS epidemic in Hong Kong, individuals prioritize emotionally close relationships during a pandemic. The pandemic restrictions interfere with social and emotional goals and regulation strategies during this time. They may be pivotal for the development of stress, anxiety, fear, sadness, and loneliness, regardless of the individual's age [69],[50]. While the transition to the endemic phase has enabled the partial restoration of social support networks, medical students are still experiencing significant stressors related to COVID-19 and the adjustments required in their academic and clinical environments. Institutions need to adapt their support strategies to effectively address these evolving needs [70]. Overall, our results are consistent with relevant frameworks and highlight the importance of innate determinants of emotional reactivity and secure attachments when medical students face unprecedented, repeated threats.

    This study has some limitations. First, the cross-sectional design makes it impossible to infer causality. Since our study included medical students from one medical school, we cannot rule out a selection bias, thus limiting the results' generalizability. In addition, data were collected through self-reporting, which may be biased because individuals tend to report more socially acceptable answers, and the rehearsal of negative autobiographical memories may influence the results [71]. Furthermore, future research should aim to incorporate a more comprehensive demographic evaluation to enhance the generalizability of the findings. Despite these limitations, the study was well-powered and was the first reported study to address the association between these variables in a population of Chinese medical students during the aftermath of the COVID-19 Omicron wave.

    Our study highlights the role of temperament traits and healthy relationships that affect the mental well-being of medical students, especially during times of crisis and the transition to the new normal. Other contributing factors may include coping mechanisms, access to counseling, and stress-management services. Additionally, as attachment styles can change over time, providing appropriate mental health services based on our findings could help improve the mental health of medical students.

    The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.



    Conflict of interest



    The author declares no conflicts of interest.

    [1] Wang C, Horby PW, Hayden FG, et al. (2020) A novel coronavirus outbreak of global health concern. Lancet 395: 470-473. doi: 10.1016/S0140-6736(20)30185-9
    [2] WHO WHO coronavirus disease (COVID-19) dashboard, 2021 Available from: https://covid19.who.int.
    [3] Susskind D, Vines D (2020) The economics of the COVID-19 pandemic: an assessment. Oxf Rev Econ Policy 36: S1-S13. doi: 10.1093/oxrep/graa036
    [4] Furman J (2020) Protecting people now, helping the economy rebound later. Mitigating the COVID economic crisis act fast and do whatever it takes 191-196. Available from: https://voxeu.org/system/files/epublication/COVIDEconomicCrisis.pdf.
    [5] World Bank (2020)  Global economic prospects, June 2020 Washington, DC: World Bank.
    [6] Marjanovic Z, Greenglass ER, Fiksenbaum L, et al. (2013) Psychometric evaluation of the Financial Threat Scale (FTS) in the context of the great recession. J Econ Psychol 36: 1-10. doi: 10.1016/j.joep.2013.02.005
    [7] Baddour K, Kudrick LD, Neopaney A, et al. (2020) Potential impact of the COVID-19 pandemic on financial toxicity in cancer survivors. Head Neck 42: 1332-1338. doi: 10.1002/hed.26187
    [8] The Lancet (2020) Redefining vulnerability in the era of COVID-19. Lancet 395: 1089.
    [9] Finch A, Tribble AG (2021) The path ahead: From global pandemic to health promotion. Prev Med Rep 21: 101271. doi: 10.1016/j.pmedr.2020.101271
    [10] Witteveen D (2020) Sociodemographic inequality in exposure to COVID-19-Induced economic hardship in the United Kingdom. Res Soc Stratification Mob 69: 100551. doi: 10.1016/j.rssm.2020.100551
    [11] Blundell R, Costa Dias M, Joyce R, et al. (2020) COVID-19 and inequalities. Fisc Stud 41: 291-319. doi: 10.1111/1475-5890.12232
    [12] Burström B, Tao W (2020) Social determinants of health and inequalities in COVID-19. Eur J Public Health 30: 617-618. doi: 10.1093/eurpub/ckaa095
    [13] Economou C, Kaitelidou D, Konstantakopoulou O, et al. (2020) Preventing transmission. Policy responses: Greece Available from: https://www.covid19healthsystem.org/countries/greece/countrypage.aspx.
    [14] Foundation for Economic & Industrial Research The Greek economy. Quarterly Bulletin VOL. 2/20 (2020) .Available from: http://iobe.gr/greek_economy_en.asp?PD=2020.
    [15] Marsellou E (2020) Consumer price index fell during COVID-19 lockdown. Greek economic outlook Centre of Planning and Economic Research, 16-18. Available from: https://www.kepe.gr/index.php/en/research/recent-publications/greek-economic outlook/item/3044-greek-economic-outlook-issue-42.html.
    [16] European Parliament Public opinion in the EU in time of coronavirus crisis. Third round (2020) .Available from: https://www.europarl.europa.eu/at-your-service/en/be-heard/eurobarometer/public-opinion-in-the-eu-in-time-of-coronavirus-crisis-3.
    [17] Hazakis KJ (2021) Is there a way out of the crisis? Macroeconomic challenges for Greece after the Covid-19 pandemic. Eur Polit Soc 1-15.
    [18] Gaviria A (2002) Household responses to adverse income shocks in Latin America. Revista Dessarolo y Sociedad 99-127. doi: 10.13043/dys.49.3
    [19] O'Donnell O, O'Neill S, Van Ourti T, et al. (2016) Conindex: estimation of concentration indices. Stata J 16: 112-138. doi: 10.1177/1536867X1601600112
    [20] O'Donnell O, van Doorslaer E, Wagstaff A, et al. (2007) Analyzing health equity using household survey data: A guide to techniques and their implementation. World Bank .
    [21] Kakwani N, Wagstaff A, van Doorslaer E (1997) Socioeconomic inqualities in health: Measurement, computation, and statistical inference. J Econom 77: 87-103. doi: 10.1016/S0304-4076(96)01807-6
    [22] Erreygers G (2009) Correcting the concentration index. J Health Econ 28: 504-515. doi: 10.1016/j.jhealeco.2008.02.003
    [23] van Doorslaer E, Van Ourti T (2013) Measuring inequality and inequity in health and health care. The Oxford handbook of health economics Oxford: Oxford University Press, 837-869.
    [24] Hendrickx J (1999)  Stata technical Bulletin-52, using categorical variables in Stata College Station, TX: Stata LP, 2-8.
    [25] Nattino G, Lemeshow S, Phillips G, et al. (2017) Assessing the calibration of dichotomous outcome models with the calibration belt. Stata J 17: 1003-1014. doi: 10.1177/1536867X1801700414
    [26] Park CL, Russell BS, Fendrich M, et al. (2020) Americans' COVID-19 Stress, Coping, and Adherence to CDC Guidelines. J Gen Intern Med 35: 2296-2303. doi: 10.1007/s11606-020-05898-9
    [27] Settersten RA, Bernardi L, Härkönen J, et al. (2020) Understanding the effects of Covid-19 through a life course lens. Adv Life Course Res 45. doi: 10.1016/j.alcr.2020.100360
    [28] Sarkar P, Debnath N, Reang D (2021) Coupled human-environment system amid COVID-19 crisis: A conceptual model to understand the nexus. Sci Total Environ 753: 141757. doi: 10.1016/j.scitotenv.2020.141757
    [29] Kansiime MK, Tambo JA, Mugambi I, et al. (2021) COVID-19 implications on household income and food security in Kenya and Uganda: findings from a rapid assessment. World Dev 137: 105199. doi: 10.1016/j.worlddev.2020.105199
    [30] Zaidel EJ, Forsyth CJ, Novick G, et al. (2020) COVID-19: implications for people with Chagas disease. Glob Heart 15: 69. doi: 10.5334/gh.891
    [31] Levine DT, Morton J, O'Reilly M (2020) Child safety, protection, and safeguarding in the time of COVID-19 in Great Britain: proposing a conceptual framework. Child Abuse Negl 110: 104668. doi: 10.1016/j.chiabu.2020.104668
    [32] Ali S, Asaria M, Stranges S (2020) COVID-19 and inequality: are we all in this together? Can J Public Health 111: 415-416. doi: 10.17269/s41997-020-00351-0
    [33] Leone T COVID-19 Sends the Bill: Socially disadvantaged workers suffer the severest losses in earnings. Latin American and the Caribbean economic association working papers Seres. No 0050 (2020) .Available from: http://vox.lacea.org/files/Working_Papers/lacea_wps_0050_leone.pdf.
    [34] Carman K, Nataraj S (2020) How are Americans paying their bills during the COVID-19 pandemic? Rand Corporation .
    [35] Sampaio FJB (2020) Reflections on the COVID-19 pandemic. Int Braz J Urol 46: 499-500. doi: 10.1590/s1677-5538.ibju.2020.04.02
    [36] Petrakis PE, Kostis PC (2020)  The Evolution of the Greek Economy: Past Challenges and Future Approaches Cham: Springer International Publishing, 156-157.
    [37] Betcherman G, Giannakopoulos N, Laliotis I, et al. (2020) Reacting quickly and protecting jobs. The short-term impacts of the COVID-19 lockdown on the Greek labor market. World Bank Group .
    [38] Finseraas H, Ringdal K (2012) Economic globalization, personal risks and the demand for a comprehensive welfare state. The future of the welfare state Edward Elgar, 68-87.
    [39] Papanikos GT (2020) The impact of the Covid-19 pandemic on Greek tourism. Athens J Tourism 7: 87-100. doi: 10.30958/ajt.7-2-2
    [40] Organization for Economic Co-operation and Development Distributional risks associated with non-standard work: Stylised facts and policy considerations (2020) .Available from: https://www.oecd.org/coronavirus/policy-responses/distributional-risks-associated-with-non-standard-work-stylised-facts-and-policy-considerations-68fa7d61/.
    [41] Kartseva MA, Kuznetsova PO (2020) The economic consequences of the coronavirus pandemic: which groups will suffer more in terms of loss of employment and income? Popul Econ 4: 26-33. doi: 10.3897/popecon.4.e53194
    [42] Hansel TC, Saltzman LY, Bordnick PS (2020) Behavioral health and response for COVID-19. Disaster Med Public Health Prep 14: 670-676. doi: 10.1017/dmp.2020.180
    [43] Foster G (2020) Early estimates of the impact of COVID-19 disruptions on jobs, wages, and lifetime earnings of schoolchildren in Australia. Aust J Lab Econ 23: 129-151.
    [44] Armitage R, Nellums LB (2020) COVID-19: compounding the health-related harms of human trafficking. Clin Med 24: 100409.
    [45] Brener A, Mazor-Aronovitch K, Rachmiel M, et al. (2020) Lessons Learned from the Continuous Glucose Monitoring Metrics in Pediatric Patients with Type 1 Diabetes under COVID-19 Lockdown. Acta Diabetologica 57: 1511-1517. doi: 10.1007/s00592-020-01596-4
    [46] Marmot M (2002) The influence of income on health: views of an epidemiologist. Health Aff 21: 31-46. doi: 10.1377/hlthaff.21.2.31
    [47] Marmot M, Wilkinson RG (2001) Psychosocial and material pathways in the relation between income and health: A response to Lynch et al. BMJ 322: 1233-1236. doi: 10.1136/bmj.322.7296.1233
    [48] Douglas M, Katikireddi SV, Taulbut M, et al. (2020) Mitigating the wider health effects of Covid-19 pandemic response. BMJ 369: m1557. doi: 10.1136/bmj.m1557
    [49] Kajdy A, Feduniw S, Ajdacka U, et al. (2020) Risk factors for anxiety and depression among pregnant women during the COVID-19 pandemic: A web-based cross-sectional survey. Medicine 99: e21279. doi: 10.1097/MD.0000000000021279
    [50] Jay J, Bor J, Nsoesie EO, et al. (2020) Neighbourhood income and physical distancing during the COVID-19 pandemic in the United States. Nat Hum Behav 4: 1294-302. doi: 10.1038/s41562-020-00998-2
    [51] Glover RE, van Schalkwyk MCI, Akl EA, et al. (2020) A framework for identifying and mitigating the equity harms of COVID-19 policy interventions. J Clin Epidemiol 128: 35-48. doi: 10.1016/j.jclinepi.2020.06.004
    [52] Papanastasiou S, Papatheodorou C (2018) The Greek depression: poverty outcomes and welfare responses. East West J Econ Bus XXI: 205-222.
    [53] Zavras D (2020) Studying healthcare affordability during an economic recession: the case of Greece. Int J Environ Res Public Health 17: 7790. doi: 10.3390/ijerph17217790
    [54] Chantzaras A, Yfantopoulos J (2017) The effects of the economic crisis on health status and health inequalities in Greece. Value Health 20: A510. doi: 10.1016/j.jval.2017.08.630
    [55] Adrikopoulou C (2020) Greece COVID-19. Eur State Aid Law Q 19: 89-92. doi: 10.21552/estal/2020/1/20
    [56] European Foundation for the Improvement of Living and Working Conditions COVID-19 EU policywatch. Database of national-level responses (2020) .Available from: https://static.eurofound.europa.eu/covid19db/index.html.
    [57] European Systemic Risk Board Greece. Measures taken in response to coronavirus (COVID-19) pandemic (2021) .Available from: https://www.esrb.europa.eu/home/search/coronavirus/countries/html/esrb.covidpmc_greece.en.html.
    [58] European Commission Greece. Details of Greece's support measures to help citizens and companies during the significant economic impact of the coronavirus pandemic (2021) .Available from: https://ec.europa.eu/info/live-work-travel-eu/coronavirus-response/jobs-and-economy-during-coronavirus-pandemic/state-aid cases/greece_en.
    [59] International Monetary Fund Greece. IMF country report No.20/308 (2020) .Available from: https://www.imf.org/en/Publications/CR/Issues/2020/11/30/Greece-Second-Post-Program-Monitoring-Discussions-Press-Release-Staff-Report-Staff-Statement-49922.
    [60] Organisation for Economic Co-operation and Development (2020)  Regional policy for Greece post-2020. OECD Territorial Reviews Paris: OECD Publishing.
    [61] Organisation for Economic Co-operation and Development The territorial impact of COVID-19: Managing the crisis across levels of government (2020) .Available from: http://www.oecd.org/coronavirus/policy-responses/the-territorial-impact-of-covid-19-managing-the-crisis-across-levels-of-government-d3e314e1/.
    [62] Acharya R, Porwal A (2020) A vulnerability index for the management of and response to the COVID-19 epidemic in India: An ecological study. Lancet Glob Health 8: e1142-e1151. doi: 10.1016/S2214-109X(20)30300-4
  • Reader Comments
  • © 2021 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(3716) PDF downloads(222) Cited by(6)

Figures and Tables

Tables(3)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog