The present study examines whether easy talking with parents would buffer the association between bullying victimization and declining academic performance among a nationally representative sample of foreign-born and U.S.-born adolescents. The study was drawn from the Health Behavior in School-Aged Children 2009-2010 cohort study in the United States. Analyses include descriptive statistics, correlation analysis, and moderated regression analysis for each group (i.e., foreign-born and U.S.-born). Results indicate a significant relationship between bullying victimization and declining academic performance for foreign-born and U.S.-born adolescents. Findings also suggest that easy talking with parents moderated the association between bullying victimization and declining academic performance but for foreign-born adolescents only. The study highlights the importance of family and parental communication to ensure immigrant adolescents’ health, well-being, and academic progress. Practice and policy implications and future research directions are discussed.

The COVID-19 pandemic has led to an abrupt transition from face-to-face learning to online learning, which has also affected the mental health of college students. In this study, we examined the relationship between students adjustment to online learning and their mental health by using the Dual-Continua Model. The model assumes that mental disorder and mental well-being are related yet distinct factors of mental health. For this purpose, 2,933 college students completed an online survey around the beginning of the Fall semester of 2020 (N = 1,724) and the Spring semester of 2021 (N = 1,209). We assessed participants mental well-being, mental disorders, and academic distress by means of the online survey. In addition, we incorporated grades and log data accumulated in the Learning Management System (LMS) as objective learning indicators of academic achievement and engagement in online learning. Results revealed that two dimensions of mental health (i.e., mental well-being and mental disorder) were independently associated with all objective and subjective online learning indicators. Specifically, languishing (i.e., low levels of mental well-being) was negatively associated with student engagement derived from LMS log data and academic achievement and was positively associated with self-reported academic distress even after we controlled for the effects of mental disorder. In addition, mental disorder was negatively related to student engagement and academic achievement and was positively related to academic distress even after we controlled for the effects of mental well-being. These results remained notable even when we controlled for the effects of sociodemographic variables. Our findings imply that applying the Dual-Continua Model contributes to a better understanding of the relationship between college students mental health and their adaptation to online learning. We suggest that it is imperative to implement university-wide interventions that promote mental well-being and alleviate psychological symptoms for students successful adjustment to online learning.

The reverse correlation (RC) method has been widely used, because it allows visualization of mental representations without a priori assumptions about relevant dimensions. We employed the RC method to visualize mental representations of self and examined their relationships with traits related to self-image. For this purpose, 110 participants (70 women) performed a two-image forced choice RC task to generate a classification image of self (self-CI). Participants perceived their self-CIs as bearing a stronger resemblance to themselves than did CIs of others (filler-CIs). Valence ratings of participants who performed the RC task (RC sample) and of 30 independent raters both showed positive correlations with self-esteem, explicit self-evaluation, and extraversion. Moreover, valence ratings of independent raters were negatively correlated with social anxiety symptoms. On the other hand, valence ratings of the RC sample and independent raters were not correlated with depression symptoms, trait anxiety, or social desirability. The results imply that mental representations of self can be properly visualized by using the RC method.

BACKGROUND: As the COVID-19 (Coronavirus disease 2019) pandemic is prolonged, psychological responses to the pandemic have changed, and a new scale to reflect these changes needs to be developed. In this study, we attempt to develop and validate the COVID-19 Impact Scale (CIS) to measure the psychological stress responses of the COVID-19 pandemic, including emotional responses and difficulty with activities of daily living. METHODS: We recruited 2152 participants. Participants completed the CIS, the Fear of COVID-19 Scale (FCV-19S), and other mental health related measures. The factor structure, reliability, and validity of the CIS were analyzed. In addition, the validity of the scale was confirmed by its relationships to the existing measures assessing fear of COVID-19, depression, anxiety, subjective well-being, and suicidal ideation. RESULTS: Using exploratory factor analysis (N1 = 1076), we derived a one-factor structure. In confirmatory factor analysis (N2 = 1076), the one-factor model showed good to excellent fitness. The CIS was positively correlated with depression, anxiety, suicidal ideation, fear of COVID-19 and negatively correlated with subjective well-being. The FCV-19S did not show significant correlations with subjective well-being or suicidal ideation, and FCV-19Ss explanatory powers on depression and anxiety were lower than those of the CIS. CONCLUSIONS: These results support that the CIS is a valid assessment of emotional problems and deterioration of the quality of life caused by the COVID-19 pandemic. Finally, the limitations of this study and future research directions are discussed.

Reverse correlation (RC) method has been recently used to visualize mental representations of self. Previous studies have mainly examined the relationship between psychological aspects measured by self-reports and classification images of self (self-CIs), which are visual proxies of self-image generated through the RC method. In Experiment 1 (N = 118), to extend the validity of self-CIs, we employed social evaluation on top of self-reports as criterion variables and examined the relationship between self-CIs and social evaluation provided by clinical psychologists. Experiment 1 revealed that the valence ratings of self-CIs evaluated by independent raters predicted social evaluation after controlling for the effects of self-reported self-esteem and extraversion. Furthermore, in Experiment 2 (N = 127), we examined whether a computational scoring method - a method to assess self-CIs without employing independent raters - could be applied to evaluate the valence of participants self-CIs. Experiment 2 found that the computational scores of self-CIs were comparable to independent valence ratings of self-CIs. We provide evidence that self-CIs can add independent information to self-reports in predicting social evaluation. We also suggest that the computational scoring method can complement the independent rating process of self-CIs. Overall, our findings reveal that self-CIs are a valid and useful tool to examine self-image more profoundly.

Inclusion of the effects of vegetation feedback in a global climate change simulation suggests that the vegetation–climate feedback works to alleviate partially the summer surface warming and the associated heat waves over Europe induced by the increase in atmospheric CO2 concentrations. The projected warming of 4°C over most of Europe with static vegetation has been reduced by 1°C as the dynamic vegetation feedback effects are included.. Examination of the simulated surface energy fluxes suggests that additional greening in the presence of vegetation feedback effects enhances evapotranspiration and precipitation, thereby limiting the warming, particularly in the daily maximum temperature. The greening also tends to reduce the frequency and duration of heat waves. Results in this study strongly suggest that the inclusion of vegetation feedback within climate models is a crucial factor for improving the projection of warm season temperatures and heat waves over Europe.