Objectives: To explore psychological problems (Anxiety, Depression and Stress) in general population during Covid-19 pandemic. To find predictive effects of cognitive emotion regulation on psychological problems. Methodology: Convenient sampling technique was used to obtain the sample of 500 participants (Male = 239, Female = 261). Research instrument consists of four parts. First part comprised of consent form, second part was about demographic profile, third part was Depression, Anxiety and Stress scale (DASS-21) while Cognitive Emotion Regulation Questionnaire was the last part of the instrument. Results: SPSS 23.0 (Statistical Package for Social Sciences) version was used for study analysis. Descriptive statistics used to summarize the raw data. The inferential statistics such as regression, correlation and t-test were used to calculate the findings according to research objectives. Results indicated that 33%, 40% and 27% individuals were experiencing Depression, Anxiety and Stress respectively during Covid-19 pandemic. Among these participants, 48% ( N = 242) were experiencing normal level of all these targeted psychological problems while remaining 52% ( N = 258) respondents have mild to very severe level of all these disorders. Furthermore, findings of linear regression analysis illustrated that cognitive emotion regulation significantly predicts psychological problems [ R 2 =.216; F = 51.223, p < .01] and 21% variation in psychological problems is due to cognitive emotion regulation. Conclusion: This study recommended that policy makers must develop and implement some necessary programmes to prevent and cure people from devastating psychological and mental health consequences of covid-19 on priority basis.

Objectives: To determine the predictive association between fear of COVID-19 and emotional distress (depression, anxiety, and stress) in frontline and non-frontline nurses. To explore the mediating role of socio-demographic features.Methods: Correlational cross-sectional research design was implied. A total of 500 on-duty male and female, frontline and non-frontline, nurses were included from five major hospitals in Gujrat (Aziz Bhatti Shaheed Hospital, City Hospital, Doctors Hospital, Akram Hospital, and Gujrat Hospital). Fear of COVID-19 scale and the Urdu version of depression, anxiety, and stress scale - 21 (DASS-21) were used to measure variables of interest. Descriptive statistics, structural equation modeling (SEM), linear regression, and t-test were carried out using Statistical Package for Social Sciences (SPSS) 21.Result: Structural equation modeling (SEM) revealed a significant predictive link between fear of COVID-19 and depression, anxiety, and stress (goodness of model fit; NFI = 0.93, GFI = 0.914, AGFI = 0.93, CFI = 0.936, and IFI = 0.936). Furthermore, a significant mediating effect of certain demographic features was discovered by SEM (CMIN/DF = 1.11, NFI = 0.94, TLI = 0.98, GFI = 0.08, AGFI = 0.93, RMSEA = 0.029, CFI = 0.99, and IFI = 0.99). Results of linear regression analysis also revealed a momentous predictive association between fear of COVID-19 and emotional distress (R = 0.860). In comparative analysis, the results of t-test explored the statistical significant difference in fear of COVID-19 and emotional distress between frontline (mean = 25.775, 36.147 and SD = 1.75, 2.23) and non-frontline nurses (mean = 21.702, 27.353 and SD = 4.607, 10.212), with t(130) =7.111, 6.92.Conclusion: Managing the mediating effect of demographic characteristics and reducing the fear of COVID-19 can help nurses to overcome emotional distress, such as depression, anxiety, and stress. Further, this will increase the productivity among nurses.

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The objective of current study was two-folds. First objective was to identify mental health problems (Anxiety, Depression and Stress) in flood effected people in Punjab, Pakistan. Second objective was to explore predictive effects of social support on mental health problems. Convenient sampling method was used to find the sample of 600 participants (Male = 40%, Female = 60%). Study tool covers four parts. First part included an agreement form, second part was about demographic outline, third part was Depression, Anxiety and Stress scale (DASS-21) while Perceived Social Support Scale was the last part of the research tool. Data was analysed using SPSS 23.0 version (Statistical Package for Social Sciences). Descriptive statistics used to abridge the raw data. The inferential statistics such as regression, correlation and t-test were used to determine the outcomes according to study aim. Results specified that 38%, 20% and 43% individuals were experiencing Depression, Anxiety and Stress respectively due to flood distaster. Furthermore, findings of linear regression analysis demonstrated that social support significantly predicts mental health problems [R²=.716; F = 61.223, p < .01] and 71% variation in mental health problems is due to the social support. This study suggested that policy manufacturers must establish and implement some essential platforms, support packeges and mental health interventions to prevent and cure people from overwhelming psychological and mental health costs of flood distruction on priority basis.

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