Back

Correlates of Psychological Distress among University Students with a special reference to Gender Differences

Pervaiz, R.; Malik, T. A.

2025-09-12 psychiatry and clinical psychology
10.1101/2025.09.09.25334787 medRxiv
Show abstract

AimThe study examined the relationship between impostor phenomenon (IP) and distress (PD) as well as and the protective impact of psychological capital (PsyCap) with focus on gender differences. It further investigated whether psychological capital serves as a protective factor against distress among students. Subject and MethodsTo conduct cross-sectional survey, 323 participants (74% females; 18-28 years; undergraduate and postgraduate) were recruited from universities in Islamabad, Pakistan. They completed the Clance Impostor Phenomenon Scale (CIPS), Depression, Anxiety and Stress Scale (DASS-21) and the Psychological Capital Questionnaire (PCQ-12). The collected data was analyzed using correlation and regression analysis stratified by gender. ResultsIn the current sample, females reported higher levels of IP and PD as compared to males, whereas PsyCap did not differ by gender. IP and PD showed positive association (r=.61) whereas PsyCap showed a modest negative correlation with PD (r=.28). Regression analysis found IP to be significant predictor of severe PD only among female students, even after controlling for covariates. PsyCap and PD were correlated but the former did not independently predict the latter except for female students. ConclusionThe results indicate that IP is an important risk factor for distress among Pakistani female postgraduate students, thus highlighting the need for targeted interventions. Universities should focus on psycho-education on IP feelings and introducing resilience building programs with special focus on females students more vulnerable to distress.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.