AU - Karami, Zeinab AU - Moradi, Omid TI - Predict psychological vulnerability based on the pathological personality traits and uncertainty intolerance of COVID-19 ward nurses PT - JOURNAL ARTICLE TA - Shenakht JN - Shenakht VO - 9 VI - 4 IP - 4 4099 - http://shenakht.muk.ac.ir/article-1-1408-en.html 4100 - http://shenakht.muk.ac.ir/article-1-1408-en.pdf SO - Shenakht 4 ABĀ  - Introduction: The coronavirus disease (COVID-19) outbreak has challenged the global health system and put much psychological pressure on nurses who are responsible for providing primary health services. Aim: The present study aimed to predict psychological vulnerability based on the pathological personality traits and uncertainty intolerance of COVID-19 ward nurses. Method: The present research was a descriptive correlational study. The statistical population consisted of 153 nurses working in the COVID-19 wards in Tohid, Kowsar, and Be’sat hospitals in Sanandaj, Kurdistan, Iran, within winter 2020 to spring 2021 using convenience sampling. The Psychological Vulnerability Scale, Pathological Personality Traits Scale and Intolerance of Uncertainty Scale were used to collect the data. Pearson correlation and regression analysis were used to analyze the data using SPSS-22 software. Results: The results of correlation analysis showed that the psychological vulnerability variable was significantly associated with pathological personality traits and uncertainty intolerance in nurses (P<0.01). Additionally, based on the results of regression analysis, 59% of the variance of the psychological vulnerability variable was predictable with the help of predictor variables (i.e., pathological personality traits and uncertainty intolerance) (P<0.01). Conclusion: Pathological personality traits and uncertainty intolerance play an essential role in the psychological vulnerability of nurses during COVID-19. It is recommended to provide nurses with necessary psychological training to strengthen their positive emotions and improve uncertainty tolerance. CP - IRAN IN - LG - eng PB - Shenakht PG - 16 PT - Research YR - 2022