Objective: The coronavirus 2019 (COVID -19) pandemic had great psychological impact on COVID-19 patients and their families. Relatives of the deceased COVID-19 patients are at risk for complicated grief. Healthcare providers (HCPs) should be able to identify complicated grief cases. The aim of this study was to assess HCP knowledge regarding complicated grief during the COVID-19 pandemic. Method: This cross-sectional study was conducted using an online researcher-made questionnaire. The questionnaire was designed and validated before being used in this study. The questionnaire included demographic questions as well as knowledge about complicated grief and its symptoms, risk factors and management. The link to the questionnaire website was sent to HCP governmental and private sectors. Data was analyzed using the ordinal regression model by the SPSS 16 software. Results: A total of 887 HCPs (69% female and 31% male) participated in this study. Majority of the participants (594, 70%) had fair overall knowledge about complicated grief while 206 (23.2%) participants had poor knowledge. Poor knowledge level about risk factors for complicated grief was observed in 44.3% of the participants. Fair or poor knowledge about prevention and management of complicate grief was observed in 39.2% of participants. Knowledge about complicated grief had a significant positive relationship with female gender (OR: 1.55; 95% CI: 1.15-2.08) and higher education level (OR: 1.86; 95% CI: 1.37-2.54). Conclusion: Knowledge of HCPs about complicated grief was low. There is need for HCP knowledge improvement regarding complicated grief by appropriate education.

Background: Bipolar disorder is considered a psychiatric disease without any effective screening questionnaire to monitor and manage Iranian patients. This study aims to implement a researcher-made questionnaire in the form of educational interactive software for better management of patients with bipolar disorder and prevent further complications. Methods: The present cross-sectional study evaluated the efficacy of psychoeducational-interactive-therapeutic software for patients with bipolar disorder, which is a network-based software providing a researcher-made questionnaire in a planned manner. This software can predict the occurrence of future bipolar episodes for each patient by using artificial intelligence algorithms after the occurrence of two mood episodes as the training phase. The patients with bipolar disorder were asked to use the software for a year and their mood episodes were compared before and after using the software. We evaluate the reliability of the questionnaires in the software with internal consistency using alpha Cronbach test and test-retest analysis. Face validity and content validity were also evaluated. Results: The content validity index of the instrument was 93%, and the Cronbach's alpha coefficient of the whole questionnaire was 0.955. Also, the ICC coefficient for this questionnaire is above 0.70, and the correlation coefficient of the answers in all constructs of the questionnaire is more than 0.8. Thirty male patients with bipolar disorder who experienced four episodes of mood swings per year experienced an average of 2 mood episodes per year following the use of this software. Conclusion: Our Psychoeducational-interactive-therapeutic software is the first Persian language software based on artificial intelligence to monitor clinical symptoms in patients with bipolar disorder, which uses a standard questionnaire to predict the incidence of episodes of depression and mania in these patients.

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