Background: The coronavirus disease 2019 (COVID-19) crisis has greatly impressed medical education by shifting traditional educational methods to e-learning. Objectives: This study evaluated the undergraduate medical students' attitudes toward e-learning during the COVID-19 pandemic Methods: This cross-sectional study included undergraduate medical students of Mashhad University of Medical Sciences, Mashhad, Iran, in the academic year 2020 - 21 by census sampling method, whose attitude toward e-learning was evaluated based on the Ghanizadeh et al. scale. Categorical variables were demonstrated with frequency and percentage, and quantitative variables were described using the mean and standard deviation. An independent-sample t test was run to study the hypothesis. Analysis of covariance (ANCOVA) was performed to compare pre-clinical and clinical groups' attitudes toward e-learning after gender control. Statistical analyses were performed by SPSS 23. Results: The study enrolled 528 undergraduate medical students. The findings indicated that 85.4% of the students agreed with the necessity of more effective e-learning in medical education, and 95.5% believed that e-learning should play a complementary role in medical education. It was found that clinical students had a marginally statistically significantly better attitude toward e-learning than pre-clinical students (t = -2.04, df = 526, P = 0.041). Nevertheless, no significant difference was observed between the two groups after gender control (t = 2.87, P = 0.091). It was shown that males had more positive attitudes toward e-learning than females (t = 2.28, df = 526, P = 0.023). Conclusions: The results revealed acceptable attitudes toward e-learning. Although many students declared e-learning's usefulness and confirmed its complementary role in medical education, some announced that it could not replace in-person training.

Background and Aim: Metaphor is a systematic relationship between two conceptual domains. In metaphor; an experimental or sensual domain called source domain, is related to another domain as target domain. The purpose of this study was to review understanding of metaphorical time pattern in the medical and paramedical students based on gender, age and academic status. Materials and Methods: This descriptive study included 120 medical and paramedical students of Tehran medical university with minimum undergraduate university degree. Time metaphorical pattern questionnaire which had been designed by Raiisi (2019) on the basis of time metaphor corpus analysis included 3 subscales of object, place and matter. Pearson correlation test, T test, analysis of one-way variance and post hoc test (least significant difference) were used for data analysis. Results: The results showed that there was no difference between gender and understanding of metaphorical time pattern. Higher educational levels (from undergraduate to postgraduate and specialty courses) led to better understanding of metaphorical time pattern in Persian speaker students. Students of higher age showed improved understanding of metaphorical time pattern in relation to the object subclass. Conclusion: Perception of the time is not dependent on gender but can be improved with increasing age and higher academic levels.

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Anger is defined as a psychobiological emotional state that consists of feelings varying in intensity from mild irritation or annoyance to intense fury and rage. Dysfunction in anger regulation is marker of most psychiatric disorders. The most important point about anger regulation by the individuals is how to express anger and control it. The purpose of the present study is to predict the anger expression from the anger experience in individuals with psychiatric disorder for assessment of how to express and control the anger. To this end, the number of 3,000 subjects of individuals with clinical disorders had filled in the State-Trait Anger Expression Inventory–II (STAXI–II). After removing the uncertain diagnoses (900 subjects), the number of 2,100 data was considered in the analysis. Then, the computational codes based on three soft computing algorithms, including Radial Basis Function (RBF), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Decision Tree (DT) were developed to predict the scales of anger expression of the individuals with psychiatric disorders. The scales of anger experience were used as input data of the developed computational codes. Comparison between the results obtained from the DT, RBF and ANFIS algorithms show that all the developed soft computing algorithms forecast the anger expression scales with an acceptable accuracy. However, the accuracy of the DT algorithm is better than the other algorithms.