Differences in personality related determinants of empathetic sensibility in female and male students of medicine

The issues of personality and its relations with the level of empathetic sensibility of medical doctors are broadly discussed in the literature. The aim of this study was an assessment of personality related predictors of empathy indicators in female and male students of medicine with consideration of gender differences. Methods applied were Empathic Sensitiveness Scale (ESS) and Personality Inventory (NEO-PI-R). The study included 153 participants, who were students of the fifth year of medical studies. Students filled in questionnaires during workshops in clinical psychological skills. Participation in the study was voluntary and anonymous. The statistical analysis was performed using Statistica 13 PL and PS IMAGO PRO (SPSS). Linear regression analysis with the interaction component was performed to explore the relationship between personality factors and gender and their interaction with the variable dependent level of empathy. The analysis showed that Extraversion, Openness and Agreeableness are associated with the level of Empathic Concern. Neuroticism, Extraversion, Agreeableness and Conscientiousness are associated with the level of Personal Distress. Extraversion, Openness, Agreeableness and Conscientiousness are associated with the level of Perspective-taking. The regression analysis with the interactive component showed that there is no relationship between gender and the level of empathy, therefore the interactions were insignificant. Empathetic sensibility is related to personality dimensions of the students of medicine. Although there has been no interaction among chief personality dimensions, empathy indicators and gender, detailed analysis of personality dimensions’ components has shown differences between men and women.

Impact of covid-19 pandemic: Perception of clinical year medical students on transition of traditional to online lectures in a medical university of Karachi, Pakistan.

Objective: To determine the perception of clinical undergraduate MBBS students of online lectures in a medical university of Karachi, Pakistan. Study Design: Descriptive Study. Setting: Bahria University Medical and Dental College. Period: May 2020 till July 2020. Material & Methods: Four weeks after introduction of online lectures. Data was collected from clinical year students over two weeks using Google forms and analysed using SPSS version 22. Result: Of 450 clinical year students, n=234 responded. Overall, 48.7% (n = 114) students were satisfied with the online lectures, 34.2 %( n= 80) were completely satisfied and 17.7 %( n=39) were unsatisfied. 53.4 %( n=125) felt lectures were serving the purpose whereas 37.2% (n=87) do not feel the same. The lack of interaction component in online lectures was felt by 45.7% (n=107) students. Regarding clinical teaching, 72.2% (n= 170) do not think it is possible online, 15.8 (n=35) feel it possible and 12.4% (n=29) were hopeful. Majority 61.1% (n=143) think it is impossible to complete medical studies online. Student ideas for improvement included availability of lecture recording for later viewing, integrated quizzes, increasing interactive component, training of faculty, small group sessions and case based teaching. Majorly students faced internet connectivity issues and timings of long lectures without break. Conclusion: Online lectures can be improved by reducing the issues faced by students, providing them easy internet access, faculty training programs to make interactive and case based presentation and quizzes.

Test-Retest Reliability of Binaural Interaction Component (BIC) Using Speech and Non-Speech Evoked ABR

Background and Objective: Binaural hearing serves as an advantage in daily communication by facilitating better localization of sounds and perception of speech in the presence of noise. BIC of ABR has been used to understand the binaural representation of different stimuli, such as transient clicks, and complex signals, such as speech. The present study aimed to investigate the test-retest reliability of the binaural interaction component for click and speech evoked ABR. Methods: 30 individuals with normal hearing served as participants for the present study. ABR for click and speech stimuli (/da/) were recorded from these participants in monaural and binaural conditions. BIC was calculated using the formula: BIC = (L + R)- BI where, L + R is the sum of the left and right evoked potentials obtained with monaural stimulation, and BI is the response acquired from binaural stimulation. To investigate reliability, all the participants underwent three recording sessions. Session 1 and session 2 (intra-session) were carried out on the same day, separately. Whereas, session 3 (inter-session) was carried out after a minimum gap of 3 - 5 days after the first session. Intraclass correlation was used to investigate the test-retest reliability of click and speech evoked BIC across the three sessions. Results: The test-retest reliability for BICclick was found to be excellent for latency measures and fair to good for amplitude measures. BICspeech was found to be fair to good, except for BIC-3. Conclusion: The results of the present study indicate that the reliability of BICclick is better than that of BICspeech. These results suggest that the clinical utility of BICspeech should be exerted with caution.

Deep Feedback Network for Recommendation

Both explicit and implicit feedbacks can reflect user opinions on items, which are essential for learning user preferences in recommendation. However, most current recommendation algorithms merely focus on implicit positive feedbacks (e.g., click), ignoring other informative user behaviors. In this paper, we aim to jointly consider explicit/implicit and positive/negative feedbacks to learn user unbiased preferences for recommendation. Specifically, we propose a novel Deep feedback network (DFN) modeling click, unclick and dislike behaviors. DFN has an internal feedback interaction component that captures fine-grained interactions between individual behaviors, and an external feedback interaction component that uses precise but relatively rare feedbacks (click/dislike) to extract useful information from rich but noisy feedbacks (unclick). In experiments, we conduct both offline and online evaluations on a real-world recommendation system WeChat Top Stories used by millions of users. The significant improvements verify the effectiveness and robustness of DFN. The source code is in https://github.com/qqxiaochongqq/DFN.

Fine-Grained Action Recognition by Motion Saliency and Mid-Level Patches

Effective extraction of human body parts and operated objects participating in action is the key issue of fine-grained action recognition. However, most of the existing methods require intensive manual annotation to train the detectors of these interaction components. In this paper, we represent videos by mid-level patches to avoid the manual annotation, where each patch corresponds to an action-related interaction component. In order to capture mid-level patches more exactly and rapidly, candidate motion regions are extracted by motion saliency. Firstly, the motion regions containing interaction components are segmented by a threshold adaptively calculated according to the saliency histogram of the motion saliency map. Secondly, we introduce a mid-level patch mining algorithm for interaction component detection, with object proposal generation and mid-level patch detection. The object proposal generation algorithm is used to obtain multi-granularity object proposals inspired by the idea of the Huffman algorithm. Based on these object proposals, the mid-level patch detectors are trained by K-means clustering and SVM. Finally, we build a fine-grained action recognition model using a graph structure to describe relationships between the mid-level patches. To recognize actions, the proposed model calculates the appearance and motion features of mid-level patches and the binary motion cooperation relationships between adjacent patches in the graph. Extensive experiments on the MPII cooking database demonstrate that the proposed method gains better results on fine-grained action recognition.