Senior Vs. Junior Urology Trainees: Factors Associated with Burnout

INTRODUCTION AND OBJECTIVE: Burnout is one of the many challenges physicians may face in their careers. While there are studies focusing on burnout among surgical specialties, there are limited studies focusing on burn out at different levels of training. The aim of this study is to measure and compare rates of burnout between junior and senior trainees in urological surgery as well as to identify changes in training that could be made to improve work-life balance. METHOD(S): To assess professional burnout, respondents completed the 22-item Maslach Burnout Inventory Human Services Survey as part of the 2019 AUA census. Respondents were categorized into junior trainees (PGY1, PGY2, PGY3) and senior trainees (PGY4, PGY5, and fellow). RESULT(S): A total of 512 respondents were included. Senior trainees represented 57% while junior trainees represented 43% of the total cohort. The prevalence of burnout (p=0.005) and high levels of depersonalization (p=0.018) were 50% in junior trainees and 38% in senior trainees. High levels of emotional exhaustion were 9% for junior and 5% for senior trainees (p=0.053) (Table 1). The top changes to improve work life balance for urology trainees were access to meal plans, on-call rooms, and time to attend health appointments (Figure 1). CONCLUSION(S): Juniors experience higher levels of burnout, depersonalization, and emotional exhaustion when compared to seniors. Providing meal plans, on-call quarters, and time to attend health appointments are the top changes to improve work-life balance among urology trainees. These changes may have a dramatic impact on the well-being of our trainees, especially in this post-COVID era. (Figure Presented).

A topic modeling analysis for Korean online newspapers: Focusing on the social perceptions of nurses during the COVID-19 epidemic period

Purpose: This study explored the meaning of the social perceptions of nurses in online news articles during the coronavirus disease 2019 (COVID-19) pandemic. Methods: A total of 339 nurse-related articles published in Korean online newspapers from January 1 to December 31, 2020, were extracted by entering various combinations of OR and AND with the four words "Corona,” "COVID,” "Nursing,” and "Nurse” as search keywords using BIGKinds, a news database provided by the Korea Press Foundation. The collected data were analyzed with a keyword network analysis and topic modeling using NetMiner 4. Results: The top keywords extracted from the nurse-related news articles were, in the following order, "metropolitan area,” "protective clothing,” "government,” "task,” and "admission.” Four topics representing keywords were identified: "encouragement for dedicated nurses,” "poor work environment,” "front-line nurses working with obligation during the COVID-19 pandemic,” and "nurses' efforts to prevent the spread of COVID-19.” Conclusion: The media's attention to the dedication of nurses, the shortage of nursing resources, and the need for government support is encouraging in that it forms the public opinion necessary to lead to substantial improvements in treating nurses. The nursing community should actively promote policy proposals to improve treatment toward nurses by utilizing the net function of the media and proactively seek and apply strategies to improve the image of nurses working in various fields. Copyright © 2022 The Korean Academic Society of Nursing Education.

Differences in Perceptions of Naturalness among Urban Park User Groups in Seoul

Though the activities in urban facilities have declined during the COVID-19, the demand for visiting open spaces and parks has increased. Visitors to city parks also increased in Seoul. People have realized that nature is an important space for safety, health, and leisure in their everyday life. This study implied that people intended to visit city parks as a natural space in the urban area. So, an assessment criterion of the quality of environments of city parks, naturalness is selected as one of the indicators. This study chose six parks as a study subject in Seoul and found differences for users in the perception of naturalness in city parks. Q methodology is a useful tool to identify differences between individual's perceptions of nature. Through the Q analysis, six groups have differences in the perception and the valuation of naturalness. The study found that differences have resulted from their past experiences, personal preferences, and psychological status. The first group can be named a dynamic type who enjoys various landscapes, and the second is the group of people who love to meditate in serene woodlands. The third group wants to observe the animals and consider the ecosystem as important, and the fourth group is those who love to feel the changing seasons with sensitivity. The fifth group recognizes nature through the contrast between the building and the natural environment in the city. The last group is those who pursue conservation for old trees or the existing nature environment itself. As a general, appraisal of perceived naturalness is subjective and individual. The perception of naturalness influences landscape preferences and attitudes to city parks. Therefore, the naturalness perceived by users should be a crucial consideration to maximize park services so that users can appreciate various tastes © This open access article is published under a Creative Commons [Attribution-NonCommercial-NoDerivatives 4.0 International] license. https://creativecommons.org/licenses/by-nc-nd/4.0/

An IoMT-Based Federated and Deep Transfer Learning Approach to the Detection of Diverse Chest Diseases Using Chest X-Rays

Since chest illnesses are so frequent these days, it is critical to identify and diagnose them effectively. As such, this study proposes a model designed to accurately predict chest disorders by analyzing multiple chest x-ray pictures obtained from a dataset, consisting of 112,120 chest X-ray images, obtained the National Institute of Health (NIH) X-ray. The study used photos from 30,805 individuals with a total of 14 different types of chest disorder, including atelectasis, consolidation, infiltration, and pneumothorax, as well as a class called "No findings" for cases in which the ailment was undiagnosed. Six distinct transfer-learning approaches, namely, VGG-16, MobileNet V2, ResNet-50, DenseNet-161, Inception V3, and VGG-19, were used in the deep learning and federated learning environment to predict the accuracy rate of detecting chest disorders. The VGG-16 model showed the best accuracy at 0.81, with a recall rate of 0.90. As a result, the Fl score of VGG-16 is 0.85, which was higher than the Fl scores computed by other transfer learning approaches. VGG-19 obtained a maximum rate of accuracy of 97.71% via federated transfer learning. According to the classification report, the VGG-16 model is the best transfer-learning model for correctly detecting chest illness.

An Agent-Based Simulation Model to Evaluate Contacts, Layout, and Policies in Entrance, Exit, and Seating in Indoor Activities Under a Pandemic Situation

The outbreak of the novel coronavirus SARS-CoV2 has dramatically changed the world and has been a severe health threat in 2020 and 2021. In this article, an agent-based simulation model of pedestrian dynamics is proposed for classroom-type indoor spaces (e.g., classroom, auditorium, food court, and meeting room), which will help organizations such as universities to evaluate alternative policies (namely entrance and exit policy, seating policy, and room layout) concerning the contact-caused risk associated with activities in such places during a pandemic situation. In particular, the proposed work focuses on solving the indoor seat allocation and traffic movement problem while practicing appropriate physical distancing measures. The proposed seating policy evaluates the distance of a seat from the doors and pathways facilitating the evaluation of contact-caused risk associated with the pathway and indoor area movement. Various statistics from two perspectives, risk, and logistics, are reported in the simulation results. The risk metrics used in evaluating different policies include average exposure duration and an average number of contacts with others. To develop a highly realistic crowd simulation considering physical distancing and human intervention nature, deadlock detection and resolution mechanisms are incorporated. From this study, it has been observed that the proposed social distancing (SD) seating policy and zonal exit policy can significantly reduce the contact number and exposure duration at a higher occupancy level. The proposed work helps the organizational policymakers to evaluate different policies and ensure the safe operation of the organizations under pandemic situations. IEEE

Immune signatures underlying post-acute COVID-19 lung sequelae.

Severe coronavirus disease 2019 (COVID-19) pneumonia survivors often exhibit long-term pulmonary sequelae, but the underlying mechanisms or associated local and systemic immune correlates are not known. Here, we have performed high-dimensional characterization of the pathophysiological and immune traits of aged COVID-19 convalescents, and correlated the local and systemic immune profiles with pulmonary function and lung imaging. We found that chronic lung impairment was accompanied by persistent respiratory immune alterations. We showed that functional severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)­specific memory T and B cells were enriched at the site of infection compared with those of blood. Detailed evaluation of the lung immune compartment revealed that dysregulated respiratory CD8+ T cell responses were associated with the impaired lung function after acute COVID-19. Single-cell transcriptomic analysis identified the potential pathogenic subsets of respiratory CD8+ T cells contributing to persistent tissue conditions after COVID-19. Our results have revealed pathophysiological and immune traits that may support the development of lung sequelae after SARS-CoV-2 pneumonia in older individuals, with implications for the treatment of chronic COVID-19 symptoms.

Dash-safe: A multi-modal geographic information system (GIS) dashboard for near real-time mapping of perceived covid risk/safety in a campus environment

Objective: To provide near-real-time information for risk assessment, risk management and stress reduction during COVID campus re-entry. Methods: The DASH-SAFE dashboard (DASHboard-Stress At-Risk Facilities Environment), shows safer and riskier areas for COVID-re-entry to the University of Arizona (UArizona) campus by overlaying inputs from multiple surveillance technologies onto an interactive GIS campus map, including: survey of perceived feelings of safety/risk related to observed health behaviors (face coverings, physical distancing);predictive modeling of people's movement through campus;building coding related to viral spread risk (occupancy/ density, ventilation rate). Users also rated their stress level and DASH-SAFE's stress reducing effect. Survey responses appear as color-coded points on the map. A pilot study using QR code prompt posters, user observations, user interviews, and a qualitative survey to explore user expectations and assess ease of use was carried out at UArizona Libraries. Predictive modeling for movement through campus was based on class schedules and known mealtimes. Results: Beta testing through a campus-wide e-mailing garnered 67 responses. 61% noted areas of safety;9% areas of distancing risk;7.5% areas of masking risk;58% noted low stress. 3 of 6 respondents who scored the tool's stress reduction effect rated it 4 stars and 2 as 5 stars. 21 respondents in the UArizona Libraries pilot study provided data that improved understanding of how they perceive safety in addition to masking and distancing space, e.g., monitoring and hand sanitizers, and informed improvements for ease of use (e.g. simplify instructions, improve location instructions, specify COVID-related stress reduction). Predictive modeling revealed anticipated pinch points and maximal building occupancy over time. Conclusion: DASH-SAFE can help users assess and avoid areas of risk and help reduce their stress. Beta and pilot testing data will be used to modify and improve the tool and a navigation and alarm tool will be added in subsequent versions.