Overcoming Burnout and Promoting Wellness in Radiation Oncology: A Report From the ACR Commission on Radiation Oncology.

Burnout, defined by the presence of emotional exhaustion, depersonalization, and decreased sense of personal accomplishment, impacts a significant portion of radiation oncologists. This has been exacerbated by the COVID-19 pandemic, is notably worse for women, and has been identified as an international concern. Key contributors to burnout within radiation oncology include inadequate clinical and administrative support, imbalanced personal and professional lives including time with family and for self-care, decreased job satisfaction secondary to increased electronic medical record and decreased patient time, unsupportive organizational culture, lack of transparency from leadership and inclusion in administrative decisions, emotionally intensive patient interactions, challenges within the radiation oncology workforce, financial security related to productivity-based compensation and increasing medical training-related debt, limited education on wellness, and fear of seeking mental health services due to stigma and potential negative impacts on the trajectory of one's career. Limited data exist to quantify the impacts of these factors on the overall levels of burnout within radiation oncology specifically, and additional efforts are needed to understand and address root causes of burnout within the field. Strategies should focus on improving the systems in which physicians work and providing the necessary skills and resources to thrive in high-stress, high-stakes work environments.

Analysis of the Effects of Lockdown on Staff and Students at Universities in Spain and Colombia Using Natural Language Processing Techniques.

The aim of this study is to analyze the effects of lockdown using natural language processing techniques, particularly sentiment analysis methods applied at large scale. Further, our work searches to analyze the impact of COVID-19 on the university community, jointly on staff and students, and with a multi-country perspective. The main findings of this work show that the most often related words were "family", "anxiety", "house", and "life". Besides this finding, we also have shown that staff have a slightly less negative perception of the consequences of COVID-19 in their daily life. We have used artificial intelligence models such as swivel embedding and a multilayer perceptron as classification algorithms. The performance that was reached in terms of accuracy metrics was 88.8% and 88.5% for students and staff, respectively. The main conclusion of our study is that higher education institutions and policymakers around the world may benefit from these findings while formulating policy recommendations and strategies to support students during this and any future pandemics.

Analysis of the Effects of Lockdown on Staff and Students at Universities in Spain and Colombia Using Natural Language Processing Techniques

The aim of this study is to analyze the effects of lockdown using natural language processing techniques, particularly sentiment analysis methods applied at large scale. Further, our work searches to analyze the impact of COVID-19 on the university community, jointly on staff and students, and with a multi-country perspective. The main findings of this work show that the most often related words were “family”, “anxiety”, “house”, and “life”. Besides this finding, we also have shown that staff have a slightly less negative perception of the consequences of COVID-19 in their daily life. We have used artificial intelligence models such as swivel embedding and a multilayer perceptron as classification algorithms. The performance that was reached in terms of accuracy metrics was 88.8% and 88.5% for students and staff, respectively. The main conclusion of our study is that higher education institutions and policymakers around the world may benefit from these findings while formulating policy recommendations and strategies to support students during this and any future pandemics.