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Face masks slow the spread of SARS-CoV-2, but it has been unknown how masks might reshape social interaction. One important possibility is that masks may influence how individuals communicate emotion through facial expressions. Here, we clarify to what extent-and how-masks influence facial emotion communication, through drift-diffusion modeling (DDM). Over two independent pre-registered studies, conducted three and 6 months into the COVID-19 pandemic, online participants judged expressions of 6 emotions (anger, disgust, fear, happiness, sadness, surprise) with the lower or upper face "masked" or unmasked. Participants in Study 1 (N = 228) correctly identified expressions above chance with lower face masks. However, they were less likely-and slower-to correctly identify these expressions relative to without masks, and they accumulated evidence for emotion more slowly-via decreased drift rate in DDM. This pattern replicated and intensified 3 months later in Study 2 (N = 264). These findings highlight how effectively individuals still communicate with masks, but also explain why they can experience difficulties communicating when masked. By revealing evidence accumulation as the underlying mechanism, this work suggests that time-sensitive situations may risk miscommunication with masks. This research could inform critical interventions to promote continued mask wearing as needed.
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COVID-19 , Masks , Humans , Pandemics , Facial Expression , Judgment , SARS-CoV-2 , EmotionsABSTRACT
Connectivism is a learning theory first proposed by Siemens and Downes (2005, 2009) as a learning theory for the digital age. This theory proposes that learning may occur external to the learner and teacher;it exists in the multiple complex networks with which the learner associates. In 2020, Corbett and Spinello followed up on Siemen's work to move connectivism from a learning theory to a leadership theory. Connectivism is a leadership theory that specifically works in modern churches. This theory emphasizes the connectedness of all knowledge and the mutuality of those connections, becoming a tool used to build better teams in the local church, especially in this dispersed environment.
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Introduction: Virtual interviewing for cardiology fellowship was instituted in the 2021 fellowship application cycle due to the COVID-19 pandemic and restricted travel. It is unknown what impact this has had on geographic patterns of fellow-training program matching. Our aim was to determine if there was a significant difference in the geographic placement in the cardiology fellowship match based on virtual interviews compared to prior years with in-person interviewing. Method(s): All U.S. based accredited cardiovascular disease fellowship programs that participated in the 2019-2021 fellowship match cycles were identified. Those that had publicly available data regarding fellow residency training and training year were included. Each fellow was categorized based on their fellowship program and residency training as from the same program, from the same state, from the same region or from a different region. These were mutually exclusive categories. Result(s): Of the 236 eligible programs, 118 programs (50%) had available data for the 2019-2021 application cycles. Cumulatively n=1787 total fellows were identified. Compared to the pre-virtual cohort (n=1178 matched fellows) there was no difference in the geographic placement during the 2021 virtual cycle (n=609 matched fellows) including the proportion that matched at the same program (30.6% vs 31.5%, p=0.738), in the same state (13% vs 13.8%, p=0.719), same region (24.2% vs 19.7%, p=0.164), or different region (35% vs 33.1%, p=0.274). There was also no difference when stratified by small, medium, or large program size or geographic region. Conclusion(s): The use of virtual interviewing in the 2021 cardiology fellowship application cycle did not result in a significant difference in the geographic placement of fellows. Further study is needed to evaluate the impact of virtual interviewing and optimize its use in fellowship recruitment.
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SETTING AND PARTICIPANTS: Design: Cross-sectional. Setting/ participants: US graduating medical students. Inclusion: US allopathic medical schools with publicly available match data of individual students, 2018-2021. Exclusions: Students matching into preliminary, transitional, combined, or non-residency positions. DESCRIPTION: The COVID-19 pandemic disrupted the 2021 residency match with potential for significant geographic impact as students were unable to attend away rotations or in-person interviews. EVALUATION: Data sources: Association of American Medical Colleges (AAMC;2020), US News and World Report (USWNR) rankings (2020), and official university websites (2018-2021). The unit of analysis was each medical student. Analysis: We used the US Census Region and Division of medical schools and residency programs to calculate the match distance from medical school to residency. 'Match space' was defined by whether an applicant matched at their: home school, home state, adjacent state, same census division, adjacent census division, or skipped 1 or more census division. We defined competitive specialty with a composite variable comprising of US senior fill rate, number of positions, and salary. Ordinal logistic regression examined match distance (dependent outcome) with school, specialty characteristics, and pre/post COVID. Results: 34,672 students representing 66 medical schools from 28 states matched into 26 specialties in 50 states and Canada. 59% of students were from public institutions, and 27% of schools ranked in the top 40 for research. The mean percentage of in-state students by school was 60.3% (range 3- 100%). Match space was lower after the pandemic (adjusted odds ratio [OR] 0.94, 95% CI 0.90 - 0.98;p=0.006), from schools with higher percentage of in-state matriculants (OR 0.74, 95% CI 0.72-0.76), from top NIH-funded institutions (OR 0.88, 95% CI 0.85-0.92), from the Northeast (OR 0.71, 95% CI 0.67-0.75;Midwest reference), and the West (OR 0.67, 95% 0.60-0.74). Match space was higher for students graduating from private schools (OR 1.11, 95% CI 1.05- 1.19) or matching into more competitive specialties (OR 1.08, 95% CI 1.02-1.14). DISCUSSION / REFLECTION / LESSONS LEARNED: After the COVID-19 pandemic, students graduating from US allopathic schools matched closer to their home institution. Students attending public schools and schools with more in-state matriculants also matched closer. School reputation, with research rank as a surrogate, showed a higher match distance relative to those outside of the top 40 ranking. Our study adds insight into how geographic match patterns were influenced by school, specialty choice, and the pandemic.
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Technology runs much of modern society’s daily functions due to how efficient, reliable, and easy it is to access and manage content anywhere at any time. This rapid growth has created an emphasis on cybersecurity to ensure data integrity in today’s digital realm and the future to come. Since more industries are relying on technology, cybersecurity is becoming more utilized as the foundation for success for many companies and individuals alike. However, as these new avenues for communication become part of daily life, cyber threats have also become more prevalent. One of these avenues affected includes healthcare telemedicine (Annaswarmy et al. 2020) which during COVID-19 pandemic provides patients with more convenient methods of medical services. To prevent cyber-attacks on these services through social engineering, among several defense techniques, including machine learning (ML), are being researched to mitigate the effects of human error. This paper provides recent social engineering attacks on healthcare systems, devices, and telemedicine services;and highlights the potential of machine learning in defending against social engineering attacks. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Background: Since the outbreak of the coronavirus epidemic, the 'virtual' telemedicine has become a critical substitute for patient-provider interactions. However, virtual encounters often face challenges in care for high-risk patients such as chronic kidney disease (CKD) patients. In this study, we explore the patient's satisfaction and practical effects of a newly established telemedicine program in CKD patients' care during the pandemic. Methods: We established an online CKD patient care program, including triage strategy, medical care delivery, and psychological support, based on a smartphone application. A total of 278 CKD patients were invited, at least 3 months before the pandemic or during the pandemic. A pilot survey interrogating medical and psychologicalconditions was conducted. The feedback to the program and the psychological assessment repeated after one month. Results: Totally, 181 patients showed active responses to the program, with 289 person-time medical consultations occurred during the study. The virtual care program provided a rapid triage, with 17% patients provided a timely referral to in-patient medical encounters. Nearly all patients (97.4%) believed the program was helpful. The number of symptoms (OR 1.309, 95%CI 1.113-1.541;P=0.001) and being enrolled during pandemic (OR 3.939, 95% CI 1.174-13.221;P=0.026) were associated with high stress. After the follow-up, the high-stress CKD group at baseline showed a significant decrease in avoidance score (6.9±4.7 vs. 9.8±1.9, P=0.015). Conclusions: During the pandemic, we established an online telemedicine care program for CKD patients that provides a rapid triage function, effective CKD disease management, and essential psychological support.