A cross-sectional study on the response abilities of clinical and preventive medical students in public health emergency.

Inconsistent training programs for public health emergency (PHE) have been criticized as a contributing factor in PHE's managerial weak points. In response, to analyze the relevant discrepancies among the medical students in the class of 2021 from Xiangya School of Medicine of Central South University, the present study conducted an online questionnaire survey using convenience sampling. The questionnaire comprised four sections, including the basic information, the subjective cognition in PHE, the rescue knowledge and capabilities of PHE, and the mastery of PHE regulations and psychological intervention abilities. To compare the abovementioned aspects, related data were collected from 235 medical students divided into two groups, namely, clinical medical students (Group A) and preventive medical students (Group B). We found a more positive attitude in PHE (P = 0.014) and a better grasp of the PHE classification (P = 0.027) and the reporting system in group B compared with group A. In addition, even if group B showed the same response capability in communicable diseases as group A, the former had less access to clinical practice, resulting in poorer performance in the noncommunicable diseases during a fire, flood, and traffic accidents (P = 0.002, P = 0.018, P = 0.002). The different emphasis of each training program contributed to the uneven distribution of abilities and cognition. Meanwhile, the lack of an integrated PHE curriculum led to unsystematic expertise. Hence, to optimize the PHE management system, equal attention should be paid to medical students with diverse majors along with a complete integrated PHE curriculum.

The prevalence and risk factors for perceived voice disorders in public school teachers.

Objectives: The aim of this study was to explore the prevalence and risk factors in public kindergarten and elementary school teachers in the Jimei district in Xiamen. We took particular interest in the relationship between work-related factors and voice disorders. Study Design: A cross-sectional investigation; a General Investigation. Methods: This study was conducted from September 14 to 18, 2020 at public kindergarten and elementary schools in Xiamen, China. A total of 3140 teachers were separated into a perceived voice disorder group (PVD) and no perceived voice disorder group (NPVD) according to the Voice Handicap Index. The chi-square test was applied to explore the differences between the PVD and NPVD groups. The univariate logistic regression models were used to identify the risk factors in terms of unadjusted odds ratio and 95% confidence interval. Stepwise logistic regression was then used to ascertain independent determinants. Results: We found that the prevalence of PVD was 47.52%. The results showed that risk factors of PVD included being female (OR = 1.574), middle-rank technical title and higher (OR = 2.199), continuous lecturing for more than 3 classes (OR = 3.034), lectured more than 10 classes a week (OR = 1.436) and taught art or physical education (OR = 1.742). Conclusions: Teachers' work-related characteristics were associated with PVD. This proves that a preventive voice care program for teachers, administered by the school or education bureau, is urgent. This could include components such as the reasonable arrangement of timetables and recruitment of a sufficient number of kindergarten and elementary school teachers.Level of evidence: Case-series.

Computer Audition for Fighting the SARS-CoV-2 Corona Crisis-Introducing the Multitask Speech Corpus for COVID-19.

Computer audition (CA) has experienced a fast development in the past decades by leveraging advanced signal processing and machine learning techniques. In particular, for its noninvasive and ubiquitous character by nature, CA-based applications in healthcare have increasingly attracted attention in recent years. During the tough time of the global crisis caused by the coronavirus disease 2019 (COVID-19), scientists and engineers in data science have collaborated to think of novel ways in prevention, diagnosis, treatment, tracking, and management of this global pandemic. On the one hand, we have witnessed the power of 5G, Internet of Things, big data, computer vision, and artificial intelligence in applications of epidemiology modeling, drug and/or vaccine finding and designing, fast CT screening, and quarantine management. On the other hand, relevant studies in exploring the capacity of CA are extremely lacking and underestimated. To this end, we propose a novel multitask speech corpus for COVID-19 research usage. We collected 51 confirmed COVID-19 patients' in-the-wild speech data in Wuhan city, China. We define three main tasks in this corpus, i.e., three-category classification tasks for evaluating the physical and/or mental status of patients, i.e., sleep quality, fatigue, and anxiety. The benchmarks are given by using both classic machine learning methods and state-of-the-art deep learning techniques. We believe this study and corpus cannot only facilitate the ongoing research on using data science to fight against COVID-19, but also the monitoring of contagious diseases for general purpose.