ABSTRACT
Background: It is necessary to use accurate methods of calculating manpower to avoid wasting resources. Given that reproductive health services are an important part of primary health services, it would be useful to apply manpower calculation methods to service providers in related occupational groups. This study calculated the required midwifery manpower in healthcare centres using the Workload Indicator of Staffing Need (WISN) method. Methods: A cross-sectional study was conducted in 2020 in 276 health centres in Tehran according to the WISN model, in which the list of activities related to reproductive health in the centres under the auspices of the University of Tehran was determined. The available working time, main workload components, and the activity standards (service standards and allowance standards) were determined through a mixed method (direct observation, comments of service providers, and meetings with experts). The volume of services provided over the period of one year prior to the onset of the Covid-19 pandemic was estimated using national statistics. Finally, the required manpower was calculated and compared with the actual workforce. Results: The results showed that currently, Tehran health centres need 34 more staff to provide reproductive health services. The working pressure was calculated as 0.9, and the current allowance factor is 1.31. Based on observations, midwifery capacity is currently not fully and exclusively used. Conclusion: To improve the quality of reproductive health services, it is recommended to eliminate the current shortage of midwives, focus more on special midwifery tasks, and reduce the number of indirect activities of midwives. © 2022 UNISA, University of South Africa, Department of Advanced Nursing Sciences. All rights reserved.
ABSTRACT
The Covid-19 outbreak has caused disruptions in the education sector, making remote education the dominant mode for lecture delivery. The lack of visual feedback and physical interaction makes it very hard for teachers to measure the engagement level of students during lectures. This paper proposes a time-bounded window operation to extract statistical features from raw gaze data, captured in a remote teaching experiment and link them with the student's attention level. Feature selection or dimensionality reduction is performed to reduce the convergence time and overcome the problem of over-fitting. Recursive feature elimination (RFE) and SelectFromModel (SFM) are used with different machine learning (ML) algorithms, and a subset of optimal feature space is obtained based on the feature scores. The model trained using the optimal feature subset showed significant improvement in accuracy and computational complexity. For instance, a support vector classifier (SVC) led 2.39% improvement in accuracy along with approximately 66% reduction in convergence time. © 2022 IEEE.
ABSTRACT
Objectives: In patients with periodontal disease, the risk of COVID-19 is very high, so it is important to check oral health. Because the clinical manifestations of COVID-19 disease are not fully understood and the relationship between severity, mortality, and oral health is challenging, in the present study, we tried to investigate the relationship between clinical outcomes and COVID-19. Therefore, the present study evaluated the associations between the dry mouth, Oral lesions, and Orofacial pain with COVID-19. Method(s): The present study is based on PRISMA guidelines;all articles were published in international databases such as PubMed, Scopus, Science Direct, and Embase between March 2019 and May 2022. 95% confidence interval for effect size with random effect modal and REML were calculated. Meta-analysis of data collected from selected studies was performed using STATA.V16 software. Result(s): In the initial review, the s of 312 studies were reviewed, two authors reviewed the full text of 36 studies, and finally, 16 studies were selected. The prevalence of dry mouth and oral lesions in patients with COVID-19 was 39% (ES: 95% CI, 32 % to 46%) and 32% (ES: 95% CI, 22 % to 42%), respectively. Conclusion(s): The prevalence of dry mouth in patients with COVID-19 was higher (39%) than oral lesions (32%) and orofacial pain (23%). Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.
ABSTRACT
The outbreak of the COVID-19 pandemic has caused unprecedented disruption to education and progressed remote teaching as a predominant model for delivering educational content. However, the online teaching and learning model has its challenges, such as the lack of technological tools to quantity the student attention and engagement with the learning content. This paper focuses on developing an e-learning framework for capturing and analysing the students' attention during remote teaching sessions and subsequently profiling their learning behaviour leveraging eye-tracking data. Our proposed eye-tracking solution deploys a webcam to capture and track raw gaze points that grant the user the freedom of natural head movement and scalability compared to conventional eye-tracking approaches. We derived various gaze metrics in conjunction with state-of the-art machine learning (ML) models like logistic regression, support vector machine and polynomial regression to classify the student attention with an accuracy above 91%. Furthermore, our findings can help in the early detection and diagnosis of attention deficit hyperactivity disorder (ADHD) among students, thus supporting their learning journeys by creating an adaptive learning environment tailored to their needs. © 2022 IEEE.
ABSTRACT
The COVID-19 pandemic has accelerated our transition to an online and self-directed learning environment. In an effort to design better e-learning materials, we investigated the effectiveness of collecting psychophysiological eye-tracking data from participants in response to visual stimuli. In particular, we focused on collecting fixation data since this is closely related to human attention. Current wearable devices allow the measurement of visual data unobtrusively and in real-time, leading to new applications in wearable technology. Despite their accuracy, head-mounted eye trackers are too expensive for deployment on large-scale deployment. Therefore, we developed a low-cost, webcam-based eye tracking solution and compared its performance with a commercial head-mounted eye tracker. Four-minute lecture slides on the 3rd year electronic engineering course were presented as stimuli to eight learners for data collection. Their eye movement was collected within the pre-defined area of interest (AOI). Our results demonstrate that a low-cost webcam-based eye-tracking solution, combined with machine learning algorithms, can achieve similar accuracy to the head-worn tracker. Based on these results, learners can use the eye tracker for attention guidance. Our work also demonstrates that these webcam-based eye trackers can be scaled up and used in large classrooms to provide real-time information to instructors regarding student attention and behaviour. © 2022 IEEE.
ABSTRACT
Background: Hypertension is the main factor to predict the severity and mortality of COVID-19. The use of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) is challenging. This study aimed to investigate the effect of ACEIs and ARBs on clinical outcomes in COVID-19 patients with hypertension. Materials and Methods: This cross-sectional study was carried out on 498 patients who were referred to Razi hospital following COVID-19 development and also had hypertension. Patients were divided into two groups receiving drugs in the ACEIs and ARB's groups and those not receiving these drugs. The primary outcome was death up to one month after the onset of symptoms. Results: Cardiovascular disease in patients taking ACEIs/ARBs was higher (p<0.001). One hundred eleven deaths (22.3%) were seen in the studied patients in whom 66 deaths (59.5%) belonged to the group not taking ACEIs and ARBs (p>0.05). Seventy-nine patients (15.86%) were admitted to ICU in which 62.03% of these patients died while the non-ICU mortality rate was 14.8% (Odds Ratio = 9.40;95% CI: 5.54 to 15.95, p <0.001). A subgroup analysis found that among patients with diabetes who had hypertension, the incidence of death was 43.55% in the group taking ARBs/ACEi lower than in another group significant (p = 0.021). Conclusion: The mortality rate in the patients taking ACEIs/ARBs is not different from other groups. It was found that among COVID-19 patients with diabetes who had hypertension, the incidence of death in the patients taking ARBs/ACEi was lower than in another group. © 2021 Universitas Gadjah Mada - Faculty of Pharmacy. All rights reserved.
ABSTRACT
Coronaviruses are a group of highly enveloped, diverse, and RNA-containing viruses that lead into respiratory, intestinal, hepatic, and neurological diseases with varied severity in a wide spectrum of animal species, such as human. In December 2019, a new coronavirus was detected in China that was stronger than the rest of the Coronaviridae family, causing potential hurt to vital organs such as the lungs, heart, liver, kidneys, and pneumonia. Although COVID-19 was reported in adults during early stages of the epidemic, further studies have shown that young children and infants are also vulnerable to the infection. Since this disease occurs in children and infants and since it may in some cases cause a critical and severe condition in them, the present study tries to find methods of prevention and treatment approaches in children with the coronavirus. © 2020 EManuscript Technologies. All rights reserved.