ABSTRACT
Aim: To evaluate the effect of decontamination and reuse on N95 masks. Background(s): The coronavirus disease (COVID-19) pandemic has strained the global availability of masks. Such shortage represents a threat to healthcare workers (HCWs). Mask reprocessing and reuse may alleviate the shortage. Many laboratory studies have proven the effectiveness and feasibility of decontaminating N95 masks. However, very few had HCWs wearing them between cycles of decontamination. Our study evaluated mask integrity (assessed by qualitative mask fitting [QMF], as well as technical measures like bacterial filtration efficacy [BFE]) through five cycles of decontamination using four different modalities - steam, moist heat (MH), UV-C irradiation (UVCI), and hydrogen peroxide vaporization (HPV). Method(s): Each study cycle involved a HCW wearing a N95 mask for two hours, followed by the assigned decontamination process, and then a QMF. This was repeated for a maximum of 5 cycles, as long as the wearer passed QMF. 40 HCWs were recruited for each of the four decontamination modalities. The technical measures of mask integrity assessed were: BFE, Particulate Filtration Efficiency (PFE), Pressure Drop and Splash Resistance. Result(s): 60.6% (HPV) to 77.5% (MH) of the masks passed five cycles of wear and decontamination, as assessed by the wearers passing QMF all five times. MH-decontaminated masks retained all technical measures of integrity through all 5 cycles. HPV reduced masks' BFE after the fourth cycle while UVCI tended to increase the Pressure Drop. Conclusion(s): The results suggest that MH is a promising method for decontaminating N95 masks without compromising fit and integrity. [Figure presented] [Table presented]Copyright © 2023 Southern Society for Clinical Investigation.
ABSTRACT
The unprecedented long-term online learning caused by COVID-19 has increased stress symptoms among students. The e-learning system reduces communications between teachers and students, making it difficult to observe student's mental issues and learning performance. This study aims to develop a non-intrusive method that can simultaneously monitor stress states and cognitive performance of student in the scenario of online education. Forty-three participants were recruited to perform a computer-based reading task under stressful and non-stressful conditions, and their eye-movement data were recorded. A tree ensemble machine learning model, named LightGBM (Light Gradient Boosting Machine), was utilized to predict stress states and reading performance of students with an accuracy of 0.825 and 0.793, respectively. An interpretable model, SHAP (SHapley Additive exPlanation), was used to identify the most important eye-movement indicators and their effects on stress and reading performance. The proposed model can serve as a foundation for further development of user-centred services in e-learning system. © 2022 The authors and IOS Press.
ABSTRACT
Purpose: The corona virus disease-19 (COVID-19) pandemic has resulted in mandatory masking of patients and physicians during outpatient visits. This study evaluated the changes in intraocular pressure (IOP) according to mask use. Methods: This prospective study enrolled 30 healthy volunteers (60 eyes). IOP was measured via Goldmann applanation tonometry (GAT) for the subjects wearing one of four commonly used masks: dental, bi-folding Korean Filter (KF)94, tri-folding KF94, and dust masks. Subjects with IOP measurement errors of more than 5 mmHg were rechecked with another GAT type. Results: The mean IOP measured via GAT before mask wearing was 13.7 +/- 1.7 mmHg. It was 13.5 +/- 2.1, 14.0 +/- 2.3, 14.3 +/- 2.5, and 13.8 +/- 1.6 mmHg with the dental, bi-folding KF94, tri-folding KF94, and dust masks, respectively. There were no significant differences in IOP according to mask type (p = 0.635). IOP errors above 5 mmHg were detected in three subjects who had contact between the GAT feeler arm and tri-folding KF94 mask during IOP measurement. Conclusions: The IOP as measured via GAT is artificially elevated by mechanical interference from the tri-fold KF94 mask. To minimize such mask-induced artifacts in GAT measurements, compress the patient's mask or change the mask type to prevent any contact during measurement.
ABSTRACT
In online learning, students’ 'fit' (or satisfaction) with necessary technologies has become a vital component in assessing their learning efficacy,especially during the COVID-19 pandemic. While current studies have notedthe impact of the curriculum, the instructor, and the learner, there is insufficientunderstanding of factors that predict students’ satisfaction with online learningduring the crisis [38]. Existing studies focus on pre-pandemic circumstances,where online learning was a minor part of the higher education (HE) paradigm.This study assesses HE students’ use (i.e. 'fit') with online learning via theirperception, behavioral intention, and satisfaction. By utilizing the InformationTechnology (IT) models of Task-Technology Fit (TTF) and Unified Theory ofAcceptance and Use of Technology (UTAUT), the study investigates if, fromstudents’ perspective, pedagogical theories are aligned with the IT models, using the quantitative survey method to gather input from students across variousdisciplines in a Singaporean university. Standard descriptive and correlationanalyses studied the link between factors and their influence on online learningsatisfaction. Significantly, the IT models are found to be valuable in assessingonline learning satisfaction. Recommendations arising from the study providehelpful strategic guidelines for future online learning, which apply to Singaporeand online learning design in general, particularly in this time of paradigmchange. © 2022,International Journal of Emerging Technologies in Learning. All Rights Reserved.