Biosafety at Home: How to Translate Biomedical Laboratory Safety Precautions for Everyday Use in the Context of COVID-19

Delphi methodology in healthcare research: How to decide its appropriateness

The Delphi technique is a systematic process of forecasting using the collective opinion of panel members. The structured method of developing consensus among panel members using Delphi methodology has gained acceptance in diverse fields of medicine. The Delphi methods assumed a pivotal role in the last few decades to develop best practice guidance using collective intelligence where research is limited, ethically/logistically difficult or evidence is conflicting. However, the attempts to assess the quality standard of Delphi studies have reported significant variance, and details of the process followed are usually unclear. We recommend systematic quality tools for evaluation of Delphi methodology; identification of problem area of research, selection of panel, anonymity of panelists, controlled feedback, iterative Delphi rounds, consensus criteria, analysis of consensus, closing criteria, and stability of the results. Based on these nine qualitative evaluation points, we assessed the quality of Delphi studies in the medical field related to coronavirus disease 2019. There was inconsistency in reporting vital elements of Delphi methods such as identification of panel members, defining consensus, closing criteria for rounds, and presenting the results. We propose our evaluation points for researchers, medical journal editorial boards, and reviewers to evaluate the quality of the Delphi methods in healthcare research.

Delphi methodology in healthcare research: How to decide its appropriateness

The Delphi technique is a systematic process of forecasting using the collective opinion of panel members. The structured method of developing consensus among panel members using Delphi methodology has gained acceptance in diverse fields of medicine. The Delphi methods assumed a pivotal role in the last few decades to develop best practice guidance using collective intelligence where research is limited, ethically/logistically difficult or evidence is conflicting. However, the attempts to assess the quality standard of Delphi studies have reported significant variance, and details of the process followed are usually unclear. We recommend systematic quality tools for evaluation of Delphi methodology; identification of problem area of research, selection of panel, anonymity of panelists, controlled feedback, iterative Delphi rounds, consensus criteria, analysis of consensus, closing criteria, and stability of the results. Based on these nine qualitative evaluation points, we assessed the quality of Delphi studies in the medical field related to coronavirus disease 2019. There was inconsistency in reporting vital elements of Delphi methods such as identification of panel members, defining consensus, closing criteria for rounds, and presenting the results. We propose our evaluation points for researchers, medical journal editorial boards, and reviewers to evaluate the quality of the Delphi methods in healthcare research.

Facial Recognition System for People with and without Face Mask in Times of the COVID-19 Pandemic

In the face of the COVID-19 pandemic, the World Health Organization (WHO) declared the use of a face mask as a mandatory biosafety measure. This has caused problems in current facial recognition systems, motivating the development of this research. This manuscript describes the development of a system for recognizing people, even when they are using a face mask, from photographs. A classification model based on the MobileNetV2 architecture and the OpenCv’s face detector is used. Thus, using these stages, it can be identified where the face is and it can be determined whether or not it is wearing a face mask. The FaceNet model is used as a feature extractor and a feedforward multilayer perceptron to perform facial recognition. For training the facial recognition models, a set of observations made up of 13,359 images is generated; 52.9% images with a face mask and 47.1% images without a face mask. The experimental results show that there is an accuracy of 99.65% in determining whether a person is wearing a mask or not. An accuracy of 99.52% is achieved in the facial recognition of 10 people with masks, while for facial recognition without masks, an accuracy of 99.96% is obtained.