ABSTRACT
The experiences that higher education students have with technology and learning with the support of technological resources can generate feelings of stress and anxiety. Understanding whether or not students are ill-adapted to technology is of utmost importance to understand the extent to which changes are needed in the teaching and learning process. With this purpose, the students' perceptions about the technology, namely its familiarity, the ease of use, the utility of technological resources, levels of satisfaction with learning from remote learning, and levels of technostress during the confinement period due to the COVID-19 pandemic were evaluated. Several statistical methods were applied, including the multiple correspondence analysis and the k-means clustering algorithm, in order to obtain a partition of students based on their perceptions and experiences in the course of remote learning. The results revealed three distinct profiles concerning students' perceptions about their relationship with technology. Copyright © 2022, IGI Global.
ABSTRACT
Anemia and jaundice are common health conditions that affect millions of children, adults, and the elderly worldwide. Recently, the pandemic caused by severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2), the virus that leads to COVID-19, has generated an extreme worldwide concern and a huge impact on public health, education, and economy, reaching all spheres of society. The development of techniques for non-invasive diagnosis and the use of mobile health (mHealth) is reaching more and more space. The analysis of a simple photograph by smartphone can allow an assessment of a person's health status. Image analysis techniques have advanced a lot in a short time. Analyses that were previously done manually, can now be done automatically by methods involving artificial intelligence. The use of smartphones, combined with machine learning techniques for image analysis (preprocessing, extraction of characteristics, classification, or regression), capable of providing predictions with high sensitivity and specificity, seems to be a trend. We presented in this review some highlights of the evaluation of anemia, jaundice, and COVID-19 by photo analysis, emphasizing the importance of using the smartphone, machine learning algorithms, and applications that are emerging rapidly. Soon, this will certainly be a reality. Also, these innovative methods will encourage the incorporation of mHealth technologies in telemedicine and the expansion of people's access to health services and early diagnosis.
ABSTRACT
OBJECTIVE: To explore the knowledge and use of personal protective equipment by nursing professionals of Primary Health Care during the Covid-19 pandemic. METHOD: This is an exploratory study of a non-probabilistic sample developed in Basic Health Units in the city of Picos, Piaui, Brazil. Data were collected between June and August 2020, by phone call, following a semi-structured script, with responses recording and transcription. For material analysis, the software IRaMuTeQ was used for statistical textual analyses: Descending Hierarchical Classification, similarity analysis, and word cloud. RESULTS: From the corpus of 6,873 words and 832 lexical units, three categories were created: (1) motivations and barriers for use (20.9%);(2) handling of personal protective equipment (classes 5 and 4) with 25% and 21.6%, respectively, and (3) measures to protect users and health professionals (classes 3 and 5) with 17.6% and 14.9%. CONCLUSION: The nursing professionals interviewed demonstrated that they did not have sufficient knowledge for the proper use of the equipment, which could compromise their integrity and that of the patient as a subject who receives unsafe care.
ABSTRACT
Objective: To explore the knowledge and use of personal protective equipment by nursing professionals of Primary Health Care during the Covid-19 pandemic. Method: This is an exploratory study of a non-probabilistic sample developed in Basic Health Units in the city of Picos, Piaui, Brazil. Data were collected between June and August 2020, by phone call, following a semi-structured script, with responses recording and transcription. For material analysis, the software IRaMuTeQ was used for statistical textual analyses: Descending Hierarchical Classification, similarity analysis, and word cloud. Results: From the corpus of 6,873 words and 832 lexical units, three categories were created: (1) motivations and barriers for use (20.9%);(2) handling of personal protective equipment (classes 5 and 4) with 25% and 21.6%, respectively, and (3) measures to protect users and health professionals (classes 3 and 5) with 17.6% and 14.9%. Conclusion: The nursing professionals interviewed demonstrated that they did not have sufficient knowledge for the proper use of the equipment, which could compromise their integrity and that of the patient as a subject who receives unsafe care.