Big Data for Biomedical Education with a Focus on the COVID-19 Era: An Integrative Review of the Literature

Bridging the Gap: A Mixed-Method Study on Gender and Sexuality Awareness in Medical Education and Practice

BACKGROUND

The integration of gender and sexuality awareness in healthcare is increasingly recognized as vital for patient outcomes. Despite this, there is a notable lack of comprehensive data on the current state of physicians' training and perceptions in these areas, leading to a gap in targeted educational interventions and optimal healthcare delivery.

OBJECTIVE

The study's aim was to explore the experiences and perceptions of attending and resident physicians regarding the inclusion of gender and sexuality content in medical school curricula and professional practice in Israel.

METHODS

This cross-sectional survey targeted a diverse group of physicians across various specializations and experience levels. Distributed through Israeli medical associations and professional networks, it included sections on experiences with gender and sexuality content, perceptions of knowledge, the impact of medical school curricula on professional capabilities, and views on integrating gender medicine in medical education. Descriptive and correlational analyses, along with gender-based and medical status-based comparisons, were employed, complemented and enhanced by qualitative analysis of participants' replies.

RESULTS

The survey, encompassing 189 respondents, revealed low-to-moderate exposure to gender and sexuality content in medical school curricula, with a similar perception of preparedness. A need for more comprehensive training was widely recognized. The majority valued training in these areas for enhancing professional capabilities, identifying ten essential gender-related knowledge areas. The preference for integrating gender medicine throughout medical education was significant. Gender-based analysis indicated variations in exposure and perceptions.

CONCLUSIONS

The study highlights a crucial need for the inclusion of gender and sexuality awareness in medical education and practice. It suggests the necessity for curriculum development, targeted training programs, policy advocacy, mentorship initiatives, and research to evaluate the effectiveness of these interventions. The findings serve as a foundation for future directions in medical education, aiming for a more inclusive, aware, and prepared medical workforce.

CLINICALTRIAL

Using Machine Learning in Veterinary Medical Education: An Introduction for Veterinary Medicine Educators

Machine learning (ML) offers potential opportunities to enhance the learning, teaching, and assessments within veterinary medical education including but not limited to assisting with admissions processes as well as student progress evaluations. The purpose of this primer is to assist veterinary educators in appraising and potentially adopting these rapid upcoming advances in data science and technology. In the first section, we introduce ML concepts and highlight similarities/differences between ML and classical statistics. In the second section, we provide a step-by-step worked example using simulated veterinary student data to answer a hypothesis-driven question. Python syntax with explanations is provided within the text to create a random forest ML prediction model, a model composed of decision trees with each decision tree being composed of nodes and leaves. Within each step of the model creation, specific considerations such as how to manage incomplete student records are highlighted when applying ML algorithms within the veterinary education field. The results from the simulated data demonstrate how decisions by the veterinary educator during ML model creation may impact the most important features contributing to the model. These results highlight the need for the veterinary educator to be fully transparent during the creation of ML models and future research is needed to establish guidelines for handling data not missing at random in medical education, and preferred methods for model evaluation.

A survey on big data-enabled innovative online education systems during the COVID-19 pandemic

With the spread of COVID-19 around the world, the education industry faces enormous challenges. Some colleges and universities have launched online teaching. Comprehensive online teaching and student health checkups help students complete the set teaching content and return to school as soon as possible. With the development of big data, combined with the epidemic risk we are facing, the rational use of big data and the internet for innovative online education has become a mainstream teaching method. Colleges and universities are not yet familiar with the development prospects and future of online education. Through the research of this paper, we can understand the combination of online education and the development of big data and promote its application in colleges and universities. Not only have innovative online education platforms such as MOOC and DingTalk been widely used, but innovative online education methods such as virtual classrooms also have been created. Based on the current epidemic background, this paper analyzes the development of online education, introduces the impact of the combination of online education and big data, and introduces innovative online education technologies and their effects. It helps online education under the influence of the new coronavirus epidemic, operating big data technology to analyze the current prospects and development of online education, showing the combination of big data technology and online education through the analysis of big data technology, and ending with more expectations on other aspects of the use of big data, which affects the online education industry as well as other industries. Finally, we summarize the combination of big data and innovative online education since the emergence of COVID-19 and introduce the concepts and methods of combining online education and big data technology in detail. The online education platform also makes a reasonable introduction. The thesis can be used to understand the problems and challenges faced by innovative online education in the context of the new coronavirus epidemic and look forward to the future on this basis.

Beyond the Digital Competencies of Medical Students: Concerns over Integrating Data Science Basics into the Medical Curriculum

Introduction. Data science is becoming increasingly prominent in the medical profession, in the face of the COVID-19 pandemic, presenting additional challenges and opportunities for medical education. We retrospectively appraised the existing biomedical informatics (BMI) and biostatistics courses taught to students enrolled in a six-year medical program. Methods. An anonymous cross-sectional survey was conducted among 121 students in their fourth year, with regard to the courses they previously attended, in contrast with the ongoing emergency medicine (EM) course during the first semester of the academic year 2020−2021, when all activities went online. The questionnaire included opinion items about courses and self-assessed knowledge, and questions probing into the respondents’ familiarity with the basics of data science. Results. Appreciation of the EM course was high, with a median (IQR) score of 9 (7−10) on a scale from 1 to 10. The overall scores for the BMI and biostatistics were 7 (5−9) and 8 (5−9), respectively. These latter scores were strongly correlated (Spearman correlation coefficient R = 0.869, p < 0.001). We found no correlation between measured and self-assessed knowledge of data science (R = 0.107, p = 0.246), but the latter was fairly and significantly correlated with the perceived usefulness of the courses. Conclusions. The keystone of this different perception of EM versus data science was the courses’ apparent value to the medical profession. The following conclusions could be drawn: (a) objective assessments of residual knowledge of the basics of data science do not necessarily correlate with the students’ subjective appraisal and opinion of the field or courses; (b) medical students need to see the explicit connection between interdisciplinary or complementary courses and the medical profession; and (c) courses on information technology and data science would better suit a distributed approach across the medical curriculum.

Covid-19 Outbreak in Brazil: Health, Social, Political, and Economic Implications

COVID-19 outbreak quickly spread to all corners of the globe. In Brazil, the outbreak was particularly frightening because it worsened existing health, political, economic, and social problems. The results already observed show the contagion ripple-spreading process across the country, causing the death of thousands of people each day and counting, added to a very serious wave of unemployment, scientific denial, and social precariousness. Based on this, this study reviews recent research that looked at the role of the government, the Brazilian health system, and the main economic and social impacts fostered by the pandemic. We perform a scoping review according to the PRISMA-ScR to structure the qualitative synthesis of the 67 associated documents. The results reinforce the negative effects of the country's mismanagement and its consequent impacts on the Brazilian economy and society. The battleground against COVID-19 has fueled political tensions, shaken the health system, and unleashed social despair tinged with thousands of deaths. Finally, in the present scoping review, we discuss concerns about the impacts of the COVID-19 outbreak in Brazil and what the world hopes the country has learned from the current crisis.

Teaching during COVID-19 pandemic in practical laboratory classes of applied biochemistry and pharmacology: A validated fast and simple protocol for detection of SARS-CoV-2 Spike sequences

The pandemic caused by the SARS-CoV-2 virus (COVID-19) is still a major health issue. The COVID-19 pandemic has forced the university teaching to consider in high priority the switch from in-presence teaching to remote teaching, including laboratory teaching. While excellent virtual-laboratory teaching has been proposed and turned out to be very useful, the need of a real-laboratory in-presence teaching is still a major need. This study was aimed at presenting a laboratory exercise focusing (a) on a very challenging therapeutic strategy, i.e. SARS-CoV-2 diagnostics, and (b) on technologies that are playing a central role in applied biochemistry and molecular biology, i.e. PCR and RT-PCR. The aims of the practical laboratory were to determine: (a) the possibility to identify SARS-CoV-2 sequences starting from a recombinant plasmid and (b) the possibility to discriminate cells with respect to the expression of SARS-CoV-2 Spike protein. This activity is simple (cell culture, RNA extraction, RT-qPCR are all well-established technologies), fast (starting from isolated and characterized RNA, few hours are just necessary), highly reproducible (therefore easily employed by even untrained students). We suggest that this laboratory practical exercises should be considered for face-to-face teaching especially if the emergency related to the COVID-19 pandemic is maintained. The teaching protocol here described might be considered in order to perform fast but meaningful in-presence teaching, making feasible the division of crowded classes in low-number cohorts of students, allowing the maintenance of the required social distance.