COVID-19 led to the widespread withdrawal of face-to-face hospital-based clinical placements, with many medical schools switching to online learning. This precipitated concern about potential negative impact on clinical and interprofessional skill acquisition. To overcome this problem, we piloted a 12-week COVID-19 safe face-to-face clinical placement for 16 medical students at the Hospital for Tropical Diseases, London, during the first wave of the COVID-19 pandemic. COVID-19 infection control measures necessitated that students remained in 'social bubbles' for placement duration. This facilitated an apprenticeship-style teaching approach, integrating students into the clinical team for placement duration. Team-based learning was adopted to develop and deliver content. Teaching comprised weekly seminars, experiential ward-based attachments and participation in quality improvement and research projects. The taught content was evaluated through qualitative feedback, reflective practice, and pre-apprenticeship and post-apprenticeship confidence questionnaires across 17 domains. Students' confidence improved in 14 of 17 domains (p<0.05). Reflective practice indicated that students valued the apprenticeship model, preferring the longer clinical attachment to existent shorter, fragmented clinical placements. Students described improved critical thinking, group cohesion, teamwork, self-confidence, self-worth and communication skills. This article describes a framework for the safe and effective delivery of a longer face-to-face apprenticeship-based clinical placement during an infectious disease pandemic. Longer apprenticeship-style attachments have hidden benefits to general professional training, which should be explored by medical schools both during the COVID-19 pandemic and, possibly, for any future clinical placements.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7887865 | PMC |
http://dx.doi.org/10.1136/postgradmedj-2021-139728 | DOI Listing |
Sci Rep
December 2024
College of Mechanical and Electronic Engineering, Dalian Minzu University, Dalian, 116650, Liaoning, China.
The novel coronavirus (COVID-19) has affected more than two million people of the world, and far social distancing and segregated lifestyle have to be adopted as a common solution in recent years. To solve the problem of sanitation control and epidemic prevention in public places, in this paper, an intelligent disinfection control system based on the STM32 single-chip microprocessor was designed to realize intelligent closed-loop disinfection in local public places such as public toilets. The proposed system comprises seven modules: image acquisition, spraying control, disinfectant liquid level control, access control, voice broadcast, system display, and data storage.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Mathematics, GC University, Lahore, Pakistan.
In this article, a nonlinear fractional bi-susceptible [Formula: see text] model is developed to mathematically study the deadly Coronavirus disease (Covid-19), employing the Atangana-Baleanu derivative in Caputo sense (ABC). A more profound comprehension of the system's intricate dynamics using fractional-order derivative is explored as the primary focus of constructing this model. The fundamental properties such as positivity and boundedness, of an epidemic model have been proven, ensuring that the model accurately reflects the realistic behavior of disease spread within a population.
View Article and Find Full Text PDFNat Commun
December 2024
Division of Rheumatology and Clinical Immunology, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
Antibody-mediated protection against pathogens is crucial to a healthy life. However, the recent SARS-CoV-2 pandemic has shown that pre-existing comorbid conditions including kidney disease account for compromised humoral immunity to infections. Individuals with kidney disease are not only susceptible to infections but also exhibit poor vaccine-induced antibody response.
View Article and Find Full Text PDFNat Commun
December 2024
State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau, China.
Lipid nanoparticles (LNPs) have proven effective in mRNA delivery, as evidenced by COVID-19 vaccines. Its key ingredient, ionizable lipids, is traditionally optimized by inefficient and costly experimental screening. This study leverages artificial intelligence (AI) and virtual screening to facilitate the rational design of ionizable lipids by predicting two key properties of LNPs, apparent pKa and mRNA delivery efficiency.
View Article and Find Full Text PDFNat Commun
December 2024
Laboratory of Aging Research and Cancer Drug Target, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China.
The immune escape capacities of XBB variants necessitate the authorization of vaccines with these antigens. In this study, we produce three recombinant trimeric proteins from the RBD sequences of Delta, BA.5, and XBB.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!