Effectiveness of Virtual Medical Teaching During the COVID-19 Crisis: Systematic Review
Bibliographic record
Abstract
BACKGROUND: In December 2019, COVID-19 emerged and rapidly spread worldwide. Transmission of SARS-CoV-2, the virus that causes COVID-19, is high; as a result, countries worldwide have imposed rigorous public health measures, such as quarantine. This has involved the suspension of medical school classes globally. Medical school attachments are vital to aid the progression of students' confidence and competencies as future physicians. Since the outbreak of COVID-19, medical schools have sought ways to replace medical placements with virtual clinical teaching. OBJECTIVE: The objective of this study was to review the advantages and disadvantages of virtual medical teaching for medical students during the COVID-19 pandemic based on the current emerging literature. METHODS: A brief qualitative review based on the application and effectiveness of virtual teaching during the COVID-19 pandemic was conducted by referencing keywords, including medical student virtual teaching COVID-19, virtual undergraduate medical education, and virtual medical education COVID-19, in the electronic databases of PubMed and Google Scholar. A total of 201 articles were found, of which 34 were included in the study. Manual searches of the reference lists of the included articles yielded 5 additional articles. The findings were tabulated and assessed under the following headings: summary of virtual teaching offered, strengths of virtual teaching, and weaknesses of virtual teaching. RESULTS: The strengths of virtual teaching included the variety of web-based resources available. New interactive forms of virtual teaching are being developed to enable students to interact with patients from their homes. Open-access teaching with medical experts has enabled students to remain abreast of the latest medical advancements and to reclaim knowledge lost by the suspension of university classes and clinical attachments. Peer mentoring has been proven to be a valuable tool for medical students with aims of increasing knowledge and providing psychological support. Weaknesses of virtual teaching included technical challenges, confidentiality issues, reduced student engagement, and loss of assessments. The mental well-being of students was found to be negatively affected during the pandemic. Inequalities of virtual teaching services worldwide were also noted to cause differences in medical education. CONCLUSIONS: In the unprecedented times of the COVID-19 pandemic, medical schools have a duty to provide ongoing education to medical students. The continuation of teaching is crucial to enable the graduation of future physicians into society. The evidence suggests that virtual teaching is effective, and institutions are working to further develop these resources to improve student engagement and interactivity. Moving forward, medical faculties must adopt a more holistic approach to student education and consider the mental impact of COVID-19 on students as well as improve the security and technology of virtual platforms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".