COVID-19: Impact on medical education in India
Bibliographic record
Abstract
Medical education is an important sector, and being at the center of ongoing pandemic, it has been affected throughout the world, including India, which homes 536 medical colleges offering about 79,000 MBBS seats every year.[1] Here, we try to analyze the impact of pandemic on medical education. Most of the medical colleges in India still follow the traditional didactic method of teaching. Of late, lecture capture technology has been tried in some health universities. However, it has its own limitations, viz., a lack of student–teacher interaction and direct monitoring.[2] Medical schools have been compelled to switch over to remote online teaching. Though this concept is practiced in the western world,[2] it is new in India. Although theory classes can be taught using this method, it is impractical for clinical teaching. Innovative methods such as online repository of patient treatment recordings and cases along with telemedicine are helpful,[2] though they may not replace real-life scenarios. For students coming from remote sections of society, reach of this method is debatable, as it requires a good internet supply. In one of the worst hit countries such as United Kingdom, examinations are postponed, expedited, or canceled. Some universities have replaced the examination of real patients by video footages and screen-based assessments; online remote and open book assessments have replaced written examinations.[3] They have their own limitations and can never replace offline examinations especially for clinicals. Internship being an important part of medical education is a transformative phase from students to real-life doctors. As most of the public sector medical college hospitals were designated as corona virus disease (COVID)-19 care centers, interns posted there were not allowed to work in certain specialties, because of the fear of unnecessary use of personal protective equipment and potential exposure to virus. Interns were posted in low-risk areas such as outpatient care in other departments, inpatient care of non-COVID patients, and assisted in the remote management of COVID-19 cases.[4] If trained in the basics of COVID-19 management, it may instill confidence to tackle future pandemics. Of course, a history has taught us some valuable lessons. For example, formal teaching, examinations, clerkships, and electives were delayed in countries such as China and Canada during severe acute respiratory syndrome outbreak,[5] whereas medical students were allowed to treat patients during 1918 Spanish flu outbreak in the United States and 1952 Polio outbreak in Denmark.[4] However, interns learn more about the management of the pandemic and are less exposed to other medical conditions. Postgraduate students were worst hit due to the pandemic. Amidst delayed entrance examinations and an ever-increasing burden on existing postgraduates with continuously inflating number of patients, final-year post graduate’s were left with no time to prepare for examinations and dissertation. Academics took a backseat. Conferences either were postponed or canceled hampering their ability to display their presentation and interactive skills.[6] Many conferences continue to be conducted on virtual platform, which hardly replicate a real-life scenario. Some residency programs have been restructured with innovative methods such as telemedicine clinics, surgical simulation, online courses on research methodologies, and training in specialty areas such as ethics, global health, and health policy.[7] COVID-19 is here to stay and so are unforeseen pandemics. Medical education unlike other courses has to adopt rapidly to the fast changing pandemic situation. It has to be restructured through innovative methods, like never before. Teacher–student duo has to learn and be prepared to the nascence of online teaching and evaluation methods. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.007 |
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".