Impact of Covid-19 on Medical Students around the Globe
Bibliographic record
Abstract
The Coronavirus (COVID-19) pandemic has caused an unexpected disturbance in healthcare systems as well as medical education worldwide. This article aims to provide an overview of the circumstances experienced by medical students during the COVID-19 pandemic. As this disease can cause life-threatening conditions, it has presented challenges to medical educators and students as they must adapt to changes in their medical education to ensure lectures are given safely as well as effectively. Many medical students feel the sudden change in their education system impacted their training negatively; 74% of students surveyed by members of McGill University reported a decrease in the quality of their education since the start of COVID-19. As well as a negative impact on medical education, this pandemic has caused unprecedented psychological stress on numerous people around the world, especially individuals in the medical field. 48% of medical students at a Canadian university reported feeling more depressed since the onset of COVID-19. The sudden changes, isolation, and worries about health have impacted students' mental health drastically. On the other hand, some students have reported that this pandemic has made a positive impact on their mental health as they had more time to focus on their mental well-being and they felt an overall reduction in pressure and stress. As COVID-19 remains to impact individuals worldwide, effective strategies towards improving mental health and quality of education should be provided to medical students affected by the challenges of this pandemic.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".