Medical students on the COVID-19 frontline: a qualitative investigation of experiences of relief, stress, and mental health
Bibliographic record
Abstract
Objective: During the early stages of the COVID-19 pandemic, medical students were abruptly removed from clinical rotations and transitioned to virtual learning. This study investigates the impact of this shift on students' wellbeing and preparedness for advanced training. Methods: Through qualitative research methods, including semi-structured interviews, the experiences of medical students working on the COVID-19 frontline were explored. Results: The comprehensive findings of the study shed light on the profound emotional journey that medical students embarked upon during the relentless public health crisis. Within the chaos and overwhelming demands of the pandemic, medical students discovered a profound sense of purpose and fulfillment in their contributions to the welfare of the community. Despite the personal sacrifices they had to make, such as long hours, limited social interactions, and potentially risking their own health, students reported feelings of relief and gratitude. Conclusion: Tailored support systems for medical students' wellbeing are crucial for improving healthcare delivery during crises. Medical schools should adopt a holistic curriculum approach, integrating interdisciplinary learning and prioritizing student wellbeing. Recognizing the pandemic's impact on students and implementing targeted support measures ensures resilience and contributes to an improved healthcare system.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".