Medical students’ perceptions of learning and working on the COVID-19 frontlines: ‘… a confirmation that I am in the right place professionally’
Bibliographic record
Abstract
The COVID-19 pandemic caused complex and enduring challenges for healthcare providers and medical educators. The rapid changes to the medical education landscape forced universities across the world to pause traditional medical training. In Basel, Switzerland, however, medical students had the opportunity to work on the COVID-19 frontlines. Our purpose was to understand how they perceived both learning and professional identity development in this novel context. We conducted semi-structured interviews with 21 medical students who worked in a COVID-19 testing facility at the University Hospital of Basel. Using constructivist grounded theory methodology, we collected and analyzed data iteratively using the constant comparative approach to develop codes and theoretical themes. Most participants perceived working on the pandemic frontlines as a positive learning experience, that was useful for improving their technical and communication skills. Participants particularly valued the comradery amongst all team members, perceiving that the hierarchy between faculty and students was less evident in comparison to their usual learning environments. Since medical students reported that their work on the pandemic frontlines positively affected their learning, the need to create more hands-on learning opportunities for medical students challenges curriculum developers. Medical students wish to feel like full-fledged care team members rather than observing sideliners. Performing simple clinical tasks and collaborative moments in a supportive learning environment may promote learning and professional development and should be encouraged in the post-pandemic era.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".