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
Abstract Introduction The COVID-19 pandemic caused complex and enduring challenges for health care providers and medical educators and changed the medical education landscape for learners. Medical students were required to adapt and learn in a novel learning environment while universities paused their formal medical training. The current study sought to investigate medical students’ experiences working on a pandemic frontline to understand how they perceived this novel learning environment influenced both their learning and their developing professional identity. Methods 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 a constant comparative approach to develop codes and theoretical categories. Results Participants described improvements in their technical and communication skills, consequently impacting their professional development. The presence of a perceived flat hierarchy between the physicians and medical students promoted professional identity development amongst the medical students. Most participants perceived working on the pandemic frontlines as a positive learning experience, which seemed supported by a flatter hierarchy and open communication compared to their usual learning environment. Conclusion Since medical students reported that their work on the pandemic frontlines positively affected their learning, the need to create hands-on learning opportunities for medical students challenge curriculum developers. Medical students wish to feel like full-fledged care team members rather than observing learners. Performing simple clinical tasks and collaborative moments in a supportive learning environment may promote learning and professional development.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".