COVID as a catalyst: medical student perspectives on professional identity formation during the COVID-19 pandemic
Bibliographic record
Abstract
Background: As a result of the COVID-19 pandemic, a national decision was made to remove all medical students from clinical environments resulting in a major disruption to traditional medical education. Our study aimed to explore medical student perspectives of professional identity formation (PIF) during a nationally unique period in which there was no clinical training in medical undergraduate programs. Methods: We interviewed fifteen UBC medical students (years 1-4) regarding their perspectives on PIF and the student role in the setting of the COVID-19 pandemic. Data were analysed iteratively and continuously to create a codebook and identify themes of PIF based on interview transcripts. Results: . Conclusions: The impact of disruptions due to COVID-19 catalyzed student reflections on their role within the healthcare system, as well as the role of self-sacrifice in physician identity. Simultaneously, students worried that disruptions to clinical training would prevent them from actualizing the identities they envisioned for themselves in the future. Ultimately, our study provides insight into student perspectives during a novel period in medical training, and highlights the unique ways in which PIF can occur in the absence of clinical exposure.
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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.017 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".