Quarantining From Professional Identity: How Did COVID-19 Impact Professional Identity Formation in Undergraduate Medical Education?
Bibliographic record
Abstract
Introduction: Professional Identity Formation (PIF) entails the integration of a profession's core values and beliefs with an individual's existing identity and values. Within undergraduate medical education (UGME), the cultivation of PIF is a key objective. The COVID-19 pandemic brought about substantial sociocultural challenges to UGME. Existing explorations into the repercussions of COVID-19 on PIF in UGME have predominantly adopted an individualistic approach. We sought to examine how the COVID-19 pandemic influenced PIF in UGME from a sociocultural perspective. This study aims to provide valuable insights for effectively nurturing PIF in future disruptive scenarios. Methods: Semi structured interviews were conducted with medical students from the graduating class of 2022 (n = 7) and class of 2023 (n = 13) on their medical education experiences during the pandemic and its impact on their PIF. We used the Transformation in Medical Education (TIME) framework to develop the interview guide. Direct content analysis was used for data analysis. Results: The COVID-19 pandemic significantly impacted the UGME experience, causing disruptions such as an abrupt shift to online learning, increased social isolation, and limited in-person opportunities. Medical students felt disconnected from peers, educators, and the clinical setting. In the clerkship stage, students recognized knowledge gaps, producing a "late blooming" effect. There was increased awareness for self-care and burnout prevention. Discussion: Our study suggests that pandemic disruptors delayed PIF owing largely to slower acquisition of skills/knowledge and impaired socialization with the medical community. This highlights the crucial role of sociocultural experiences in developing PIF in UGME. PIF is a dynamic and adaptable process that was preserved during the COVID-19 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 | 0.017 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".