Medical education during the COVID-19 pandemic and the process of professional identity formation: Resident perspectives from a North American training program
Bibliographic record
Abstract
Introduction: The coronavirus disease 2019 (COVID-19) pandemic forced immediate changes to the delivery of medical education globally. At the University of Toronto, traditional in-person group learning and bedside teaching were replaced by virtual learning. The ensuing professional and social isolation impacted the centuries-old art of medicine and socialization into communities of practice (COPs). Methods: The authors explored the perceived impact of the pandemic on the education and training of internal medicine (IM) residents at the University of Toronto and how it may have affected the process of their professional identity formation (PIF). Semi-structured interviews were conducted with nine IM residents using a constructivist grounded theory approach. Results: Residents discussed the effects of COVID-19 pandemic on their learning, training, and wellness. They appreciated the convenience of virtual asynchronous learning but were concerned about the loss of bedside teaching, procedural opportunities, and varied clinical exposure. They considered the impact of the pandemic on their future practice and the absence of community building. They acknowledged how personal and patient stressors, social and professional isolation, and loss of coping strategies affected their wellness. Discussion: The COVID-19 pandemic affected the educational and training experiences and wellness of IM residents at the University of Toronto. It altered both clinical and nonclinical experiences and residents’ socialization into COPs—all critical to PIF. Various recommendations to support residents in their PIF process are discussed. A future area of research is how PIF evolves in the coming years, given the pandemic's unprecedented impact on professional training and community building.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| 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".