Examining the Effect of Virtual Learning on Canadian Pre-Clerkship Medical Student Well-Being During the COVID-19 Pandemic
Bibliographic record
Abstract
Introduction: The restrictions of the COVID-19 pandemic resulted in the broad and abrupt incorporation of virtual/online learning into medical school curricula. While current literature explores the effectiveness and economic advantages of virtual curricula, robust literature surrounding the effect of virtual learning on medical student well-being is needed. This study aims to explore the effects of a predominantly virtual curriculum on pre-clerkship medical student well-being. Methods: This study followed a constructivist grounded theory approach. During the 2020-2021 and 2021-2022 academic years, students in pre-clerkship medical studies at Western University in Canada were interviewed by medical student researchers over Zoom. Data was analyzed iteratively using constant comparison. Results: We found that students experiencing virtual learning faced two key challenges: 1) virtual learning may be associated with an increased sense of social isolation, negatively affecting wellbeing, 2) virtual learning may impede or delay the development of trainees' professional identity. With time, however, we found that many students were able to adapt by using protective coping strategies that enabled them to appreciate positive elements of online learning, such as its flexibility. Discussion: When incorporating virtual learning into medical education, curriculum developers should prioritize optimizing existing and creating new ways for students to interact with both peers and faculty to strengthen medical student identity and combat feelings of social isolation.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".