The Impact of COVID-19 on the Obstetrics and Gynecology Clinical Clerkship Experience
Bibliographic record
Abstract
Background The COVID-19 pandemic disrupted many aspects of medical education; however, there is currently no published literature describing the pandemic's impact on the obstetrics and gynecology (OBGYN) clerkship experience. The purpose of this study was to survey medical students at the University of Toronto who completed their OBGYN rotation during the pandemic to gain perspective on how it impacted their clerkship experience. Methods An anonymous, voluntary survey regarding the OBGYN clerkship experience was circulated to all University of Toronto medical students who completed their clerkships between September 2020 and August 2021. Data was collected between December 3, 2021 and January 3, 2022. Results Of the 255 students surveyed, 95 (36.4%) responded. Among them, 57 (64%) reported increased stress during their clerkship, while 40 (44.9%) found their OBGYN rotation to be more stressful than other rotations. Additionally, 30 (33.7%) indicated that the pandemic led them to question their choice of medicine as a career. Regarding the quality of their OBGYN rotation, 21 (24.4%) noticed a significant change, with 29 (33.7%) attributing this to a lack of patients and 35 (40.7%) expressing concerns about acquiring hands-on skills. A thematic analysis of student responses identified four key themes: Virtual Learning, Clinical Workload and Volume, Surgical Cancellations, and Caring for Patients During COVID. Conclusions The pandemic has had a significant impact on the OBGYN clerkship experience and student well-being. This study highlights several quality concerns, including reduced clinical volumes and limited hands-on learning opportunities, particularly in gynecology.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".