Investigating the experiences of medical students quarantined due to COVID-19 exposure
Bibliographic record
Abstract
Background: The COVID-19 pandemic profoundly impacted medical education systems worldwide. Between March 2020 and December 2021, 111 MD students at the University of Toronto completed two-week quarantines due to hospital or community exposures and experienced disrupted clinical instruction. We explored the experiences, barriers, and supports of these quarantined medical students to identify program development opportunities and improve student supports. Methods: We used a qualitative descriptive approach to explore experiences of clerkship students quarantined due to COVID-19 exposure. Methods included an online survey with open-ended questions and an audio-recorded interview. We analysed the demographic survey responses using descriptive statistics. Subsequently, we conducted descriptive thematic analysis of the narrative survey responses and transcribed interview recordings. Results: = 5) included themes of illness uncertainty, racial tensions, confidentiality of COVID-19 status, unclear academic expectations, and financial burden. Supports included friends, family, and MD program administration. Recommendations related to communication, administration, equity considerations, supports, confidentiality/privacy, and academics. Conclusion: Supporting student wellbeing and learning is at the core of medical training. Enhanced understanding of health profession trainee needs during COVID can improve institutional supportive responses to students routinely and during times of crisis.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| 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".