Piloting ‘Virtual Ward’: a novel platform for delivering medical student education by residents
Bibliographic record
Abstract
BACKGROUND: Clinical experiences lie at the heart of undergraduate medical education (UGME). COVID-19 related disruptions in Medical Education impacted medical students substantially. As educators, efforts directed at developing new mediums to educate our medical students in the face of these new limitations were vital. The Virtual Ward (VW) pilot was an inaugural resident-driven, virtual educational opportunity aimed at supplement the learning of core internal medicine skills for undergraduate medical students. METHODS: Interested medical students were paired in groups of 5-6 with an internal medicine resident tutor. The McMaster University UGME core internal medicine topic list was provided to resident tutors to teach in an open, morning-report format in which students directed content selection. Following completion of the VW series, we distributed an online anonymous survey using a 5-point Likert scale to gauge the efficacy of the intervention and compare it to existing learning modalities offered by the UGME. RESULTS: In total, 166 medical students and 27 internal medicine resident tutors participated in the VW pilot. 46 (28%) medical students responded to the survey and 96% of survey respondents rated the sessions as being helpful to their learning. The majority rated VW superior to existing learning modalities and 94% thought VW should continue after COVID-related restrictions abate. CONCLUSIONS: VW is a novel educational platform that was very well received by learners. We propose VW may have a continued supplemental role post-pandemic to help with translation of knowledge to clinical skills and provide an additional avenue of mentorship for students.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".