Training Residents for the Future: A Virtual Care Rotation for Emergency Medicine
Bibliographic record
Abstract
OBJECTIVESVirtual care (VC) is increasingly becoming a part of emergency medicine (EM) physician workflows, yet no formal digital health curricula exist within Canadian EM training programs. The objective was to design and pilot a VC elective rotation for EM residents to help address this gap and better prepare them for future VC practice.METHODSThe current work describes the design and implementation of a 4-week VC elective rotation for EM residents. The rotation consisted of VC shifts, medical transport shifts, one-on-one discussions with various stakeholders, weekly thematic articles, and a final project deliverable.RESULTSThe rotation was well received by all stakeholders, and the quality of feedback and one-on-one teaching were highlighted as strengths. Future work will consider the optimal delivery timing of this type of curricula, whether all EM residents should receive basic training in VC, and how our current findings may be generalizable to other VC sites.CONCLUSIONA formal digital health curriculum for EM residents supports competency development for delivering VC as part of future EM practice.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.046 | 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".