Virtual Care in Undergraduate Medical Education: perspectives beyond the pandemic. How medical education can support a change of culture towards virtual care delivery in Canada
Bibliographic record
Abstract
The pandemic has led to further the importance of telemedicine, teleconsultation, and technology as essential components for delivering of care in all settings. Prior to the pandemic, the instruction surrounding the safe delivery of virtual care in undergraduate medical education was sparse and informal. For care to be delivered to the high standards expected of Canadian physicians, the University of Ottawa undergraduate medical program (UGME) made the decision to define virtual care as a series of tools to facilitate and support the safe delivery of care. By focusing on virtual care as a set of tools, it provides the framework for skill development for future clinicians early in their careers and provides a critical thinking pathway to support the ever-evolving landscape of digital technology in the provision of safe, effective, timely and patient-centered care. This white paper shares our experience creating a virtual care curriculum and the possible implications for medical education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.020 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".