Virtual Care and Virtual Medical Education: A Canadian Medical Student Perspective
Bibliographic record
Abstract
Background: The COVID-19 pandemic had accelerated the adoption of virtual care as an extension of routine clinical practice. In addition, pre-clinical undergraduate medical education was affected by the transition to both synchronous and asynchronous online learning. The objective of this study was to assess the current experience and knowledge of medical students with regards to virtual care. A secondary objective was to identify opportunities for improvement in the undergraduate medical curriculum. Methods: An electronic survey was distributed to undergraduate medical students in Canadian medical schools. Main sections of the survey addressed experience with virtual care encounters and perceived impact of virtual learning on medical education. Result: Out of our 53 respondents, the majority (80%) of medical students perceived high educational importance of virtual care encounters. 91% of the students recognized the developing role of virtual care in current and future medical practices. 55% of the surveyed showed readiness to conduct virtual care in the current curriculum. 94% of the responses stated the preferred feedback method for clinical learning was immediate faculty assessment following the encounter. Discussion: The results from this study provided insight on the medical learner’s experience while navigating virtual care and identified areas of improvements at an institutional level. Effective medical training that integrates the advantages of virtual care is crucial.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".