Good for patients but not learners? Exploring faculty and learner virtual care integration
Bibliographic record
Abstract
BACKGROUND: The pandemic catapulted the adoption of virtual care far ahead of its anticipated maturation date, forcing faculty to role model and teach learners with barely enough time to master it themselves. With a scant body of prepandemic literature now accompanied by experience gained under extraordinary circumstances, we can benefit from understanding ad hoc strategies implemented by those on the front lines and from listening to learners about what is working and what is not. The purpose of this study was to explore the experience of learner integration into virtual care from both the faculty and learner perspectives. METHODS: Using a constructivist grounded theory methodology and sociomateriality as a sensitising concept, we recruited participants using purposeful and theoretical sampling from a Canadian University with limited prepandemic virtual care provision. We interviewed 16 faculty and 5 learners spanning a breadth of specialties and years of practice/education to probe their experience of teaching and learning virtual care. Data collection and analysis were conducted iteratively with themes identified through constant comparative analysis. RESULTS: Integrating learners into virtual care proved challenging initially because of a lack of familiarity with the process and later because of disrupted workflow, triggered by the structure and logistics of the virtual care clinic. Both faculty and learners identified learning deficiencies in the virtual care experience when compared with in-person clinics, but several unique and valuable learning affordances were noted. All faculty expressed a desire to keep virtual care as part of their future clinic practice, but paradoxically most felt that they were unlikely to include learners. CONCLUSIONS: Training learners in virtual care is an educational challenge that will not disappear with COVID-19, even if our participants wished it could. The perceived value for patients but not learners begs a reconsideration of the sociomaterial contribution to this pandemic paradox.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.018 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".