Virtual care rotation for internal medicine residents during the COVID-19 pandemic
Bibliographic record
Abstract
All learning objectives were met. This rotation was particularly effective at allowing residents to hone history-taking, communication, counselling, and rapport-building skills due the nature of providing care virtually. In the absence of the ability to examine patients or use body language to support interactions, residents learned to ascertain patients’ emotions and build common-ground without the benefit of face-to-face interaction. The rotation provided increased exposure to the wide scope of ambulatory internal medicine, sparking at least one resident’s interest in ambulatory care. The rotation initially focused on COVID-19 care to allow residents to feel part of the solution to the pandemic. During the PAR reflection process, we identified that residents felt that CPAC care was algorithmic and less educational than GIM clinics. To improve the educational experience of the AVCR, we now provide opportunities in virtual subspecialty medicine clinics. For technical reasons, video visits only became a possibility partway through the rotation; and we will be incorporating more for the next iteration. Barriers to implementing virtual care, privacy, legal issues, and payment models were suggested as topics of formal discussion during the rotation; we will be incorporating these topics into a weekly journal club. Administratively, finding supervisors for residents was challenging. Faculty cited having to learn the novel technology and keep abreast with the ever-evolving COVID-19 knowledge required to supervise as deterrents. Clarifying supervisory expectations for attending physicians can be helpful; nonetheless, getting buy-in from sufficient potential supervisors was only achieved through significant persistence. Notably, the flow of resident supervision in clinical interactions was not different. Patients accepted waiting on hold for case review. Moreover, speakerphone and video telecommunication made engaging in conversations between the attending physician, residents, and patients seamless. AVCR was a success. The demand for virtual care is increasing, as is the need to educate physicians in these newer models of care.2 Incorporating novel models of care into residency should be done irrespective of a pandemic in order to build residents’ skills for the future and to integrate such ‘novel’ models of care into everyday medicine.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.043 | 0.012 |
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".