Telehealth clinical learning: understanding pre-clerkship medical student experiences
Bibliographic record
Abstract
Background: With the onset of the COVID-19 pandemic, a reliance upon telehealth patient visits emerged. Many medical schools use early clinical experiences in the pre-clerkship years to provide opportunities to practice evolving clinical skills and broaden classroom learning. However, little is known about the value of telehealth visits during the pre-clerkship years. Therefore, the purpose of the current study was to determine what student learning experiences were with telehealth patient encounters during early clinical experiences. Methods: In this qualitative study, we used a descriptive phenomenological approach. We interviewed medical students using Zoom to gather their lived experiences. We grouped key findings into themes. Results: Seventeen medical students participated in the study. Key challenges included the loss of body language and visual cues leading to challenges with rapport building, the inability to perform physical examinations, and less involvement and independent practice of skills. However, positive aspects include good opportunities for history taking and benefits to note-taking. Mentorship with preceptors remained either positive or similar to in-person experiences. Conclusion: Since telehealth remains an important part of healthcare, it is crucial to train learners in telehealth clinical environments alongside standard in-person environments. However, while both challenges and benefits exist with telehealth clinical visits for junior learners, active learning processes, the use of video augmentation and robust faculty development strategies remain important to increase the educational value of these visits.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".