Education Research: Qualitative Assessment of Virtual Teaching of the Neurological Examination to Students Reveals Importance of Technique, Process, and Documentation
Bibliographic record
Abstract
Introduction: Virtual teaching sessions during the coronavirus disease 2019 pandemic were challenging for students and teachers but were also an opportunity to find creative ways to teach physical examination skills, including the neurologic examination. We examined expert opinions of the pros and cons that arise using a virtual platform to teach the neurologic examination and strategies to best address these challenges. Methods: This was a qualitative study incorporating a focus group of faculty and resident neurologists. Data were coded using conventional content analysis. An interpretivist, social constructionist approach was used to look for interesting or novel ideas, rather than testing a specific hypothesis. Three independent auditors performed a dependability and confirmability audit to confirm that the themes accurately reflected the data. Results: A single focus group was used. Four of the 6 participants were faculty neurologists and 2 were neurology residents. Five themes were identified: (1) learning the neurologic examination is complex, (2) lack of physical contact is the most important drawback of virtual teaching, (3) virtual teaching can effectively emphasize the organization of the examination, (4) virtual sessions can facilitate combined teaching of technique and demonstration of abnormalities, and (5) virtual platforms do not necessarily imply reduced participation. Conclusion: Teaching the neurologic examination is a multifaceted process that should emphasize not only technique but also an overall approach to performing and documenting the examination. Many aspects of the neurologic examination can be appropriately taught virtually using various strategies, although there may always be some limitations. Virtual education can play a useful role for future curriculum design and global education.
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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 | 0.047 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".