P.083 Effective video technology for teaching the neurological exam
Bibliographic record
Abstract
Background: With advancements in technology, the use of video as a pedagogical method in medical education has gained in popularity, and may aid in teaching clinical skills. In the UBC MD program, videos have been used to assist in teaching the -neurological exam for several decades, but the currently available videos are outdated and not of contemporary quality. Methods: Drawing upon the cognitive theory of multimedia learning from Mayer and Moreno (2003) which describes methods to maximize learning by minimizing cognitive load, we developed a tool to systematically assess pedagogical videos. We inventoried twelve existing neurology videos and analyzed their use of methods such as weeding (removing extraneous information), signalling (visually highlighting important information), and chunking (grouping similar information together). Results: Generally, older videos had poor audiovisual quality that introduced extraneous load, while more current videos had higher production value, albeit inconsistent with the depth of their content. We therefore produced a new three-part neurological exam video series. We wrote storyboards, filmed with a focus on visually depicting the exam and findings, and edited to elucidate relevant physiological concepts. Conclusions: The end product has been adopted by the UBC MD program, and can be shared with other programs who may wish to adopt them.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.032 | 0.003 |
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".