A Neurology Clerkship Curriculum Using Video-Based Lectures and Just-in-Time Teaching (JiTT)
Bibliographic record
Abstract
Introduction: Just-in-time teaching is an educational strategy that involves tailoring in-session learning activities based on student performance in presession assessments. We implemented this strategy in a third-year neurology clerkship. Methods: Linked to core neurology clerkship lectures, eight brief video-based lectures and knowledge assessments were developed. Students watched videos and completed multiple-choice questions, and results were provided to faculty, who were given the opportunity to adjust the in-person lecture accordingly. Feedback was obtained by surveys of students and faculty lecturers and from student focus groups and faculty. Student performance on the end-of-clerkship examination was analyzed. Results: Between October 2016 and April 2017, 135 students participated in the curriculum, and 56 students (41.5%) responded to the surveys. Most students agreed or strongly agreed that the new curriculum enhanced their learning and promoted their sense of responsibility in learning the content. Faculty agreed that this pedagogy helped prepare students for class. Most students watched the entire video-based lecture, although there was a trend toward decreased audience retention with longer lectures. There were no significant changes in performance on the end-of-clerkship examination after implementation of just-in-time teaching. In focus groups, students emphasized the importance of tying just-in-time teaching activities to the lecture and providing video-based lectures well in advance of the lectures. Discussion: Just-in-time teaching using video-based lectures is an acceptable and feasible method to augment learning during a neurology clinical clerkship. We believe this method could be used in other neurology clerkships with similar success.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".