Effectiveness of Supplementary Materials in Teaching the Veterinary Neurologic Examination
Bibliographic record
Abstract
Clinical neurology can be difficult for veterinary students to comprehend, and part of understanding the clinical aspect is performing a proper neurologic examination. In this study, first-year veterinary students in a Small Animal Physical Exam and Anatomy rotation were given supplemental learning activities to determine their effect on student procedural knowledge and motivation in performing a neurologic examination. Students were randomly assigned to one of three groups: the first watched a video of a clinician performing the neurologic examination, the second read a handout about the neurologic exam, and the third was the control group, where students were not provided any supplemental activities. At the start and end of the rotation, students participated in a survey assessing their overall procedural knowledge and motivation to learn about the neurologic exam. No notable improvement occurred in overall student knowledge from the beginning to end of the rotation, nor when using supplemental material ( p > .05). However, there was a significant difference in quiz scores between the three condition groups ( p < .01), suggesting the type of learning activity did influence student learning. Additionally, students in the video and reading groups showed a significant increase in motivational scores compared with those in the control group ( p < .05), demonstrating supplemental learning activities do improve student motivation in learning about the neurologic examination. This study provides evidence that while supplemental materials may not immediately help veterinary students learn to perform the neurologic examination, they do have a positive impact on students’ learning motivation.
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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 | 0.009 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".