Use of Short Animal-Themed Videos to Enhance Veterinary Students' Mood, Attention, and Understanding of Pharmacology Lectures
Bibliographic record
Abstract
Professional DVM training is inherently stressful and challenging for students. This study evaluated a simple intervention-short breaks during a veterinary pharmacology lecture course in the form of funny/cute animal videos (Mood Induction Procedures, or MIP)-to assess for potential impact on students' mood, interest in material, and perceived understanding of material. Ten YouTube video clips showing cats or dogs were selected to influence students' affective states. The videos were shown in a required pharmacology class offered during the fall semester of the second year of the DVM program at a large, land-grant institution in the western US. The student cohort consisted of 133 students (20 males, 113 females). Twenty days of the course were randomly chosen for the study and ranged from weeks 2 to 13 of the semester. Sessions in which the videos were played were alternated with sessions in which no video was played, for a total of 10 video days and 10 control days. There were significant differences in all three post-class assessment measures between the experimental (video) days and the control days. Results suggest that showing short cute animal videos in the middle of class positively affected students' mood, interest in material, and self-reported understanding of material. While the results of this study are limited to one student cohort at one institution, the ease of implementation of the technique and relatively low stakes support incorporation of the MIP technique across a variety of basic and clinical science courses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".