A Mixed-Methods Analysis of Changing Student Confidence in an Online Shelter Medicine Course
Bibliographic record
Abstract
Maddie's Shelter Medicine Program at the University of Florida College of Veterinary Medicine offers comprehensive training in shelter medicine to veterinary students based on a set of core job skills identified by the Association of Shelter Veterinarians. In 2012, this program began teaching online distance education courses to students and practicing veterinarians worldwide who sought additional training in this newly recognized specialty area. Distance learning is a novel educational strategy in veterinary medicine; most instruction at veterinary medical schools is classroom based. No previous studies have shown whether online courses can prepare veterinarians to practice shelter medicine. In this study, we investigated how an online, graduate-level course titled "Shelter Animal Physical Health" changed student self-reported confidence. First, we compared pre-course confidence regarding eight specific shelter medical practice scenarios to post-course confidence through statistical analysis. Quantitative analysis showed a significant (p<.001) increase in self-reported confidence for all eight scenarios. Next, we used open coding to identify themes within reflection papers that students were asked to write during the course and used those findings to corroborate or refute the quantitative results. Qualitative analysis of students' reflection papers identified six themes: confidence, communication, population management, outbreak management, medical care, and application. The results of this study show that distance education can be an effective method of preparing veterinarians and veterinary students to practice shelter medicine.
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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 | 0.025 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".