Undergraduate Veterinary Students' Perceptions of the Usefulness, Focus, and Application of Epidemiology Before and After Epidemiology Courses
Bibliographic record
Abstract
Student perception of the relevance of a topic is known to influence learning outcomes. To determine student perceptions of the usefulness, focus, and application of epidemiology, we conducted a study at two Australian veterinary schools in 2005. Veterinary students in year 3 at the University of Sydney and in year 5 at the University of Queensland completed a self-administered questionnaire at the commencement and conclusion of an epidemiology course. At both universities, while over 95% of students considered epidemiology to be "essential" or "quite useful" in cattle and sheep practice and in government practice, at the course end between 10% and 30% of students still did not consider epidemiology to be "essential" or "quite useful" in small-animal or equine practice. However, the percentage of students who considered that all veterinary work involved the application of epidemiological principles increased from 7% at course start to 15% at course end at the University of Sydney (p=0.188), and from 3% to 25% at the University of Queensland (p<0.001). The results indicate that, although some veterinary students, even at completion of an epidemiology course, still do not link epidemiology with an evidence-based medicine approach to patient care, undergraduate courses can positively change student perception of the usefulness, focus, and application of epidemiology. These findings will be used to refine the epidemiology courses analyzed to improve the learning outcomes of our students.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".