Veterinary Students' Attitudes on One Health: Implications for Curriculum Development at Veterinary Colleges
Bibliographic record
Abstract
One Health knowledge has been identified by the North American Veterinary Medical Education Consortium (NAVMEC) as a core competency for all graduating veterinarians. Many veterinary colleges, however, are still in the preliminary stages of exploring how best to incorporate One Health principles into their existing curricula. In February 2012, we conducted a survey among second to fourth-year Professional Veterinary Medicine (PVM) students at the Colorado State University College of Veterinary Medicine and Biomedical Sciences to assess One Health needs and attitudes. Out of 407 students, 93 (22.9%) completed the survey. Although 74.2% of respondents were very or somewhat familiar with the One Health Initiative, only 34.4% reported some level of involvement with One Health-related activities. Over 80% of respondents rated the One Health Initiative as very important for public health, wildlife health, and food-animal medicine or surgery; less than 30% rated the One Health Initiative as very important for equine medicine or surgery and small-animal medicine or surgery. The majority of respondents were very interested in educational activities involving inter-disciplinary interactions with both human and ecosystem health professionals. Our findings can help guide the development and implementation of One Health-focused curricula at veterinary colleges.
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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 | 0.014 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".