Preparing Veterinarians to Address the Health Impacts of Climate Change: Student Perceptions, Knowledge Gaps, and Opportunities
Bibliographic record
Abstract
Climate change poses a significant and increasing threat to global health. While veterinarians play integral roles in public and environmental health, the profession has been less engaged in the topic of climate change relative to their human medical counterparts. The objective of this study was to synthesize veterinary students' perceptions and knowledge about the relationship between veterinary medicine and climate change to identify educational gaps and opportunities. An online questionnaire was disseminated to American Veterinary Medical Association (AVMA)-accredited veterinary colleges through the Student American Veterinary Medical Association newsletter. Respondents were overwhelmingly confident that climate change is happening, is due to human activities, and is impacting both human and animal health. Veterinary students also expressed the belief that veterinarians should take a leadership role on the issue of climate change, especially through promoting environmental sustainability in clinical practice. Despite this enthusiasm, most students reported a lack of educational opportunities within their veterinary programs. The results of this survey highlight opportunities for the development of educational resources on the topic, many of which could complement existing material such as that devoted to practice management, economics, and client communication. Veterinarians have the potential to meaningfully contribute to both mitigation and adaptation efforts around climate change; however, they must be equipped with the tools to do so.
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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 | 0.005 | 0.014 |
| 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 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".