Environmental sustainability in veterinary clinics: best practices for the United States and Canada
Bibliographic record
Abstract
Introduction: Veterinary professionals in the United States and Canada are increasingly seeking ways to reduce the environmental impacts of clinical practice, reflecting a broader commitment within the profession to sustainability. While environmental sustainability frameworks are well established in human healthcare, equivalent resources for veterinary clinical practice in North America remain limited. This study aimed to develop evidence-based best practices for enhancing environmental sustainability in veterinary clinics in the United States and Canada. Methods: We conducted a gray literature review of open-access resources in veterinary medicine and human healthcare to identify explicit sustainability actions. Extracted actions were synthesized and reviewed by a panel of seven subject matter experts through a two-round modified Delphi process. Experts evaluated each action for implementation effort and environmental impact and provided qualitative feedback. Results: The final set comprised 199 actions, organized into 14 thematic categories. Experts emphasized the importance of leadership engagement, team empowerment, and balancing high-impact, resource-intensive interventions with low-effort "quick wins" to build momentum. Priority areas included energy efficiency, waste reduction (particularly anesthetic gas management), sustainable procurement, and community engagement. Discussion: The resulting framework provides a flexible, regionally relevant roadmap that clinics can adapt to their context, offering practical entry points for immediate action alongside strategies for long-term change. This resource can support veterinary teams, educators, and industry stakeholders in embedding sustainability into clinical practice, contributing to improved planetary and animal health.
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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 | 0.033 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".