The Challenge of Integrating Ecosystem Health throughout a Veterinary Curriculum
Bibliographic record
Abstract
This paper focuses on the question, "How can concepts of ecosystem health be made widely applicable to the diverse interests of veterinary students?" To date, most effort has focused on promoting the training of veterinarians to take an active role in the field of ecosystem health. Less attention has been placed on how ecosystem health can be made useful and valuable to the full spectrum of students, from those intending to pursue careers in ecosystem health to those seeking employment in private clinical practice. The lack of standard curricula and expectations for ecosystem health courses makes it impossible to assess how educational experiences can be combined to deliver and assess the best course. In this paper, teaching goals and teaching techniques are suggested for institutions that are seeking to weave ecosystem health throughout their curricula. Rather than dogmatically defining ecosystem health, this paper outlines potential goals and attitudes for undergraduate veterinary education that can be extracted from the conceptual foundations of ecosystem health, health promotion, and population health. The participatory nature of ecosystem health argues in favor of teaching methods that are experiential, exploitative of stories, and inclusive of a diverse group of teachers and role models.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".