Shelter Medicine Sustainability from an Academic Perspective: Challenges and Issues
Bibliographic record
Abstract
A meeting of veterinary school faculty and partners, many associated with shelter medicine and/or community medicine programming, was convened at the 2019 Shelter Medicine Veterinary Educators Conference in Pullman, WA, to discuss challenges with shelter medicine program sustainability and defining the future. The discussion was facilitated by an outside consultant and is summarized in this manuscript. The goal of the meeting was to identify challenges and issues concerning the needs and goals for shelter medicine curricula to have long-term success in academic training. Four themes were identified in the transcripts including external pressure from leadership and other stakeholders, funder expectations, time horizons, and perceptions of shelters and shelter veterinarians. Addressing these challenges will be critical to ensuring stability in academic training in shelter medicine, a critical tool for both learning outcomes for general graduates and specific for veterinarians pursuing shelter medicine as a career.
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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 | 0.031 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.010 |
| Scholarly communication | 0.024 | 0.011 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 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".