Shelter Medicine Programs Support Multiple AAVMC Competency Domains: A Survey of Shelter Medicine Programming at AVMA-Accredited Colleges
Bibliographic record
Abstract
Shelter medicine is a veterinary specialty that intersects with primary care, community practice, and animal welfare. The purpose of this study was to evaluate the availability of formal (for-credit) education in shelter medicine at American Veterinary Medical Association-accredited colleges of veterinary medicine (CVMs). A 24-question survey, available from July to September 2023, was distributed to targeted faculty members at each CVM. A total of 43 of 54 colleges responded (80%). Formal education in shelter medicine was offered by 38 (88%) institutions. The median shelter medicine program starting year was 2012 (interquartile range [IQR] 2007-2017) and program duration was a median of 12 years (IQR 6-16). The median number of Association of American Veterinary Medical Colleges competency domains addressed through shelter medicine program instruction in colleges with formal shelter medicine education was 7/9 (IQR 4-8); the mode was 9/9. Responding institutions employed 118 shelter medicine faculty and instructors with a median of two shelter medicine faculty members (IQR 2-4) employed per college. In total, 30 instructors were adjuncts (25%), and only 6 of 118 (5%) were tenured. Nine of the 43 CVMs (21%) indicated that the shelter medicine program had been discontinued at some point. Lack of faculty (5/9; 56%) was the most commonly stated reason, followed by lack of a relationship with a shelter (4/9; 44%). Program instability may also be related to factors unique to shelter medicine programs, including increased faculty effort required to secure and maintain funding and community partnerships, competing demands of funders and program stakeholders, and a low proportion of tenured, boarded and permanent faculty.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".