Developing Quality Standards for Global Veterinary Education Program Assessments: Veterinary College Strategies to Meet Workforce Demands—Results of a Global Survey
Bibliographic record
Abstract
Quality veterinary education and training programs are essential for ensuring that national veterinary workforces are well prepared to address animal and veterinary public health needs. Standards for veterinary education establishments (VEEs) around the world are thought to be diverse, but little information is available on approaches to curriculum development, quality assurance methods for evaluating veterinary education programs, teaching and assessment approaches, resources for skills development, and requirements for continuing professional development (CPD) of licensed veterinarians. In this study, VEEs within Asia, Sub-Saharan Africa, the Middle East and North Africa, Oceania, Europe, Latin America, and North America were surveyed anonymously regarding education programs and curriculum development practices as well as CPD requirements using a structured questionnaire. Responses were received from 186 VEEs across 40 countries and all global regions, with 83% coming from Latin America and Asia. Similar teaching approaches were seen at VEEs across all regions; however, large animal hospitals and ambulatory field service opportunities were less common at VEEs in parts of Asia. Accreditation of the VEE program was mandatory in 66% of facilities, but only 17% of responding VEEs were accredited by an internationally recognized accrediting body. Curriculum review occurred on a periodic basis at 81% of responding VEEs, but approaches varied significantly by region. Finally, 61% of VEEs reported no CPD requirements for licensed veterinarians. The findings suggest there are global opportunities for harmonizing and enhancing VEE program quality through development of self-assessment tools as well as supporting CPD requirements to ensure national veterinary workforce preparedness.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".