A Snapshot of Veterinary Education Establishments and Veterinary Statutory Bodies in Asia and the Pacific Region: Issues Identified and OIE Activities to Address Them
Bibliographic record
Abstract
Veterinary education establishments (VEEs) and veterinary statutory bodies (VSBs) play key roles in ensuring the effectiveness of veterinary professionals and delivery of competent national veterinary services (VS). Recognizing the need to address the quality of veterinary education and the role of VSBs for its member countries/territories (Members), the World Organisation for Animal Health (OIE) has organized conferences, workshops, and ad hoc groups leading to the development of recommendations and guidelines and the introduction of active programmers on veterinary education. In Asia and the Pacific region, veterinary education and practice as well as regulatory approach among Members vary considerably, and limited information is currently available publicly. In 2018, the OIE organized a workshop for VEEs and VSBs in Asia and the Pacific region, for which participants completed a questionnaire regarding each country’s situation relating to veterinary education, regulations, and professionals. The questionnaire results showed that most Members and Observers (Members/Observers) in Asia had at least one VEE and that the OIE guidelines for VEEs are widely used. Similarly, most Members/Observers in Asia had a VSB or equivalent authority that oversees the quality and competence of veterinarians. Some challenges were also revealed, including variations in the roles, responsibilities, and level of autonomy of VSBs and weak collaboration/coordination among the key in-country/territory stakeholders of veterinary education. The OIE offers Members recommendations and guidelines as well as several programs and activities aiming to strengthen the VS, VEEs, and VSBs, including the evaluation of veterinary services’ performance, the VEE and VSB twinning projects, and conferences and workshops.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".