Addressing Educational Challenges in Veterinary Medicine through the use of Distance Education
Bibliographic record
Abstract
The veterinary profession is currently facing many educational challenges, including an insufficient capacity to train and educate veterinarians for the multiple disciplines within the profession, a shortage of veterinarians in private and public practice, a shortage of faculty, a lack of human and professional diversity, and a rising cost of education resulting in extreme student debt loads. As a methodology for teaching, distance education (DE) has the potential to address many of these issues. By its very nature, DE can increase the capacity of current facilities and faculty. In addition, DE can allow students to acquire the necessary knowledge at less cost. This article describes a model for incorporating DE in the form of interactive Web-based courses, in conjunction with short, intensive residential programs, for the lecture portions of courses taught in the pre-veterinary, veterinary, and post-veterinary educational periods. In this model, the Web-based courses are used to convey the necessary core knowledge required at each step of the educational process. The residential portions are then used to apply the knowledge in such a way as to combine clinical applications with research in basic and applied sciences. Distance education can provide increased flexibility, high-quality educational experiences, and a less costly alternative for students while maximizing the reach of current faculty efforts and the capacity of existing physical structures.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".