An Innovative Approach to Post-graduate Education in Veterinary Public Health
Bibliographic record
Abstract
The past decade has seen a substantially increased need for animal health professionals who have advanced education in areas that impact on veterinary public health (VPH). The University of Sydney has made a significant contribution to the international capacity for training in this field by developing an online, distance program in Veterinary Public Health Management. This paper describes the distinctive characteristics of this program, which combines technical material in a range of units that influence VPH with leadership and project management. It then describes the educational model developed for delivery of its course material, including the four modalities that are structured to support engaged learning by busy animal health professionals who are working full-time (self-led, facilitator-led, peer-led, and assessment-led instructional approaches). Finally, having reflected on the efficacy of this model for post-graduate training in VPH, we discuss the progress of the program since its inception in 2002, reflecting on the challenges it has encountered and defining the factors that are critical to the success of this program.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".