Design and Evaluation of the Veterinary Epidemiology Teaching Skills (VETS) Workshop: Building Capacity in the Asia-Pacific Region
Bibliographic record
Abstract
Building workforce capacity in epidemiology skills for veterinarians in the Asia-Pacific region is crucial to health security. However, successful implementation of these programs requires a supply of trained veterinary epidemiology teachers and mentors. We sought to design and evaluate delivery of a 4-day Veterinary Epidemiology Teaching Skills (VETS) workshop as part of a larger project to strengthen field veterinary epidemiology capacity. Thirty-five veterinarians were selected to participate in the 4-day VETS workshop, consisting of nine modules delivered synchronously online. Participants were formatively assessed and given feedback from peers and facilitators on all activities. Data were collected with pre- and post-course questionnaires. Numeric values were categorized to convert into an ordinal scale with four categories. Qualitative data were analyzed using thematic analysis. Thirty-four veterinary epidemiologists from eight countries of the Asia-Pacific completed the workshop. Participants felt able to achieve most key learning outcomes through provision of succinct literature, teaching frameworks, and active participation in small groups, with multiple opportunities to give and receive feedback. Although the online workshop provided flexibility, participants felt the addition of face-to-face sessions would enrich their experience. Additionally, protected time from work duties would have improved their ability to fully engage in the workshop. The VETS workshop granted an effective online framework for veterinary epidemiologists to develop and practice skills in teaching, facilitation, assessment, feedback, case-based learning, program evaluation, and mentorship. A challenge will be ensuring provision of local teaching and mentoring opportunities to reinforce learning outcomes and build workforce capacity.
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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 | 0.086 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".