Knowledge Base and Effectiveness of Online Continuing Education about Foreign Animal Diseases for Equine Veterinarians
Bibliographic record
Abstract
The purpose of this study was to assess the effectiveness of two different methods of online education using the knowledge base of African horse sickness (AHS) among US equine veterinarians as a model. An e-mail was sent to US veterinary members of the American Association of Equine Practitioners (AAEP), inviting them to participate in a complementary online educational opportunity. We determined participants' baseline knowledge of AHS by their responses in an AHS case scenario. Participants were then randomly assigned to either a Webinar module or a text-formatted module, followed by an educational assessment quiz. Educational effectiveness was measured by considering the difference between the educational assessment quiz score and the baseline knowledge score. Of the 5,394 members from the AAEP list, 309 veterinarians agreed to participate, but only 211 completed the entire study. The median baseline knowledge score from the case scenario was 20 out of a perfect score of 100 points. The median assessment quiz score after the participants had access to the AHS educational material was 90, which was significantly higher than the baseline knowledge score (p=.01). Educational effectiveness in the module formats showed no significant difference (p=.81). Results from this study suggest that online education modules, once accessed, may improve participants' knowledge of veterinary diseases.
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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 | 0.006 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".