Teaching Evidence-Based Veterinary Medicine in the US and Canada
Bibliographic record
Abstract
There is no comprehensive review of the extent to which evidence-based veterinary medicine (EBVM) is taught in AVMA-accredited colleges of veterinary medicine in the US and Canada. We surveyed teaching faculty and librarians at these institutions to determine what EBVM skills are currently included in curricula, how they are taught, and to what extent librarians are involved in this process. Librarians appear to be an underused resource, as 59% of respondents did not use librarians/library resources in teaching EBVM. We discovered that there is no standard teaching methodology nor are there common learning activities for EBVM among our survey respondents, who represent 22 institutions. Respondents reported major barriers to inclusion such as a perceived shortage of time in an already-crowded course of study and a lack of high-quality evidence and point-of-care tools. Suggestions for overcoming these barriers include collaborating with librarians and using new EBVM online teaching resources.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".