Measuring Productivity and Impact of Veterinary Education-Related Research at the Institutional and Individual Levels Using the <i>H</i>-Index
Bibliographic record
Abstract
The natural progression of observation through inquiry to scholarship that is common to scientists is not well demonstrated among veterinary educators. One possible institutional barrier to promotion of education-related research among faculty is lack of a mechanism to demonstrate productivity and impact of scholarship of teaching and learning (SoTL) and hypothesis-driven research related to education. The h-index is one measure of research productivity. The h-index was calculated for individuals at one veterinary college and was compared between select North American schools of veterinary medicine to demonstrate baseline values for this kind of scholarship in this discipline. Use of standard search techniques using Google Scholar for citation count generated a slightly lower score than a more labor-intensive search and review of curricula vitae. The h-index across institutions ranged from 1 to 11, with a mean score of 6.0 ( SD = 2.8). Five hundred forty-four education-related articles were published in 45 different journals; the primary sites of publication were the Journal of Veterinary Medical Education ( JVME) and the Journal of the American Veterinary Medical Association.
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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 | 0.070 | 0.232 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.064 | 0.060 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".