The Role of Veterinary Medical Librarians in Teaching Information Literacy
Bibliographic record
Abstract
This qualitative study seeks to determine the nature of the instruction librarians provide to veterinary medical students at all 28 United States veterinary colleges. A secondary goal of the study was to determine in what ways and to what extent librarians participated in other instructional activities at their colleges. Over half of the librarians formally taught in one or more courses, predominantly in the first two years of the veterinary curriculum. One presentation per course was most common. Over half of the librarians interviewed stated that evidence-based veterinary medicine was taught at their colleges, and about half of these librarians collaborated with veterinary faculty in this instruction. Many librarians participated in orientation for first-year veterinary students. The librarians also taught instructional sessions for residents, interns, faculty, graduate students, and practicing veterinarians. This study found that librarians teach information literacy skills both formally and informally, but, in general, instruction by librarians was not well integrated into the curriculum. This study advances several recommendations to help veterinary students develop information literacy skills. These include: encourage veterinary faculty and administrators to collaborate more closely with librarians, incorporate a broader array of information literacy skills into assignments, and add a literature evaluation course to the curriculum.
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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 | 0.021 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".