Information-Seeking Behaviors of First-Semester Veterinary Students: A Preliminary Report
Bibliographic record
Abstract
Although emphasis in veterinary education is increasingly being placed on the ability to find, use, and communicate information, studies on the information behaviors of veterinary students or professionals are few. Improved knowledge in this area will provide valuable information for course and curriculum planning and the design of information resources. This article describes a survey of the information-seeking behaviors of first-semester veterinary students at Purdue University. A survey was administered as the first phase of a progressive semester-long assignment for a first semester DVM course in systemic mammalian physiology. The survey probed for understanding of the scientific literature and its use for course assignments and continuing learning. The survey results showed that students beginning the program tended to use Google for coursework, although some also used the resources found through the Purdue libraries' Web sites. On entering veterinary school, they became aware of specific information resources in veterinary medicine. They used a small number of accepted criteria to evaluate the Web site quality. This study confirms the findings of studies of information-seeking behaviors of undergraduate students. Further studies are needed to examine whether those behaviors change as students learn about specialized veterinary resources that are designed to address clinical needs as they progress through their training.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".