The Educational Resource Preferences and Information-Seeking Behaviors of Veterinary Medical Students and Practitioners
Bibliographic record
Abstract
The overall purpose of this study was to assess the information-seeking strategies of individuals representing different stages of veterinary training. More specifically, we conducted a survey to evaluate textbook ownership, to determine the preferred types of educational resources and why these preferences exist, and to determine if changes arise as training progresses. We asked students in the veterinary curriculum, interns, residents, and recent graduates from the University of Georgia (UGA) College of Veterinary Medicine (CVM) to participate in a confidential online survey. A total of 184 individuals participated. Respondents were grouped into one of six categories: recent graduates ( n = 6), interns/residents ( n = 11), fourth-year students ( n = 21), third-year students ( n = 46), second-year students ( n = 73), and first-year students ( n = 27). The results showed that veterinary students used class notes and non-veterinary search engines initially, whereas interns and residents consulted textbooks and the primary literature as their first sources to answer a veterinary question. Veterinary students had accrued textbooks over sequential years in the curriculum, but many interns and residents had almost twice as many textbooks as those who had not pursued additional training after graduation. An ANOVA showed that first-year students reported a preference for printed textbooks significantly more frequently than the third-year and fourth-year students ( F(5,163) = 3.265, p = .006, and p = .012, respectively). Decreased cost was most frequently cited as the factor that would increase textbook purchases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".