Bibliographic record
Abstract
Veterinary clinical pharmacology encompasses all interactions between drugs and animals and applies basic and clinical knowledge to improve rational drug use and patient outcomes. Veterinary pharmacology instructors set educational goals and objectives that, when mastered by students, lead to improved animal health. The special needs of pharmacology instruction include establishing a functional interface between basic and clinical knowledge, managing a large quantity of information, and mastering quantitative skills essential to successful drug administration and analysis of drug action. In the present study, a survey was conducted to determine the extent to which veterinary pharmacology instructors utilize information technology (IT) in their teaching. Several IT categories were investigated, including Web-based instructional aids, stand-alone pharmacology software, interactive videoconferencing, databases, personal digital assistants (PDAs), and e-book applications. Currently IT plays a largely ancillary role in pharmacology instruction. IT use is being expanded primarily through the efforts of two veterinary professional pharmacology groups, the American College of Veterinary Clinical Pharmacology (ACVCP) and the American Academy of Veterinary Pharmacology and Therapeutics (AAVPT). The long-term outcome of improved IT use in pharmacology instruction should be to support the larger educational mission of active learning and problem solving. Creation of high-quality IT resources that promote this goal has the potential to improve veterinary pharmacology instruction within and across institutions.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".