Information Technology in Veterinary Pharmacology Instruction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it