Part I: Twenty-Year Literature Overview of Veterinary and Allopathic Medicine
Bibliographic record
Abstract
Over the last 20 years, numerous reports, symposia, and workshops have focused on the challenges and changes facing veterinary and allopathic medicine. Many of these have specifically considered the changing economic and demographic profiles of the health professions, the specific roles of health professionals in society, and the importance of professional curricula in meeting changing professional and societal needs. Changing curricula to address future demands is a common thread that runs through all of these reports. Future demands most consistently noted include the fact that modern veterinary curricula must emphasize the acquisition of skills, values, and attitudes in addition to the acquisition of knowledge. Skills relating to business management, strong interpersonal communication, and problem solving have often been noted as lacking in current curricula. Furthermore, future curricula must allow for greater diversification and "specialization" among veterinary students; should promote greater opportunities for an emphasis on public health and population medicine, including food safety, food security, and bio- and agro-terrorism; and should motivate students to be active learners who possess strong lifelong learning skills and attitudes.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".