Public-Health Education at Kansas State University
Bibliographic record
Abstract
What are veterinary medical and public-health professionals doing to remedy the immediate and impending shortages of veterinarians in population health and public practice? This question was addressed at the joint symposium of the Association of American Veterinary Medical Colleges and the Association of Schools of Public Health, held in April 2007. Thinking locally, faculty and students at Kansas State University (KSU) asked similar questions after attending the symposium: What are we doing within the College of Veterinary Medicine to tackle this problem? What can we do better with new collaborators? Both the professional veterinary curriculum and the Master of Public Health (MPH) at KSU provide exceptional opportunities to address these questions. Students are exposed to public health as a possible career choice early in veterinary school, and this exposure is repeated several times in different venues throughout their professional education. Students also have opportunities to pursue interests in population medicine and public health through certificate programs, summer research programs, study abroad, and collaborations with contributing organizations unique to KSU, such as its Food Science Institute, National Agricultural Biosecurity Center, and Biosecurity Research Institute. Moreover, students may take advantage of the interdisciplinary nature of public-health education at KSU, where collaborations with several different colleges and departments within the university have been established. We are pleased to be able to offer these opportunities to our students and hope that our experience may be instructive for the development of similar programs at other institutions, to the eventual benefit of the profession at large.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.094 | 0.014 |
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".